id
stringlengths 9
10
| submitter
stringlengths 5
47
⌀ | authors
stringlengths 5
1.72k
| title
stringlengths 11
234
| comments
stringlengths 1
491
⌀ | journal-ref
stringlengths 4
396
⌀ | doi
stringlengths 13
97
⌀ | report-no
stringlengths 4
138
⌀ | categories
stringclasses 1
value | license
stringclasses 9
values | abstract
stringlengths 29
3.66k
| versions
listlengths 1
21
| update_date
int64 1,180B
1,718B
| authors_parsed
listlengths 1
98
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2308.15833 | Zhen Zhang | Zhen Zhang and Hongrui Sun and Hui Sun | Depth analysis of battery performance based on a data-driven approach | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Capacity attenuation is one of the most intractable issues in the current of
application of the cells. The disintegration mechanism is well known to be very
complex across the system. It is a great challenge to fully comprehend this
process and predict the process accurately. Thus, the machine learning (ML)
technology is employed to predict the specific capacity change of the cell
throughout the cycle and grasp this intricate procedure. Different from the
previous work, according to the WOA-ELM model proposed in this work (R2 =
0.9999871), the key factors affecting the specific capacity of the battery are
determined, and the defects in the machine learning black box are overcome by
the interpretable model. Their connection with the structural damage of
electrode materials and battery failure during battery cycling is
comprehensively explained, revealing their essentiality to battery performance,
which is conducive to superior research on contemporary batteries and
modification.
| [
{
"version": "v1",
"created": "Wed, 30 Aug 2023 08:15:27 GMT"
}
] | 1,693,440,000,000 | [
[
"Zhang",
"Zhen",
""
],
[
"Sun",
"Hongrui",
""
],
[
"Sun",
"Hui",
""
]
] |
2308.15863 | EPTCS | Richard Comploi-Taupe (Siemens AG \"Osterreich, Vienna, Austria) | Inductive Learning of Declarative Domain-Specific Heuristics for ASP | In Proceedings ICLP 2023, arXiv:2308.14898 | EPTCS 385, 2023, pp. 129-140 | 10.4204/EPTCS.385.14 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Domain-specific heuristics are a crucial technique for the efficient solving
of problems that are large or computationally hard. Answer Set Programming
(ASP) systems support declarative specifications of domain-specific heuristics
to improve solving performance. However, such heuristics must be invented
manually so far. Inventing domain-specific heuristics for answer-set programs
requires expertise with the domain under consideration and familiarity with ASP
syntax, semantics, and solving technology. The process of inventing useful
heuristics would highly profit from automatic support. This paper presents a
novel approach to the automatic learning of such heuristics. We use Inductive
Logic Programming (ILP) to learn declarative domain-specific heuristics from
examples stemming from (near-)optimal answer sets of small but representative
problem instances. Our experimental results indicate that the learned
heuristics can improve solving performance and solution quality when solving
larger, harder instances of the same problem.
| [
{
"version": "v1",
"created": "Wed, 30 Aug 2023 08:55:17 GMT"
}
] | 1,693,440,000,000 | [
[
"Comploi-Taupe",
"Richard",
"",
"Siemens AG Österreich, Vienna, Austria"
]
] |
2308.15879 | EPTCS | Mario Alviano (University of Calabria), Ly Ly Trieu (New Mexico State
Universty), Tran Cao Son (New Mexico State Universty), Marcello Balduccini
(Saint Joseph's University) | Explanations for Answer Set Programming | In Proceedings ICLP 2023, arXiv:2308.14898 | EPTCS 385, 2023, pp. 27-40 | 10.4204/EPTCS.385.4 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The paper presents an enhancement of xASP, a system that generates
explanation graphs for Answer Set Programming (ASP). Different from xASP, the
new system, xASP2, supports different clingo constructs like the choice rules,
the constraints, and the aggregates such as #sum, #min. This work formalizes
and presents an explainable artificial intelligence system for a broad fragment
of ASP, capable of shrinking as much as possible the set of assumptions and
presenting explanations in terms of directed acyclic graphs.
| [
{
"version": "v1",
"created": "Wed, 30 Aug 2023 09:03:07 GMT"
}
] | 1,693,440,000,000 | [
[
"Alviano",
"Mario",
"",
"University of Calabria"
],
[
"Trieu",
"Ly Ly",
"",
"New Mexico State\n Universty"
],
[
"Son",
"Tran Cao",
"",
"New Mexico State Universty"
],
[
"Balduccini",
"Marcello",
"",
"Saint Joseph's University"
]
] |
2308.15891 | EPTCS | Francesca Toni (Department of Computing, Imperial College London, UK),
Nico Potyka (Department of Computing, Imperial College London, UK), Markus
Ulbricht (Department of Computer Science, Leipzig University, Germany),
Pietro Totis (Department of Computer Science, KU Leuven, Belgium) | Understanding ProbLog as Probabilistic Argumentation | In Proceedings ICLP 2023, arXiv:2308.14898 | EPTCS 385, 2023, pp. 183-189 | 10.4204/EPTCS.385.18 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | ProbLog is a popular probabilistic logic programming language/tool, widely
used for applications requiring to deal with inherent uncertainties in
structured domains. In this paper we study connections between ProbLog and a
variant of another well-known formalism combining symbolic reasoning and
reasoning under uncertainty, i.e. probabilistic argumentation. Specifically, we
show that ProbLog is an instance of a form of Probabilistic Abstract
Argumentation (PAA) that builds upon Assumption-Based Argumentation (ABA). The
connections pave the way towards equipping ProbLog with alternative semantics,
inherited from PAA/PABA, as well as obtaining novel argumentation semantics for
PAA/PABA, leveraging on prior connections between ProbLog and argumentation.
Further, the connections pave the way towards novel forms of argumentative
explanations for ProbLog's outputs.
| [
{
"version": "v1",
"created": "Wed, 30 Aug 2023 09:05:32 GMT"
}
] | 1,693,440,000,000 | [
[
"Toni",
"Francesca",
"",
"Department of Computing, Imperial College London, UK"
],
[
"Potyka",
"Nico",
"",
"Department of Computing, Imperial College London, UK"
],
[
"Ulbricht",
"Markus",
"",
"Department of Computer Science, Leipzig University, Germany"
],
[
"Totis",
"Pietro",
"",
"Department of Computer Science, KU Leuven, Belgium"
]
] |
2308.15898 | EPTCS | Alessandro Dal Pal\`u (Universit\`a di Parma, Italy), Agostino Dovier
(Universit\`a di Udine, Italy), Andrea Formisano (Universit\`a di Udine,
Italy) | An xAI Approach for Data-to-Text Processing with ASP | In Proceedings ICLP 2023, arXiv:2308.14898 | EPTCS 385, 2023, pp. 353-366 | 10.4204/EPTCS.385.38 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The generation of natural language text from data series gained renewed
interest among AI research goals. Not surprisingly, the few proposals in the
state of the art are based on training some system, in order to produce a text
that describes and that is coherent to the data provided as input. Main
challenges of such approaches are the proper identification of "what" to say
(the key descriptive elements to be addressed in the data) and "how" to say:
the correspondence and accuracy between data and text, the presence of
contradictions/redundancy in the text, the control of the amount of synthesis.
This paper presents a framework that is compliant with xAI requirements. In
particular we model ASP/Python programs that enable an explicit control of
accuracy errors and amount of synthesis, with proven optimal solutions. The
text description is hierarchically organized, in a top-down structure where
text is enriched with further details, according to logic rules. The generation
of natural language descriptions' structure is also managed by logic rules.
| [
{
"version": "v1",
"created": "Wed, 30 Aug 2023 09:09:09 GMT"
}
] | 1,693,440,000,000 | [
[
"Palù",
"Alessandro Dal",
"",
"Università di Parma, Italy"
],
[
"Dovier",
"Agostino",
"",
"Università di Udine, Italy"
],
[
"Formisano",
"Andrea",
"",
"Università di Udine,\n Italy"
]
] |
2308.15926 | Jianghong Ma | Dezhao Yang, Jianghong Ma, Shanshan Feng, Haijun Zhang, Zhao Zhang | IDVT: Interest-aware Denoising and View-guided Tuning for Social
Recommendation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the information age, recommendation systems are vital for efficiently
filtering information and identifying user preferences. Online social platforms
have enriched these systems by providing valuable auxiliary information.
Socially connected users are assumed to share similar preferences, enhancing
recommendation accuracy and addressing cold start issues. However, empirical
findings challenge the assumption, revealing that certain social connections
can actually harm system performance. Our statistical analysis indicates a
significant amount of noise in the social network, where many socially
connected users do not share common interests. To address this issue, we
propose an innovative \underline{I}nterest-aware \underline{D}enoising and
\underline{V}iew-guided \underline{T}uning (IDVT) method for the social
recommendation. The first ID part effectively denoises social connections.
Specifically, the denoising process considers both social network structure and
user interaction interests in a global view. Moreover, in this global view, we
also integrate denoised social information (social domain) into the propagation
of the user-item interactions (collaborative domain) and aggregate user
representations from two domains using a gating mechanism. To tackle potential
user interest loss and enhance model robustness within the global view, our
second VT part introduces two additional views (local view and dropout-enhanced
view) for fine-tuning user representations in the global view through
contrastive learning. Extensive evaluations on real-world datasets with varying
noise ratios demonstrate the superiority of IDVT over state-of-the-art social
recommendation methods.
| [
{
"version": "v1",
"created": "Wed, 30 Aug 2023 10:03:55 GMT"
}
] | 1,693,440,000,000 | [
[
"Yang",
"Dezhao",
""
],
[
"Ma",
"Jianghong",
""
],
[
"Feng",
"Shanshan",
""
],
[
"Zhang",
"Haijun",
""
],
[
"Zhang",
"Zhao",
""
]
] |
2308.15969 | Jasmina Gajcin | Jasmina Gajcin, James McCarthy, Rahul Nair, Radu Marinescu, Elizabeth
Daly, Ivana Dusparic | Iterative Reward Shaping using Human Feedback for Correcting Reward
Misspecification | 7 pages, 2 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | A well-defined reward function is crucial for successful training of an
reinforcement learning (RL) agent. However, defining a suitable reward function
is a notoriously challenging task, especially in complex, multi-objective
environments. Developers often have to resort to starting with an initial,
potentially misspecified reward function, and iteratively adjusting its
parameters, based on observed learned behavior. In this work, we aim to
automate this process by proposing ITERS, an iterative reward shaping approach
using human feedback for mitigating the effects of a misspecified reward
function. Our approach allows the user to provide trajectory-level feedback on
agent's behavior during training, which can be integrated as a reward shaping
signal in the following training iteration. We also allow the user to provide
explanations of their feedback, which are used to augment the feedback and
reduce user effort and feedback frequency. We evaluate ITERS in three
environments and show that it can successfully correct misspecified reward
functions.
| [
{
"version": "v1",
"created": "Wed, 30 Aug 2023 11:45:40 GMT"
}
] | 1,693,440,000,000 | [
[
"Gajcin",
"Jasmina",
""
],
[
"McCarthy",
"James",
""
],
[
"Nair",
"Rahul",
""
],
[
"Marinescu",
"Radu",
""
],
[
"Daly",
"Elizabeth",
""
],
[
"Dusparic",
"Ivana",
""
]
] |
2308.15985 | Jianwu Fang | Jianwu Fang, iahuan Qiao, Jianru Xue, and Zhengguo Li | Vision-Based Traffic Accident Detection and Anticipation: A Survey | accepted in IEEE Transactions on Circuits and Systems for Video
Technology; 16 pages, 155 references | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traffic accident detection and anticipation is an obstinate road safety
problem and painstaking efforts have been devoted. With the rapid growth of
video data, Vision-based Traffic Accident Detection and Anticipation (named
Vision-TAD and Vision-TAA) become the last one-mile problem for safe driving
and surveillance safety. However, the long-tailed, unbalanced, highly dynamic,
complex, and uncertain properties of traffic accidents form the
Out-of-Distribution (OOD) feature for Vision-TAD and Vision-TAA. Current AI
development may focus on these OOD but important problems. What has been done
for Vision-TAD and Vision-TAA? What direction we should focus on in the future
for this problem? A comprehensive survey is important. We present the first
survey on Vision-TAD in the deep learning era and the first-ever survey for
Vision-TAA. The pros and cons of each research prototype are discussed in
detail during the investigation. In addition, we also provide a critical review
of 31 publicly available benchmarks and related evaluation metrics. Through
this survey, we want to spawn new insights and open possible trends for
Vision-TAD and Vision-TAA tasks.
| [
{
"version": "v1",
"created": "Wed, 30 Aug 2023 12:13:41 GMT"
}
] | 1,693,440,000,000 | [
[
"Fang",
"Jianwu",
""
],
[
"Qiao",
"iahuan",
""
],
[
"Xue",
"Jianru",
""
],
[
"Li",
"Zhengguo",
""
]
] |
2308.16262 | Kiet Vo | Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet | Causal Strategic Learning with Competitive Selection | Added more discussions on assumptions and the algorithm, and expand
the Conclusion | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the problem of agent selection in causal strategic learning under
multiple decision makers and address two key challenges that come with it.
Firstly, while much of prior work focuses on studying a fixed pool of agents
that remains static regardless of their evaluations, we consider the impact of
selection procedure by which agents are not only evaluated, but also selected.
When each decision maker unilaterally selects agents by maximising their own
utility, we show that the optimal selection rule is a trade-off between
selecting the best agents and providing incentives to maximise the agents'
improvement. Furthermore, this optimal selection rule relies on incorrect
predictions of agents' outcomes. Hence, we study the conditions under which a
decision maker's optimal selection rule will not lead to deterioration of
agents' outcome nor cause unjust reduction in agents' selection chance. To that
end, we provide an analytical form of the optimal selection rule and a
mechanism to retrieve the causal parameters from observational data, under
certain assumptions on agents' behaviour. Secondly, when there are multiple
decision makers, the interference between selection rules introduces another
source of biases in estimating the underlying causal parameters. To address
this problem, we provide a cooperative protocol which all decision makers must
collectively adopt to recover the true causal parameters. Lastly, we complement
our theoretical results with simulation studies. Our results highlight not only
the importance of causal modeling as a strategy to mitigate the effect of
gaming, as suggested by previous work, but also the need of a benevolent
regulator to enable it.
| [
{
"version": "v1",
"created": "Wed, 30 Aug 2023 18:43:11 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Sep 2023 09:17:11 GMT"
},
{
"version": "v3",
"created": "Sat, 3 Feb 2024 22:44:45 GMT"
}
] | 1,707,177,600,000 | [
[
"Vo",
"Kiet Q. H.",
""
],
[
"Aadil",
"Muneeb",
""
],
[
"Chau",
"Siu Lun",
""
],
[
"Muandet",
"Krikamol",
""
]
] |
2308.16328 | Li He | Li He, Siyi Hu, Ailun Pei | Debunking Disinformation: Revolutionizing Truth with NLP in Fake News
Detection | The content is not particularly relevant to the research | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The Internet and social media have altered how individuals access news in the
age of instantaneous information distribution. While this development has
increased access to information, it has also created a significant problem: the
spread of fake news and information. Fake news is rapidly spreading on digital
platforms, which has a negative impact on the media ecosystem, public opinion,
decision-making, and social cohesion. Natural Language Processing(NLP), which
offers a variety of approaches to identify content as authentic, has emerged as
a potent weapon in the growing war against disinformation. This paper takes an
in-depth look at how NLP technology can be used to detect fake news and reveals
the challenges and opportunities it presents.
| [
{
"version": "v1",
"created": "Wed, 30 Aug 2023 21:25:31 GMT"
},
{
"version": "v2",
"created": "Wed, 15 Nov 2023 20:56:25 GMT"
}
] | 1,700,179,200,000 | [
[
"He",
"Li",
""
],
[
"Hu",
"Siyi",
""
],
[
"Pei",
"Ailun",
""
]
] |
2308.16364 | Matija Franklin | Matija Franklin, Philip Moreira Tomei, Rebecca Gorman | Strengthening the EU AI Act: Defining Key Terms on AI Manipulation | 10 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The European Union's Artificial Intelligence Act aims to regulate
manipulative and harmful uses of AI, but lacks precise definitions for key
concepts. This paper provides technical recommendations to improve the Act's
conceptual clarity and enforceability. We review psychological models to define
"personality traits," arguing the Act should protect full "psychometric
profiles." We urge expanding "behavior" to include "preferences" since
preferences causally influence and are influenced by behavior. Clear
definitions are provided for "subliminal," "manipulative," and "deceptive"
techniques, considering incentives, intent, and covertness. We distinguish
"exploiting individuals" from "exploiting groups," emphasising different policy
needs. An "informed decision" is defined by four facets: comprehension,
accurate information, no manipulation, and understanding AI's influence. We
caution the Act's therapeutic use exemption given the lack of regulation of
digital therapeutics by the EMA. Overall, the recommendations strengthen
definitions of vague concepts in the EU AI Act, enhancing precise applicability
to regulate harmful AI manipulation.
| [
{
"version": "v1",
"created": "Wed, 30 Aug 2023 23:42:07 GMT"
}
] | 1,693,526,400,000 | [
[
"Franklin",
"Matija",
""
],
[
"Tomei",
"Philip Moreira",
""
],
[
"Gorman",
"Rebecca",
""
]
] |
2308.16441 | Chen Zhao | Dong Li, Wenjun Wang, Minglai Shao, Chen Zhao | Contrastive Representation Learning Based on Multiple Node-centered
Subgraphs | CIKM 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As the basic element of graph-structured data, node has been recognized as
the main object of study in graph representation learning. A single node
intuitively has multiple node-centered subgraphs from the whole graph (e.g.,
one person in a social network has multiple social circles based on his
different relationships). We study this intuition under the framework of graph
contrastive learning, and propose a multiple node-centered subgraphs
contrastive representation learning method to learn node representation on
graphs in a self-supervised way. Specifically, we carefully design a series of
node-centered regional subgraphs of the central node. Then, the mutual
information between different subgraphs of the same node is maximized by
contrastive loss. Experiments on various real-world datasets and different
downstream tasks demonstrate that our model has achieved state-of-the-art
results.
| [
{
"version": "v1",
"created": "Thu, 31 Aug 2023 04:04:09 GMT"
}
] | 1,693,526,400,000 | [
[
"Li",
"Dong",
""
],
[
"Wang",
"Wenjun",
""
],
[
"Shao",
"Minglai",
""
],
[
"Zhao",
"Chen",
""
]
] |
2308.16538 | Carsten Maple | Carsten Maple, Lukasz Szpruch, Gregory Epiphaniou, Kalina Staykova,
Simran Singh, William Penwarden, Yisi Wen, Zijian Wang, Jagdish Hariharan,
Pavle Avramovic | The AI Revolution: Opportunities and Challenges for the Finance Sector | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This report examines Artificial Intelligence (AI) in the financial sector,
outlining its potential to revolutionise the industry and identify its
challenges. It underscores the criticality of a well-rounded understanding of
AI, its capabilities, and its implications to effectively leverage its
potential while mitigating associated risks. The potential of AI potential
extends from augmenting existing operations to paving the way for novel
applications in the finance sector. The application of AI in the financial
sector is transforming the industry. Its use spans areas from customer service
enhancements, fraud detection, and risk management to credit assessments and
high-frequency trading. However, along with these benefits, AI also presents
several challenges. These include issues related to transparency,
interpretability, fairness, accountability, and trustworthiness. The use of AI
in the financial sector further raises critical questions about data privacy
and security. A further issue identified in this report is the systemic risk
that AI can introduce to the financial sector. Being prone to errors, AI can
exacerbate existing systemic risks, potentially leading to financial crises.
Regulation is crucial to harnessing the benefits of AI while mitigating its
potential risks. Despite the global recognition of this need, there remains a
lack of clear guidelines or legislation for AI use in finance. This report
discusses key principles that could guide the formation of effective AI
regulation in the financial sector, including the need for a risk-based
approach, the inclusion of ethical considerations, and the importance of
maintaining a balance between innovation and consumer protection. The report
provides recommendations for academia, the finance industry, and regulators.
| [
{
"version": "v1",
"created": "Thu, 31 Aug 2023 08:30:09 GMT"
}
] | 1,693,526,400,000 | [
[
"Maple",
"Carsten",
""
],
[
"Szpruch",
"Lukasz",
""
],
[
"Epiphaniou",
"Gregory",
""
],
[
"Staykova",
"Kalina",
""
],
[
"Singh",
"Simran",
""
],
[
"Penwarden",
"William",
""
],
[
"Wen",
"Yisi",
""
],
[
"Wang",
"Zijian",
""
],
[
"Hariharan",
"Jagdish",
""
],
[
"Avramovic",
"Pavle",
""
]
] |
2308.16596 | Victor Qu\'etu | Victor Qu\'etu and Marta Milovanovi\'c | The Quest of Finding the Antidote to Sparse Double Descent | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In energy-efficient schemes, finding the optimal size of deep learning models
is very important and has a broad impact. Meanwhile, recent studies have
reported an unexpected phenomenon, the sparse double descent: as the model's
sparsity increases, the performance first worsens, then improves, and finally
deteriorates. Such a non-monotonic behavior raises serious questions about the
optimal model's size to maintain high performance: the model needs to be
sufficiently over-parametrized, but having too many parameters wastes training
resources.
In this paper, we aim to find the best trade-off efficiently. More precisely,
we tackle the occurrence of the sparse double descent and present some
solutions to avoid it. Firstly, we show that a simple $\ell_2$ regularization
method can help to mitigate this phenomenon but sacrifices the
performance/sparsity compromise. To overcome this problem, we then introduce a
learning scheme in which distilling knowledge regularizes the student model.
Supported by experimental results achieved using typical image classification
setups, we show that this approach leads to the avoidance of such a phenomenon.
| [
{
"version": "v1",
"created": "Thu, 31 Aug 2023 09:56:40 GMT"
}
] | 1,693,526,400,000 | [
[
"Quétu",
"Victor",
""
],
[
"Milovanović",
"Marta",
""
]
] |
2308.16615 | Lossan Bonde | Lossan Bonde, Severin Dembele | High Accuracy Location Information Extraction from Social Network Texts
Using Natural Language Processing | null | International Journal on Natural Language Computing (IJNLC)
Vol.12, No.4, August 2023 | 10.5121/ijnlc.2023.12401 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Terrorism has become a worldwide plague with severe consequences for the
development of nations. Besides killing innocent people daily and preventing
educational activities from taking place, terrorism is also hindering economic
growth. Machine Learning (ML) and Natural Language Processing (NLP) can
contribute to fighting terrorism by predicting in real-time future terrorist
attacks if accurate data is available. This paper is part of a research project
that uses text from social networks to extract necessary information to build
an adequate dataset for terrorist attack prediction. We collected a set of 3000
social network texts about terrorism in Burkina Faso and used a subset to
experiment with existing NLP solutions. The experiment reveals that existing
solutions have poor accuracy for location recognition, which our solution
resolves. We will extend the solution to extract dates and action information
to achieve the project's goal.
| [
{
"version": "v1",
"created": "Thu, 31 Aug 2023 10:21:24 GMT"
}
] | 1,693,526,400,000 | [
[
"Bonde",
"Lossan",
""
],
[
"Dembele",
"Severin",
""
]
] |
2308.16879 | Chen Zhao | Yujie Lin, Chen Zhao, Minglai Shao, Xujiang Zhao, Haifeng Chen | Adaptation Speed Analysis for Fairness-aware Causal Models | CIKM 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For example, in machine translation tasks, to achieve bidirectional
translation between two languages, the source corpus is often used as the
target corpus, which involves the training of two models with opposite
directions. The question of which one can adapt most quickly to a domain shift
is of significant importance in many fields. Specifically, consider an original
distribution p that changes due to an unknown intervention, resulting in a
modified distribution p*. In aligning p with p*, several factors can affect the
adaptation rate, including the causal dependencies between variables in p. In
real-life scenarios, however, we have to consider the fairness of the training
process, and it is particularly crucial to involve a sensitive variable (bias)
present between a cause and an effect variable. To explore this scenario, we
examine a simple structural causal model (SCM) with a cause-bias-effect
structure, where variable A acts as a sensitive variable between cause (X) and
effect (Y). The two models, respectively, exhibit consistent and contrary
cause-effect directions in the cause-bias-effect SCM. After conducting unknown
interventions on variables within the SCM, we can simulate some kinds of domain
shifts for analysis. We then compare the adaptation speeds of two models across
four shift scenarios. Additionally, we prove the connection between the
adaptation speeds of the two models across all interventions.
| [
{
"version": "v1",
"created": "Thu, 31 Aug 2023 17:36:57 GMT"
}
] | 1,693,526,400,000 | [
[
"Lin",
"Yujie",
""
],
[
"Zhao",
"Chen",
""
],
[
"Shao",
"Minglai",
""
],
[
"Zhao",
"Xujiang",
""
],
[
"Chen",
"Haifeng",
""
]
] |
2309.00138 | Pakizar Shamoi Dr | Pavel Kozlov, Alisher Akram, Pakizar Shamoi | Fuzzy Approach for Audio-Video Emotion Recognition in Computer Games for
Children | 8 pages. Prepared for the Elsevier conference | null | 10.1016/j.procs.2023.12.139 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computer games are widespread nowadays and enjoyed by people of all ages. But
when it comes to kids, playing these games can be more than just fun, it is a
way for them to develop important skills and build emotional intelligence.
Facial expressions and sounds that kids produce during gameplay reflect their
feelings, thoughts, and moods. In this paper, we propose a novel framework that
integrates a fuzzy approach for the recognition of emotions through the
analysis of audio and video data. Our focus lies within the specific context of
computer games tailored for children, aiming to enhance their overall user
experience. We use the FER dataset to detect facial emotions in video frames
recorded from the screen during the game. For the audio emotion recognition of
sounds a kid produces during the game, we use CREMA-D, TESS, RAVDESS, and Savee
datasets. Next, a fuzzy inference system is used for the fusion of results.
Besides this, our system can detect emotion stability and emotion diversity
during gameplay, which, together with prevailing emotion report, can serve as
valuable information for parents worrying about the effect of certain games on
their kids. The proposed approach has shown promising results in the
preliminary experiments we conducted, involving 3 different video games, namely
fighting, racing, and logic games, and providing emotion-tracking results for
kids in each game. Our study can contribute to the advancement of
child-oriented game development, which is not only engaging but also accounts
for children's cognitive and emotional states.
| [
{
"version": "v1",
"created": "Thu, 31 Aug 2023 21:22:00 GMT"
}
] | 1,705,881,600,000 | [
[
"Kozlov",
"Pavel",
""
],
[
"Akram",
"Alisher",
""
],
[
"Shamoi",
"Pakizar",
""
]
] |
2309.00172 | Thayanne Fran\c{c}a Da Silva | T. F. Silva and J. E. B. Maia | Detecting Evidence of Organization in groups by Trajectories | 17 pages, 16 figures, 3 algorithms, 1 table | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Effective detection of organizations is essential for fighting crime and
maintaining public safety, especially considering the limited human resources
and tools to deal with each group that exhibits co-movement patterns. This
paper focuses on solving the Network Structure Inference (NSI) challenge. Thus,
we introduce two new approaches to detect network structure inferences based on
agent trajectories. The first approach is based on the evaluation of graph
entropy, while the second considers the quality of clustering indices. To
evaluate the effectiveness of the new approaches, we conducted experiments
using four scenario simulations based on the animal kingdom, available on the
NetLogo platform: Ants, Wolf Sheep Predation, Flocking, and Ant Adaptation.
Furthermore, we compare the results obtained with those of an approach
previously proposed in the literature, applying all methods to simulations of
the NetLogo platform. The results demonstrate that our new detection approaches
can more clearly identify the inferences of organizations or networks in the
simulated scenarios.
| [
{
"version": "v1",
"created": "Thu, 31 Aug 2023 23:57:02 GMT"
}
] | 1,693,785,600,000 | [
[
"Silva",
"T. F.",
""
],
[
"Maia",
"J. E. B.",
""
]
] |
2309.00300 | Jiatong Li | Jiatong Li, Qi Liu, Fei Wang, Jiayu Liu, Zhenya Huang, Fangzhou Yao,
Linbo Zhu, Yu Su | Towards the Identifiability and Explainability for Personalized Learner
Modeling: An Inductive Paradigm | Accepted by the ACM Web Conference 2024 (WWW '24) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Personalized learner modeling using cognitive diagnosis (CD), which aims to
model learners' cognitive states by diagnosing learner traits from behavioral
data, is a fundamental yet significant task in many web learning services.
Existing cognitive diagnosis models (CDMs) follow the proficiency-response
paradigm that views learner traits and question parameters as trainable
embeddings and learns them through learner performance prediction. However, we
notice that this paradigm leads to the inevitable non-identifiability and
explainability overfitting problem, which is harmful to the quantification of
learners' cognitive states and the quality of web learning services. To address
these problems, we propose an identifiable cognitive diagnosis framework
(ID-CDF) based on a novel response-proficiency-response paradigm inspired by
encoder-decoder models. Specifically, we first devise the diagnostic module of
ID-CDF, which leverages inductive learning to eliminate randomness in
optimization to guarantee identifiability and captures the monotonicity between
overall response data distribution and cognitive states to prevent
explainability overfitting. Next, we propose a flexible predictive module for
ID-CDF to ensure diagnosis preciseness. We further present an implementation of
ID-CDF, i.e., ID-CDM, to illustrate its usability. Extensive experiments on
four real-world datasets with different characteristics demonstrate that ID-CDF
can effectively address the problems without loss of diagnosis preciseness.
| [
{
"version": "v1",
"created": "Fri, 1 Sep 2023 07:18:02 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Feb 2024 14:34:31 GMT"
},
{
"version": "v3",
"created": "Fri, 2 Feb 2024 03:53:12 GMT"
},
{
"version": "v4",
"created": "Mon, 19 Feb 2024 15:01:33 GMT"
}
] | 1,708,387,200,000 | [
[
"Li",
"Jiatong",
""
],
[
"Liu",
"Qi",
""
],
[
"Wang",
"Fei",
""
],
[
"Liu",
"Jiayu",
""
],
[
"Huang",
"Zhenya",
""
],
[
"Yao",
"Fangzhou",
""
],
[
"Zhu",
"Linbo",
""
],
[
"Su",
"Yu",
""
]
] |
2309.00306 | Patrick Betz | Patrick Betz, Stefan L\"udtke, Christian Meilicke, Heiner
Stuckenschmidt | On the Aggregation of Rules for Knowledge Graph Completion | KLR Workshop@ICML2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Rule learning approaches for knowledge graph completion are efficient,
interpretable and competitive to purely neural models. The rule aggregation
problem is concerned with finding one plausibility score for a candidate fact
which was simultaneously predicted by multiple rules. Although the problem is
ubiquitous, as data-driven rule learning can result in noisy and large
rulesets, it is underrepresented in the literature and its theoretical
foundations have not been studied before in this context. In this work, we
demonstrate that existing aggregation approaches can be expressed as marginal
inference operations over the predicting rules. In particular, we show that the
common Max-aggregation strategy, which scores candidates based on the rule with
the highest confidence, has a probabilistic interpretation. Finally, we propose
an efficient and overlooked baseline which combines the previous strategies and
is competitive to computationally more expensive approaches.
| [
{
"version": "v1",
"created": "Fri, 1 Sep 2023 07:32:11 GMT"
}
] | 1,693,785,600,000 | [
[
"Betz",
"Patrick",
""
],
[
"Lüdtke",
"Stefan",
""
],
[
"Meilicke",
"Christian",
""
],
[
"Stuckenschmidt",
"Heiner",
""
]
] |
2309.00317 | Son T. Luu | Anh Hoang Tran, Tam Minh Nguyen and Son T. Luu | A Text-based Approach For Link Prediction on Wikipedia Articles | Accepted by DSAA 2023 Conference in the DSAA Student Competition
Section | null | 10.1109/DSAA60987.2023.10302627 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This paper present our work in the DSAA 2023 Challenge about Link Prediction
for Wikipedia Articles. We use traditional machine learning models with POS
tags (part-of-speech tags) features extracted from text to train the
classification model for predicting whether two nodes has the link. Then, we
use these tags to test on various machine learning models. We obtained the
results by F1 score at 0.99999 and got 7th place in the competition. Our source
code is publicly available at this link:
https://github.com/Tam1032/DSAA2023-Challenge-Link-prediction-DS-UIT_SAT
| [
{
"version": "v1",
"created": "Fri, 1 Sep 2023 08:00:43 GMT"
},
{
"version": "v2",
"created": "Tue, 7 Nov 2023 03:32:14 GMT"
}
] | 1,699,401,600,000 | [
[
"Tran",
"Anh Hoang",
""
],
[
"Nguyen",
"Tam Minh",
""
],
[
"Luu",
"Son T.",
""
]
] |
2309.01194 | Haomin Wen | Haomin Wen, Youfang Lin, Lixia Wu, Xiaowei Mao, Tianyue Cai, Yunfeng
Hou, Shengnan Guo, Yuxuan Liang, Guangyin Jin, Yiji Zhao, Roger Zimmermann,
Jieping Ye, Huaiyu Wan | A Survey on Service Route and Time Prediction in Instant Delivery:
Taxonomy, Progress, and Prospects | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Instant delivery services, such as food delivery and package delivery, have
achieved explosive growth in recent years by providing customers with
daily-life convenience. An emerging research area within these services is
service Route\&Time Prediction (RTP), which aims to estimate the future service
route as well as the arrival time of a given worker. As one of the most crucial
tasks in those service platforms, RTP stands central to enhancing user
satisfaction and trimming operational expenditures on these platforms. Despite
a plethora of algorithms developed to date, there is no systematic,
comprehensive survey to guide researchers in this domain. To fill this gap, our
work presents the first comprehensive survey that methodically categorizes
recent advances in service route and time prediction. We start by defining the
RTP challenge and then delve into the metrics that are often employed.
Following that, we scrutinize the existing RTP methodologies, presenting a
novel taxonomy of them. We categorize these methods based on three criteria:
(i) type of task, subdivided into only-route prediction, only-time prediction,
and joint route\&time prediction; (ii) model architecture, which encompasses
sequence-based and graph-based models; and (iii) learning paradigm, including
Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively,
we highlight the limitations of current research and suggest prospective
avenues. We believe that the taxonomy, progress, and prospects introduced in
this paper can significantly promote the development of this field.
| [
{
"version": "v1",
"created": "Sun, 3 Sep 2023 14:43:33 GMT"
}
] | 1,693,958,400,000 | [
[
"Wen",
"Haomin",
""
],
[
"Lin",
"Youfang",
""
],
[
"Wu",
"Lixia",
""
],
[
"Mao",
"Xiaowei",
""
],
[
"Cai",
"Tianyue",
""
],
[
"Hou",
"Yunfeng",
""
],
[
"Guo",
"Shengnan",
""
],
[
"Liang",
"Yuxuan",
""
],
[
"Jin",
"Guangyin",
""
],
[
"Zhao",
"Yiji",
""
],
[
"Zimmermann",
"Roger",
""
],
[
"Ye",
"Jieping",
""
],
[
"Wan",
"Huaiyu",
""
]
] |
2309.01622 | Mla{\dj}an Jovanovi\'c Dr | Peter Voss and Mladjan Jovanovic | Concepts is All You Need: A More Direct Path to AGI | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Little demonstrable progress has been made toward AGI (Artificial General
Intelligence) since the term was coined some 20 years ago. In spite of the
fantastic breakthroughs in Statistical AI such as AlphaZero, ChatGPT, and
Stable Diffusion none of these projects have, or claim to have, a clear path to
AGI. In order to expedite the development of AGI it is crucial to understand
and identify the core requirements of human-like intelligence as it pertains to
AGI. From that one can distill which particular development steps are necessary
to achieve AGI, and which are a distraction. Such analysis highlights the need
for a Cognitive AI approach rather than the currently favored statistical and
generative efforts. More specifically it identifies the central role of
concepts in human-like cognition. Here we outline an architecture and
development plan, together with some preliminary results, that offers a much
more direct path to full Human-Level AI (HLAI)/ AGI.
| [
{
"version": "v1",
"created": "Mon, 4 Sep 2023 14:14:41 GMT"
}
] | 1,693,958,400,000 | [
[
"Voss",
"Peter",
""
],
[
"Jovanovic",
"Mladjan",
""
]
] |
2309.02009 | Florence Dupin de Saint-Cyr | Florence Dupin de Saint Cyr - Bannay (IRIT-ADRIA), Henri Prade
(IRIT-ADRIA) | Belief revision and incongruity: is it a joke? | A special paper on/in humor/honor for/of Philippe Besnard | Journal of Applied Non-Classical Logics, In press, Special issue
in honour of Philippe Besnard, pp.1-28 | 10.1080/11663081.2023.2244379 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Incongruity often makes people laugh. You have to be smart to say stupid
things. It requires to be even smarter for understanding them. This paper is a
shameless attempt to formalize this intelligent behavior in the case of an
agent listening to a joke. All this is a matter of revision of beliefs,
surprise and violation of norms.
| [
{
"version": "v1",
"created": "Tue, 5 Sep 2023 07:47:08 GMT"
}
] | 1,693,958,400,000 | [
[
"Bannay",
"Florence Dupin de Saint Cyr -",
"",
"IRIT-ADRIA"
],
[
"Prade",
"Henri",
"",
"IRIT-ADRIA"
]
] |
2309.02287 | Kim Hammar | Kim Hammar and Neil Dhir | Optimal Observation-Intervention Trade-Off in Optimisation Problems with
Causal Structure | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | We consider the problem of optimising an expensive-to-evaluate grey-box
objective function, within a finite budget, where known side-information exists
in the form of the causal structure between the design variables. Standard
black-box optimisation ignores the causal structure, often making it
inefficient and expensive. The few existing methods that consider the causal
structure are myopic and do not fully accommodate the observation-intervention
trade-off that emerges when estimating causal effects. In this paper, we show
that the observation-intervention trade-off can be formulated as a non-myopic
optimal stopping problem which permits an efficient solution. We give
theoretical results detailing the structure of the optimal stopping times and
demonstrate the generality of our approach by showing that it can be integrated
with existing causal Bayesian optimisation algorithms. Experimental results
show that our formulation can enhance existing algorithms on real and synthetic
benchmarks.
| [
{
"version": "v1",
"created": "Tue, 5 Sep 2023 14:46:06 GMT"
}
] | 1,693,958,400,000 | [
[
"Hammar",
"Kim",
""
],
[
"Dhir",
"Neil",
""
]
] |
2309.03041 | Xuanxiang Huang | Xuanxiang Huang, Joao Marques-Silva | A Refutation of Shapley Values for Explainability | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recent work demonstrated the existence of Boolean functions for which Shapley
values provide misleading information about the relative importance of features
in rule-based explanations. Such misleading information was broadly categorized
into a number of possible issues. Each of those issues relates with features
being relevant or irrelevant for a prediction, and all are significant
regarding the inadequacy of Shapley values for rule-based explainability. This
earlier work devised a brute-force approach to identify Boolean functions,
defined on small numbers of features, and also associated instances, which
displayed such inadequacy-revealing issues, and so served as evidence to the
inadequacy of Shapley values for rule-based explainability. However, an
outstanding question is how frequently such inadequacy-revealing issues can
occur for Boolean functions with arbitrary large numbers of features. It is
plain that a brute-force approach would be unlikely to provide insights on how
to tackle this question. This paper answers the above question by proving that,
for any number of features, there exist Boolean functions that exhibit one or
more inadequacy-revealing issues, thereby contributing decisive arguments
against the use of Shapley values as the theoretical underpinning of
feature-attribution methods in explainability.
| [
{
"version": "v1",
"created": "Wed, 6 Sep 2023 14:34:18 GMT"
},
{
"version": "v2",
"created": "Tue, 13 Feb 2024 07:35:25 GMT"
}
] | 1,707,868,800,000 | [
[
"Huang",
"Xuanxiang",
""
],
[
"Marques-Silva",
"Joao",
""
]
] |
2309.03638 | Yulu Pi | Yulu Pi | Beyond XAI:Obstacles Towards Responsible AI | work in progress | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rapidly advancing domain of Explainable Artificial Intelligence (XAI) has
sparked significant interests in developing techniques to make AI systems more
transparent and understandable. Nevertheless, in real-world contexts, the
methods of explainability and their evaluation strategies present numerous
limitations.Moreover, the scope of responsible AI extends beyond just
explainability. In this paper, we explore these limitations and discuss their
implications in a boarder context of responsible AI when considering other
important aspects, including privacy, fairness and contestability.
| [
{
"version": "v1",
"created": "Thu, 7 Sep 2023 11:08:14 GMT"
}
] | 1,694,131,200,000 | [
[
"Pi",
"Yulu",
""
]
] |
2309.03651 | Manuel Eberhardinger | Manuel Eberhardinger, Johannes Maucher, Setareh Maghsudi | Learning of Generalizable and Interpretable Knowledge in Grid-Based
Reinforcement Learning Environments | to be published in AIIDE 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Understanding the interactions of agents trained with deep reinforcement
learning is crucial for deploying agents in games or the real world. In the
former, unreasonable actions confuse players. In the latter, that effect is
even more significant, as unexpected behavior cause accidents with potentially
grave and long-lasting consequences for the involved individuals. In this work,
we propose using program synthesis to imitate reinforcement learning policies
after seeing a trajectory of the action sequence. Programs have the advantage
that they are inherently interpretable and verifiable for correctness. We adapt
the state-of-the-art program synthesis system DreamCoder for learning concepts
in grid-based environments, specifically, a navigation task and two miniature
versions of Atari games, Space Invaders and Asterix. By inspecting the
generated libraries, we can make inferences about the concepts the black-box
agent has learned and better understand the agent's behavior. We achieve the
same by visualizing the agent's decision-making process for the imitated
sequences. We evaluate our approach with different types of program
synthesizers based on a search-only method, a neural-guided search, and a
language model fine-tuned on code.
| [
{
"version": "v1",
"created": "Thu, 7 Sep 2023 11:46:57 GMT"
}
] | 1,694,131,200,000 | [
[
"Eberhardinger",
"Manuel",
""
],
[
"Maucher",
"Johannes",
""
],
[
"Maghsudi",
"Setareh",
""
]
] |
2309.04295 | Chengwu Liu | Chengwu Liu, Jianhao Shen, Huajian Xin, Zhengying Liu, Ye Yuan,
Haiming Wang, Wei Ju, Chuanyang Zheng, Yichun Yin, Lin Li, Ming Zhang, Qun
Liu | FIMO: A Challenge Formal Dataset for Automated Theorem Proving | Added a hyperlink to the dataset made accessible on GitHub | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present FIMO, an innovative dataset comprising formal mathematical problem
statements sourced from the International Mathematical Olympiad (IMO)
Shortlisted Problems. Designed to facilitate advanced automated theorem proving
at the IMO level, FIMO is currently tailored for the Lean formal language. It
comprises 149 formal problem statements, accompanied by both informal problem
descriptions and their corresponding LaTeX-based informal proofs. Through
initial experiments involving GPT-4, our findings underscore the existing
limitations in current methodologies, indicating a substantial journey ahead
before achieving satisfactory IMO-level automated theorem proving outcomes.
| [
{
"version": "v1",
"created": "Fri, 8 Sep 2023 12:34:28 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Dec 2023 08:38:01 GMT"
}
] | 1,701,820,800,000 | [
[
"Liu",
"Chengwu",
""
],
[
"Shen",
"Jianhao",
""
],
[
"Xin",
"Huajian",
""
],
[
"Liu",
"Zhengying",
""
],
[
"Yuan",
"Ye",
""
],
[
"Wang",
"Haiming",
""
],
[
"Ju",
"Wei",
""
],
[
"Zheng",
"Chuanyang",
""
],
[
"Yin",
"Yichun",
""
],
[
"Li",
"Lin",
""
],
[
"Zhang",
"Ming",
""
],
[
"Liu",
"Qun",
""
]
] |
2309.05371 | Jean-Baptiste Herv\'e | Jean-Baptiste Herv\'e, Oliver Withington, Marion Herv\'e, Laurissa
Tokarchuk, Christoph Salge | Exploring Minecraft Settlement Generators with Generative Shift Analysis | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With growing interest in Procedural Content Generation (PCG) it becomes
increasingly important to develop methods and tools for evaluating and
comparing alternative systems. There is a particular lack regarding the
evaluation of generative pipelines, where a set of generative systems work in
series to make iterative changes to an artifact. We introduce a novel method
called Generative Shift for evaluating the impact of individual stages in a PCG
pipeline by quantifying the impact that a generative process has when it is
applied to a pre-existing artifact. We explore this technique by applying it to
a very rich dataset of Minecraft game maps produced by a set of alternative
settlement generators developed as part of the Generative Design in Minecraft
Competition (GDMC), all of which are designed to produce appropriate
settlements for a pre-existing map. While this is an early exploration of this
technique we find it to be a promising lens to apply to PCG evaluation, and we
are optimistic about the potential of Generative Shift to be a domain-agnostic
method for evaluating generative pipelines.
| [
{
"version": "v1",
"created": "Mon, 11 Sep 2023 10:48:42 GMT"
}
] | 1,694,476,800,000 | [
[
"Hervé",
"Jean-Baptiste",
""
],
[
"Withington",
"Oliver",
""
],
[
"Hervé",
"Marion",
""
],
[
"Tokarchuk",
"Laurissa",
""
],
[
"Salge",
"Christoph",
""
]
] |
2309.06888 | Konrad Abicht | Konrad Abicht | OWL Reasoners still useable in 2023 | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In a systematic literature and software review over 100 OWL reasoners/systems
were analyzed to see if they would still be usable in 2023. This has never been
done in this capacity. OWL reasoners still play an important role in knowledge
organisation and management, but the last comprehensive surveys/studies are
more than 8 years old. The result of this work is a comprehensive list of 95
standalone OWL reasoners and systems using an OWL reasoner. For each item,
information on project pages, source code repositories and related
documentation was gathered. The raw research data is provided in a Github
repository for anyone to use.
| [
{
"version": "v1",
"created": "Wed, 13 Sep 2023 11:22:42 GMT"
}
] | 1,694,649,600,000 | [
[
"Abicht",
"Konrad",
""
]
] |
2309.08611 | Hongpeng Zhang | Zhang Hong-Peng | Maneuver Decision-Making Through Proximal Policy Optimization And Monte
Carlo Tree Search | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Maneuver decision-making can be regarded as a Markov decision process and can
be address by reinforcement learning. However, original reinforcement learning
algorithms can hardly solve the maneuvering decision-making problem. One reason
is that agents use random actions in the early stages of training, which makes
it difficult to get rewards and learn how to make effective decisions. To
address this issue, a method based on proximal policy optimization and Monte
Carlo tree search is proposed. The method uses proximal policy optimization to
train the agent, and regards the results of air combat as targets to train the
value network. Then, based on the value network and the visit count of each
node, Monte Carlo tree search is used to find the actions with more expected
returns than random actions, which can improve the training performance. The
ablation studies and simulation experiments indicate that agents trained by the
proposed method can make different decisions according to different states,
which demonstrates that the method can solve the maneuvering decision problem
that the original reinforcement learning algorithm cannot solve.
| [
{
"version": "v1",
"created": "Mon, 28 Aug 2023 14:48:49 GMT"
}
] | 1,695,081,600,000 | [
[
"Hong-Peng",
"Zhang",
""
]
] |
2309.08978 | Shiqi Jiang | Fucheng Jia, Shiqi Jiang, Ting Cao, Wei Cui, Tianrui Xia, Xu Cao,
Yuanchun Li, Deyu Zhang, Ju Ren, Yunxin Liu, Lili Qiu, Mao Yang | Accelerating In-Browser Deep Learning Inference on Diverse Edge Clients
through Just-in-Time Kernel Optimizations | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Web applications are increasingly becoming the primary platform for AI
service delivery, making in-browser deep learning (DL) inference more
prominent. However, current in-browser inference systems fail to effectively
utilize advanced web programming techniques and customize kernels for various
client devices, leading to suboptimal performance.
To address the issues, this paper presents the first in-browser inference
system, nn-JIT.web, which enables just-in-time (JIT) auto-generation of
optimized kernels for both CPUs and GPUs during inference. The system achieves
this by using two novel web programming techniques that can significantly
reduce kernel generation time, compared to other tensor compilers such as TVM,
while maintaining or even improving performance. The first technique,
Tensor-Web Compiling Co-Design, lowers compiling costs by unifying tensor and
web compiling and eliminating redundant and ineffective compiling passes. The
second technique, Web-Specific Lite Kernel Optimization Space Design, reduces
kernel tuning costs by focusing on web programming requirements and efficient
hardware resource utilization, limiting the optimization space to only dozens.
nn-JIT.web is evaluated for modern transformer models on a range of client
devices, including the mainstream CPUs and GPUs from ARM, Intel, AMD and
Nvidia. Results show that nn-JIT.web can achieve up to 8.2x faster within 30
seconds compared to the baselines across various models.
| [
{
"version": "v1",
"created": "Sat, 16 Sep 2023 12:29:25 GMT"
}
] | 1,695,081,600,000 | [
[
"Jia",
"Fucheng",
""
],
[
"Jiang",
"Shiqi",
""
],
[
"Cao",
"Ting",
""
],
[
"Cui",
"Wei",
""
],
[
"Xia",
"Tianrui",
""
],
[
"Cao",
"Xu",
""
],
[
"Li",
"Yuanchun",
""
],
[
"Zhang",
"Deyu",
""
],
[
"Ren",
"Ju",
""
],
[
"Liu",
"Yunxin",
""
],
[
"Qiu",
"Lili",
""
],
[
"Yang",
"Mao",
""
]
] |
2309.09125 | Anas El Fathi | Anas El Fathi, Marc D. Breton | Using Reinforcement Learning to Simplify Mealtime Insulin Dosing for
People with Type 1 Diabetes: In-Silico Experiments | 6 pages, 4 figures, conference | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | People with type 1 diabetes (T1D) struggle to calculate the optimal insulin
dose at mealtime, especially when under multiple daily injections (MDI)
therapy. Effectively, they will not always perform rigorous and precise
calculations, but occasionally, they might rely on intuition and previous
experience. Reinforcement learning (RL) has shown outstanding results in
outperforming humans on tasks requiring intuition and learning from experience.
In this work, we propose an RL agent that recommends the optimal
meal-accompanying insulin dose corresponding to a qualitative meal (QM)
strategy that does not require precise carbohydrate counting (CC) (e.g., a
usual meal at noon.). The agent is trained using the soft actor-critic approach
and comprises long short-term memory (LSTM) neurons. For training, eighty
virtual subjects (VS) of the FDA-accepted UVA/Padova T1D adult population were
simulated using MDI therapy and QM strategy. For validation, the remaining
twenty VS were examined in 26-week scenarios, including intra- and inter-day
variabilities in glucose. \textit{In-silico} results showed that the proposed
RL approach outperforms a baseline run-to-run approach and can replace the
standard CC approach. Specifically, after 26 weeks, the time-in-range
($70-180$mg/dL) and time-in-hypoglycemia ($<70$mg/dL) were $73.1\pm11.6$% and $
2.0\pm 1.8$% using the RL-optimized QM strategy compared to $70.6\pm14.8$% and
$ 1.5\pm 1.5$% using CC. Such an approach can simplify diabetes treatment,
resulting in improved quality of life and glycemic outcomes.
| [
{
"version": "v1",
"created": "Sun, 17 Sep 2023 01:34:02 GMT"
}
] | 1,695,081,600,000 | [
[
"Fathi",
"Anas El",
""
],
[
"Breton",
"Marc D.",
""
]
] |
2309.09404 | Siva Likitha Valluru | Siva Likitha Valluru, Biplav Srivastava, Sai Teja Paladi, Siwen Yan,
Sriraam Natarajan | Promoting Research Collaboration with Open Data Driven Team
Recommendation in Response to Call for Proposals | 9 pages, 2 figures, 3 tables, Accepted to The Thirty-Sixth Annual
Conference on Innovative Applications of Artificial Intelligence
(IAAI/AAAI-24) | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Building teams and promoting collaboration are two very common business
activities. An example of these are seen in the TeamingForFunding problem,
where research institutions and researchers are interested to identify
collaborative opportunities when applying to funding agencies in response to
latter's calls for proposals. We describe a novel system to recommend teams
using a variety of AI methods, such that (1) each team achieves the highest
possible skill coverage that is demanded by the opportunity, and (2) the
workload of distributing the opportunities is balanced amongst the candidate
members. We address these questions by extracting skills latent in open data of
proposal calls (demand) and researcher profiles (supply), normalizing them
using taxonomies, and creating efficient algorithms that match demand to
supply. We create teams to maximize goodness along a novel metric balancing
short- and long-term objectives. We validate the success of our algorithms (1)
quantitatively, by evaluating the recommended teams using a goodness score and
find that more informed methods lead to recommendations of smaller number of
teams but higher goodness, and (2) qualitatively, by conducting a large-scale
user study at a college-wide level, and demonstrate that users overall found
the tool very useful and relevant. Lastly, we evaluate our system in two
diverse settings in US and India (of researchers and proposal calls) to
establish generality of our approach, and deploy it at a major US university
for routine use.
| [
{
"version": "v1",
"created": "Mon, 18 Sep 2023 00:04:08 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Sep 2023 05:55:14 GMT"
},
{
"version": "v3",
"created": "Tue, 24 Oct 2023 03:21:32 GMT"
},
{
"version": "v4",
"created": "Fri, 5 Jan 2024 04:54:24 GMT"
},
{
"version": "v5",
"created": "Thu, 25 Jan 2024 16:22:56 GMT"
}
] | 1,706,227,200,000 | [
[
"Valluru",
"Siva Likitha",
""
],
[
"Srivastava",
"Biplav",
""
],
[
"Paladi",
"Sai Teja",
""
],
[
"Yan",
"Siwen",
""
],
[
"Natarajan",
"Sriraam",
""
]
] |
2309.09416 | Gilles Blondel | Gilles Blondel | Causal Discovery and Prediction: Methods and Algorithms | PhD Thesis, 101 pages. arXiv admin note: text overlap with
arXiv:1610.05556 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We are not only observers but also actors of reality. Our capability to
intervene and alter the course of some events in the space and time surrounding
us is an essential component of how we build our model of the world. In this
doctoral thesis we introduce a generic a-priori assessment of each possible
intervention, in order to select the most cost-effective interventions only,
and avoid unnecessary systematic experimentation on the real world. Based on
this a-priori assessment, we propose an active learning algorithm that
identifies the causal relations in any given causal model, using a least cost
sequence of interventions. There are several novel aspects introduced by our
algorithm. It is, in most case scenarios, able to discard many causal model
candidates using relatively inexpensive interventions that only test one value
of the intervened variables. Also, the number of interventions performed by the
algorithm can be bounded by the number of causal model candidates. Hence, fewer
initial candidates (or equivalently, more prior knowledge) lead to fewer
interventions for causal discovery.
Causality is intimately related to time, as causes appear to precede their
effects. Cyclical causal processes are a very interesting case of causality in
relation to time. In this doctoral thesis we introduce a formal analysis of
time cyclical causal settings by defining a causal analog to the purely
observational Dynamic Bayesian Networks, and provide a sound and complete
algorithm for the identification of causal effects in the cyclic setting. We
introduce the existence of two types of hidden confounder variables in this
framework, which affect in substantially different ways the identification
procedures, a distinction with no analog in either Dynamic Bayesian Networks or
standard causal graphs.
| [
{
"version": "v1",
"created": "Mon, 18 Sep 2023 01:19:37 GMT"
}
] | 1,695,081,600,000 | [
[
"Blondel",
"Gilles",
""
]
] |
2309.09441 | Hossein Jamali | Hossein Jamali, Ponkoj Chandra Shill, David Feil-Seifer, Frederick C.
Harris, Jr., Sergiu M. Dascalu | A Schedule of Duties in the Cloud Space Using a Modified Salp Swarm
Algorithm | 15 pages, 6 figures, 2023 IFIP International Internet of Things
Conference. Dallas-Fort Worth Metroplex, Texas, USA | null | 10.1007/978-3-031-45878-1_5 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Cloud computing is a concept introduced in the information technology era,
with the main components being the grid, distributed, and valuable computing.
The cloud is being developed continuously and, naturally, comes up with many
challenges, one of which is scheduling. A schedule or timeline is a mechanism
used to optimize the time for performing a duty or set of duties. A scheduling
process is accountable for choosing the best resources for performing a duty.
The main goal of a scheduling algorithm is to improve the efficiency and
quality of the service while at the same time ensuring the acceptability and
effectiveness of the targets. The task scheduling problem is one of the most
important NP-hard issues in the cloud domain and, so far, many techniques have
been proposed as solutions, including using genetic algorithms (GAs), particle
swarm optimization, (PSO), and ant colony optimization (ACO). To address this
problem, in this paper, one of the collective intelligence algorithms, called
the Salp Swarm Algorithm (SSA), has been expanded, improved, and applied. The
performance of the proposed algorithm has been compared with that of GAs, PSO,
continuous ACO, and the basic SSA. The results show that our algorithm has
generally higher performance than the other algorithms. For example, compared
to the basic SSA, the proposed method has an average reduction of approximately
21% in makespan.
| [
{
"version": "v1",
"created": "Mon, 18 Sep 2023 02:48:41 GMT"
}
] | 1,708,473,600,000 | [
[
"Jamali",
"Hossein",
""
],
[
"Shill",
"Ponkoj Chandra",
""
],
[
"Feil-Seifer",
"David",
""
],
[
"Harris,",
"Frederick C.",
"Jr."
],
[
"Dascalu",
"Sergiu M.",
""
]
] |
2309.09500 | Zijian Zhang | Zijian Zhang, Xiangyu Zhao, Qidong Liu, Chunxu Zhang, Qian Ma, Wanyu
Wang, Hongwei Zhao, Yiqi Wang and Zitao Liu | PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the era of information explosion, spatio-temporal data mining serves as a
critical part of urban management. Considering the various fields demanding
attention, e.g., traffic state, human activity, and social event, predicting
multiple spatio-temporal attributes simultaneously can alleviate regulatory
pressure and foster smart city construction. However, current research can not
handle the spatio-temporal multi-attribute prediction well due to the complex
relationships between diverse attributes. The key challenge lies in how to
address the common spatio-temporal patterns while tackling their distinctions.
In this paper, we propose an effective solution for spatio-temporal
multi-attribute prediction, PromptST. We devise a spatio-temporal transformer
and a parameter-sharing training scheme to address the common knowledge among
different spatio-temporal attributes. Then, we elaborate a spatio-temporal
prompt tuning strategy to fit the specific attributes in a lightweight manner.
Through the pretrain and prompt tuning phases, our PromptST is able to enhance
the specific spatio-temoral characteristic capture by prompting the backbone
model to fit the specific target attribute while maintaining the learned common
knowledge. Extensive experiments on real-world datasets verify that our
PromptST attains state-of-the-art performance. Furthermore, we also prove
PromptST owns good transferability on unseen spatio-temporal attributes, which
brings promising application potential in urban computing. The implementation
code is available to ease reproducibility.
| [
{
"version": "v1",
"created": "Mon, 18 Sep 2023 05:57:12 GMT"
}
] | 1,695,081,600,000 | [
[
"Zhang",
"Zijian",
""
],
[
"Zhao",
"Xiangyu",
""
],
[
"Liu",
"Qidong",
""
],
[
"Zhang",
"Chunxu",
""
],
[
"Ma",
"Qian",
""
],
[
"Wang",
"Wanyu",
""
],
[
"Zhao",
"Hongwei",
""
],
[
"Wang",
"Yiqi",
""
],
[
"Liu",
"Zitao",
""
]
] |
2309.09770 | Carlos Mougan | Carlos Mougan, Richard Plant, Clare Teng, Marya Bazzi, Alvaro
Cabrejas-Egea, Ryan Sze-Yin Chan, David Salvador Jasin, Martin Stoffel,
Kirstie Jane Whitaker, Jules Manser | How to Data in Datathons | 37th Conference on Neural Information Processing Systems (NeurIPS
2023) Track on Datasets and Benchmark | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rise of datathons, also known as data or data science hackathons, has
provided a platform to collaborate, learn, and innovate in a short timeframe.
Despite their significant potential benefits, organizations often struggle to
effectively work with data due to a lack of clear guidelines and best practices
for potential issues that might arise. Drawing on our own experiences and
insights from organizing >80 datathon challenges with >60 partnership
organizations since 2016, we provide guidelines and recommendations that serve
as a resource for organizers to navigate the data-related complexities of
datathons. We apply our proposed framework to 10 case studies.
| [
{
"version": "v1",
"created": "Mon, 18 Sep 2023 13:51:23 GMT"
},
{
"version": "v2",
"created": "Tue, 19 Sep 2023 15:44:27 GMT"
},
{
"version": "v3",
"created": "Mon, 9 Oct 2023 12:43:06 GMT"
},
{
"version": "v4",
"created": "Wed, 25 Oct 2023 10:20:20 GMT"
}
] | 1,698,278,400,000 | [
[
"Mougan",
"Carlos",
""
],
[
"Plant",
"Richard",
""
],
[
"Teng",
"Clare",
""
],
[
"Bazzi",
"Marya",
""
],
[
"Cabrejas-Egea",
"Alvaro",
""
],
[
"Chan",
"Ryan Sze-Yin",
""
],
[
"Jasin",
"David Salvador",
""
],
[
"Stoffel",
"Martin",
""
],
[
"Whitaker",
"Kirstie Jane",
""
],
[
"Manser",
"Jules",
""
]
] |
2309.09825 | Minjia Mao | Xiao Fang, Shangkun Che, Minjia Mao, Hongzhe Zhang, Ming Zhao,
Xiaohang Zhao | Bias of AI-Generated Content: An Examination of News Produced by Large
Language Models | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Large language models (LLMs) have the potential to transform our lives and
work through the content they generate, known as AI-Generated Content (AIGC).
To harness this transformation, we need to understand the limitations of LLMs.
Here, we investigate the bias of AIGC produced by seven representative LLMs,
including ChatGPT and LLaMA. We collect news articles from The New York Times
and Reuters, both known for their dedication to provide unbiased news. We then
apply each examined LLM to generate news content with headlines of these news
articles as prompts, and evaluate the gender and racial biases of the AIGC
produced by the LLM by comparing the AIGC and the original news articles. We
further analyze the gender bias of each LLM under biased prompts by adding
gender-biased messages to prompts constructed from these news headlines. Our
study reveals that the AIGC produced by each examined LLM demonstrates
substantial gender and racial biases. Moreover, the AIGC generated by each LLM
exhibits notable discrimination against females and individuals of the Black
race. Among the LLMs, the AIGC generated by ChatGPT demonstrates the lowest
level of bias, and ChatGPT is the sole model capable of declining content
generation when provided with biased prompts.
| [
{
"version": "v1",
"created": "Mon, 18 Sep 2023 14:47:24 GMT"
},
{
"version": "v2",
"created": "Tue, 19 Sep 2023 01:13:22 GMT"
},
{
"version": "v3",
"created": "Wed, 3 Apr 2024 19:47:54 GMT"
}
] | 1,712,275,200,000 | [
[
"Fang",
"Xiao",
""
],
[
"Che",
"Shangkun",
""
],
[
"Mao",
"Minjia",
""
],
[
"Zhang",
"Hongzhe",
""
],
[
"Zhao",
"Ming",
""
],
[
"Zhao",
"Xiaohang",
""
]
] |
2309.09864 | Daria de Tinguy | Daria de Tinguy, Toon Van de Maele, Tim Verbelen, Bart Dhoedt | Learning Spatial and Temporal Hierarchies: Hierarchical Active Inference
for navigation in Multi-Room Maze Environments | IROS 2023 Workshop World Models and Predictive Coding in Cognitive
Robotics. arXiv admin note: text overlap with arXiv:2306.13546 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cognitive maps play a crucial role in facilitating flexible behaviour by
representing spatial and conceptual relationships within an environment. The
ability to learn and infer the underlying structure of the environment is
crucial for effective exploration and navigation. This paper introduces a
hierarchical active inference model addressing the challenge of inferring
structure in the world from pixel-based observations. We propose a three-layer
hierarchical model consisting of a cognitive map, an allocentric, and an
egocentric world model, combining curiosity-driven exploration with
goal-oriented behaviour at the different levels of reasoning from context to
place to motion. This allows for efficient exploration and goal-directed search
in room-structured mini-grid environments.
| [
{
"version": "v1",
"created": "Mon, 18 Sep 2023 15:24:55 GMT"
}
] | 1,695,168,000,000 | [
[
"de Tinguy",
"Daria",
""
],
[
"Van de Maele",
"Toon",
""
],
[
"Verbelen",
"Tim",
""
],
[
"Dhoedt",
"Bart",
""
]
] |
2309.09898 | Simon Hosemann | Maurice Funk, Simon Hosemann, Jean Christoph Jung, Carsten Lutz | Towards Ontology Construction with Language Models | KBC-LM'23: Knowledge Base Construction from Pre-trained Language
Models workshop at ISWC 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We present a method for automatically constructing a concept hierarchy for a
given domain by querying a large language model. We apply this method to
various domains using OpenAI's GPT 3.5. Our experiments indicate that LLMs can
be of considerable help for constructing concept hierarchies.
| [
{
"version": "v1",
"created": "Mon, 18 Sep 2023 16:02:39 GMT"
}
] | 1,695,081,600,000 | [
[
"Funk",
"Maurice",
""
],
[
"Hosemann",
"Simon",
""
],
[
"Jung",
"Jean Christoph",
""
],
[
"Lutz",
"Carsten",
""
]
] |
2309.09901 | Sara Colantonio Ph.D. | Gianluca Carloni, Andrea Berti, Sara Colantonio | The role of causality in explainable artificial intelligence | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Causality and eXplainable Artificial Intelligence (XAI) have developed as
separate fields in computer science, even though the underlying concepts of
causation and explanation share common ancient roots. This is further enforced
by the lack of review works jointly covering these two fields. In this paper,
we investigate the literature to try to understand how and to what extent
causality and XAI are intertwined. More precisely, we seek to uncover what
kinds of relationships exist between the two concepts and how one can benefit
from them, for instance, in building trust in AI systems. As a result, three
main perspectives are identified. In the first one, the lack of causality is
seen as one of the major limitations of current AI and XAI approaches, and the
"optimal" form of explanations is investigated. The second is a pragmatic
perspective and considers XAI as a tool to foster scientific exploration for
causal inquiry, via the identification of pursue-worthy experimental
manipulations. Finally, the third perspective supports the idea that causality
is propaedeutic to XAI in three possible manners: exploiting concepts borrowed
from causality to support or improve XAI, utilizing counterfactuals for
explainability, and considering accessing a causal model as explaining itself.
To complement our analysis, we also provide relevant software solutions used to
automate causal tasks. We believe our work provides a unified view of the two
fields of causality and XAI by highlighting potential domain bridges and
uncovering possible limitations.
| [
{
"version": "v1",
"created": "Mon, 18 Sep 2023 16:05:07 GMT"
}
] | 1,695,081,600,000 | [
[
"Carloni",
"Gianluca",
""
],
[
"Berti",
"Andrea",
""
],
[
"Colantonio",
"Sara",
""
]
] |
2309.10129 | Haochen Zhang | Haochen Zhang and Xi Chen and Lin F. Yang | Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement
Learning | 15 pages, 5 figures, 9 tables, submitted to Financial Cryptography
and Data Security 2024 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decentralized exchanges (DEXs) are a cornerstone of decentralized finance
(DeFi), allowing users to trade cryptocurrencies without the need for
third-party authorization. Investors are incentivized to deposit assets into
liquidity pools, against which users can trade directly, while paying fees to
liquidity providers (LPs). However, a number of unresolved issues related to
capital efficiency and market risk hinder DeFi's further development. Uniswap
V3, a leading and groundbreaking DEX project, addresses capital efficiency by
enabling LPs to concentrate their liquidity within specific price ranges for
deposited assets. Nevertheless, this approach exacerbates market risk, as LPs
earn trading fees only when asset prices are within these predetermined
brackets. To mitigate this issue, this paper introduces a deep reinforcement
learning (DRL) solution designed to adaptively adjust these price ranges,
maximizing profits and mitigating market risks. Our approach also neutralizes
price-change risks by hedging the liquidity position through a rebalancing
portfolio in a centralized futures exchange. The DRL policy aims to optimize
trading fees earned by LPs against associated costs, such as gas fees and
hedging expenses, which is referred to as loss-versus-rebalancing (LVR). Using
simulations with a profit-and-loss (PnL) benchmark, our method demonstrates
superior performance in ETH/USDC and ETH/USDT pools compared to existing
baselines. We believe that this strategy not only offers investors a valuable
asset management tool but also introduces a new incentive mechanism for DEX
designers.
| [
{
"version": "v1",
"created": "Mon, 18 Sep 2023 20:10:28 GMT"
}
] | 1,695,168,000,000 | [
[
"Zhang",
"Haochen",
""
],
[
"Chen",
"Xi",
""
],
[
"Yang",
"Lin F.",
""
]
] |
2309.10209 | Chen Zhao | Haoliang Wang, Chen Zhao, Yunhui Guo, Kai Jiang, Feng Chen | Towards Effective Semantic OOD Detection in Unseen Domains: A Domain
Generalization Perspective | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Two prevalent types of distributional shifts in machine learning are the
covariate shift (as observed across different domains) and the semantic shift
(as seen across different classes). Traditional OOD detection techniques
typically address only one of these shifts. However, real-world testing
environments often present a combination of both covariate and semantic shifts.
In this study, we introduce a novel problem, semantic OOD detection across
domains, which simultaneously addresses both distributional shifts. To this
end, we introduce two regularization strategies: domain generalization
regularization, which ensures semantic invariance across domains to counteract
the covariate shift, and OOD detection regularization, designed to enhance OOD
detection capabilities against the semantic shift through energy bounding.
Through rigorous testing on three standard domain generalization benchmarks,
our proposed framework showcases its superiority over conventional domain
generalization approaches in terms of OOD detection performance. Moreover, it
holds its ground by maintaining comparable InD classification accuracy.
| [
{
"version": "v1",
"created": "Mon, 18 Sep 2023 23:48:22 GMT"
}
] | 1,695,168,000,000 | [
[
"Wang",
"Haoliang",
""
],
[
"Zhao",
"Chen",
""
],
[
"Guo",
"Yunhui",
""
],
[
"Jiang",
"Kai",
""
],
[
"Chen",
"Feng",
""
]
] |
2309.10216 | Shili Sheng | Shili Sheng, David Parker and Lu Feng | Safe POMDP Online Planning via Shielding | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Partially observable Markov decision processes (POMDPs) have been widely used
in many robotic applications for sequential decision-making under uncertainty.
POMDP online planning algorithms such as Partially Observable Monte-Carlo
Planning (POMCP) can solve very large POMDPs with the goal of maximizing the
expected return. But the resulting policies cannot provide safety guarantees
which are imperative for real-world safety-critical tasks (e.g., autonomous
driving). In this work, we consider safety requirements represented as
almost-sure reach-avoid specifications (i.e., the probability to reach a set of
goal states is one and the probability to reach a set of unsafe states is
zero). We compute shields that restrict unsafe actions which would violate the
almost-sure reach-avoid specifications. We then integrate these shields into
the POMCP algorithm for safe POMDP online planning. We propose four distinct
shielding methods, differing in how the shields are computed and integrated,
including factored variants designed to improve scalability. Experimental
results on a set of benchmark domains demonstrate that the proposed shielding
methods successfully guarantee safety (unlike the baseline POMCP without
shielding) on large POMDPs, with negligible impact on the runtime for online
planning.
| [
{
"version": "v1",
"created": "Tue, 19 Sep 2023 00:02:05 GMT"
},
{
"version": "v2",
"created": "Sat, 2 Mar 2024 15:49:21 GMT"
}
] | 1,709,596,800,000 | [
[
"Sheng",
"Shili",
""
],
[
"Parker",
"David",
""
],
[
"Feng",
"Lu",
""
]
] |
2309.10253 | Jiahao Yu | Jiahao Yu, Xingwei Lin, Zheng Yu, Xinyu Xing | GPTFUZZER: Red Teaming Large Language Models with Auto-Generated
Jailbreak Prompts | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Large language models (LLMs) have recently experienced tremendous popularity
and are widely used from casual conversations to AI-driven programming.
However, despite their considerable success, LLMs are not entirely reliable and
can give detailed guidance on how to conduct harmful or illegal activities.
While safety measures can reduce the risk of such outputs, adversarial
jailbreak attacks can still exploit LLMs to produce harmful content. These
jailbreak templates are typically manually crafted, making large-scale testing
challenging.
In this paper, we introduce GPTFuzz, a novel black-box jailbreak fuzzing
framework inspired by the AFL fuzzing framework. Instead of manual engineering,
GPTFuzz automates the generation of jailbreak templates for red-teaming LLMs.
At its core, GPTFuzz starts with human-written templates as initial seeds, then
mutates them to produce new templates. We detail three key components of
GPTFuzz: a seed selection strategy for balancing efficiency and variability,
mutate operators for creating semantically equivalent or similar sentences, and
a judgment model to assess the success of a jailbreak attack.
We evaluate GPTFuzz against various commercial and open-source LLMs,
including ChatGPT, LLaMa-2, and Vicuna, under diverse attack scenarios. Our
results indicate that GPTFuzz consistently produces jailbreak templates with a
high success rate, surpassing human-crafted templates. Remarkably, GPTFuzz
achieves over 90% attack success rates against ChatGPT and Llama-2 models, even
with suboptimal initial seed templates. We anticipate that GPTFuzz will be
instrumental for researchers and practitioners in examining LLM robustness and
will encourage further exploration into enhancing LLM safety.
| [
{
"version": "v1",
"created": "Tue, 19 Sep 2023 02:19:48 GMT"
},
{
"version": "v2",
"created": "Wed, 4 Oct 2023 06:15:12 GMT"
}
] | 1,696,464,000,000 | [
[
"Yu",
"Jiahao",
""
],
[
"Lin",
"Xingwei",
""
],
[
"Yu",
"Zheng",
""
],
[
"Xing",
"Xinyu",
""
]
] |
2309.10293 | Thanveer Shaik Mr | Thanveer Shaik, Xiaohui Tao, Haoran Xie, Lin Li, Juan D. Velasquez,
Niall Higgins | QXAI: Explainable AI Framework for Quantitative Analysis in Patient
Monitoring Systems | This work has been submitted to the ELSEVIER for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessible | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Artificial Intelligence techniques can be used to classify a patient's
physical activities and predict vital signs for remote patient monitoring.
Regression analysis based on non-linear models like deep learning models has
limited explainability due to its black-box nature. This can require
decision-makers to make blind leaps of faith based on non-linear model results,
especially in healthcare applications. In non-invasive monitoring, patient data
from tracking sensors and their predisposing clinical attributes act as input
features for predicting future vital signs. Explaining the contributions of
various features to the overall output of the monitoring application is
critical for a clinician's decision-making. In this study, an Explainable AI
for Quantitative analysis (QXAI) framework is proposed with post-hoc model
explainability and intrinsic explainability for regression and classification
tasks in a supervised learning approach. This was achieved by utilizing the
Shapley values concept and incorporating attention mechanisms in deep learning
models. We adopted the artificial neural networks (ANN) and attention-based
Bidirectional LSTM (BiLSTM) models for the prediction of heart rate and
classification of physical activities based on sensor data. The deep learning
models achieved state-of-the-art results in both prediction and classification
tasks. Global explanation and local explanation were conducted on input data to
understand the feature contribution of various patient data. The proposed QXAI
framework was evaluated using PPG-DaLiA data to predict heart rate and mobile
health (MHEALTH) data to classify physical activities based on sensor data.
Monte Carlo approximation was applied to the framework to overcome the time
complexity and high computation power requirements required for Shapley value
calculations.
| [
{
"version": "v1",
"created": "Tue, 19 Sep 2023 03:50:30 GMT"
},
{
"version": "v2",
"created": "Wed, 20 Sep 2023 03:02:24 GMT"
},
{
"version": "v3",
"created": "Fri, 2 Feb 2024 08:07:40 GMT"
}
] | 1,707,091,200,000 | [
[
"Shaik",
"Thanveer",
""
],
[
"Tao",
"Xiaohui",
""
],
[
"Xie",
"Haoran",
""
],
[
"Li",
"Lin",
""
],
[
"Velasquez",
"Juan D.",
""
],
[
"Higgins",
"Niall",
""
]
] |
2309.10324 | Pooja Singhal | Bliss Singhal, Fnu Pooja | Metastatic Breast Cancer Prognostication Through Multimodal Integration
of Dimensionality Reduction Algorithms and Classification Algorithms | 10 pages, 14 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Machine learning (ML) is a branch of Artificial Intelligence (AI) where
computers analyze data and find patterns in the data. The study focuses on the
detection of metastatic cancer using ML. Metastatic cancer is the point where
the cancer has spread to other parts of the body and is the cause of
approximately 90% of cancer related deaths. Normally, pathologists spend hours
each day to manually classify whether tumors are benign or malignant. This
tedious task contributes to mislabeling metastasis being over 60% of time and
emphasizes the importance to be aware of human error, and other inefficiencies.
ML is a good candidate to improve the correct identification of metastatic
cancer saving thousands of lives and can also improve the speed and efficiency
of the process thereby taking less resources and time. So far, deep learning
methodology of AI has been used in the research to detect cancer. This study is
a novel approach to determine the potential of using preprocessing algorithms
combined with classification algorithms in detecting metastatic cancer. The
study used two preprocessing algorithms: principal component analysis (PCA) and
the genetic algorithm to reduce the dimensionality of the dataset, and then
used three classification algorithms: logistic regression, decision tree
classifier, and k-nearest neighbors to detect metastatic cancer in the
pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline
comprising of PCA, the genetic algorithm, and the k-nearest neighbors
algorithm, suggesting that preprocessing and classification algorithms have
great potential for detecting metastatic cancer.
| [
{
"version": "v1",
"created": "Tue, 19 Sep 2023 05:12:02 GMT"
}
] | 1,695,168,000,000 | [
[
"Singhal",
"Bliss",
""
],
[
"Pooja",
"Fnu",
""
]
] |
2309.10371 | Benjamin Goertzel | Ben Goertzel | Generative AI vs. AGI: The Cognitive Strengths and Weaknesses of Modern
LLMs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A moderately detailed consideration of interactive LLMs as cognitive systems
is given, focusing on LLMs circa mid-2023 such as ChatGPT, GPT-4, Bard, Llama,
etc.. Cognitive strengths of these systems are reviewed, and then careful
attention is paid to the substantial differences between the sort of cognitive
system these LLMs are, and the sort of cognitive systems human beings are. It
is found that many of the practical weaknesses of these AI systems can be tied
specifically to lacks in the basic cognitive architectures according to which
these systems are built. It is argued that incremental improvement of such LLMs
is not a viable approach to working toward human-level AGI, in practical terms
given realizable amounts of compute resources. This does not imply there is
nothing to learn about human-level AGI from studying and experimenting with
LLMs, nor that LLMs cannot form significant parts of human-level AGI
architectures that also incorporate other ideas. Social and ethical matters
regarding LLMs are very briefly touched from this perspective, which implies
that while care should be taken regarding misinformation and other issues, and
economic upheavals will need their own social remedies based on their
unpredictable course as with any powerfully impactful technology, overall the
sort of policy needed as regards modern LLMs is quite different than would be
the case if a more credible approximation to human-level AGI were at hand.
| [
{
"version": "v1",
"created": "Tue, 19 Sep 2023 07:12:55 GMT"
}
] | 1,695,168,000,000 | [
[
"Goertzel",
"Ben",
""
]
] |
2309.10424 | Vicent Blanes-Selva | Juan M. Garc\'ia-G\'omez, Vicent Blanes-Selva, Jos\'e Carlos de
Bartolom\'e Cenzano, Jaime Cebolla-Cornejo and Ascensi\'on
Do\~nate-Mart\'inez | Functional requirements to mitigate the Risk of Harm to Patients from
Artificial Intelligence in Healthcare | 14 pages, 1 figure, 1 table | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The Directorate General for Parliamentary Research Services of the European
Parliament has prepared a report to the Members of the European Parliament
where they enumerate seven main risks of Artificial Intelligence (AI) in
medicine and healthcare: patient harm due to AI errors, misuse of medical AI
tools, bias in AI and the perpetuation of existing inequities, lack of
transparency, privacy and security issues, gaps in accountability, and
obstacles in implementation.
In this study, we propose fourteen functional requirements that AI systems
may implement to reduce the risks associated with their medical purpose: AI
passport, User management, Regulation check, Academic use only disclaimer, data
quality assessment, Clinicians double check, Continuous performance evaluation,
Audit trail, Continuous usability test, Review of retrospective/simulated
cases, Bias check, eXplainable AI, Encryption and use of field-tested
libraries, and Semantic interoperability.
Our intention here is to provide specific high-level specifications of
technical solutions to ensure continuous good performance and use of AI systems
to benefit patients in compliance with the future EU regulatory framework.
| [
{
"version": "v1",
"created": "Tue, 19 Sep 2023 08:37:22 GMT"
}
] | 1,695,168,000,000 | [
[
"García-Gómez",
"Juan M.",
""
],
[
"Blanes-Selva",
"Vicent",
""
],
[
"Cenzano",
"José Carlos de Bartolomé",
""
],
[
"Cebolla-Cornejo",
"Jaime",
""
],
[
"Doñate-Martínez",
"Ascensión",
""
]
] |
2309.10532 | Yuan Yang | Yuan Yang, Deepayan Sanyal, James Ainooson, Joel Michelson, Effat
Farhana, Maithilee Kunda | A Cognitively-Inspired Neural Architecture for Visual Abstract Reasoning
Using Contrastive Perceptual and Conceptual Processing | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We introduce a new neural architecture for solving visual abstract reasoning
tasks inspired by human cognition, specifically by observations that human
abstract reasoning often interleaves perceptual and conceptual processing as
part of a flexible, iterative, and dynamic cognitive process. Inspired by this
principle, our architecture models visual abstract reasoning as an iterative,
self-contrasting learning process that pursues consistency between perceptual
and conceptual processing of visual stimuli. We explain how this new
Contrastive Perceptual-Conceptual Network (CPCNet) works using matrix reasoning
problems in the style of the well-known Raven's Progressive Matrices
intelligence test. Experiments on the machine learning dataset RAVEN show that
CPCNet achieves higher accuracy than all previously published models while also
using the weakest inductive bias. We also point out a substantial and
previously unremarked class imbalance in the original RAVEN dataset, and we
propose a new variant of RAVEN -- AB-RAVEN -- that is more balanced in terms of
abstract concepts.
| [
{
"version": "v1",
"created": "Tue, 19 Sep 2023 11:18:01 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Sep 2023 01:42:46 GMT"
},
{
"version": "v3",
"created": "Fri, 20 Oct 2023 09:02:22 GMT"
}
] | 1,698,019,200,000 | [
[
"Yang",
"Yuan",
""
],
[
"Sanyal",
"Deepayan",
""
],
[
"Ainooson",
"James",
""
],
[
"Michelson",
"Joel",
""
],
[
"Farhana",
"Effat",
""
],
[
"Kunda",
"Maithilee",
""
]
] |
2309.10737 | Tuan Dam | Tuan Dam, Pascal Stenger, Lukas Schneider, Joni Pajarinen, Carlo
D'Eramo, Odalric-Ambrym Maillard | Monte-Carlo tree search with uncertainty propagation via optimal
transport | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces a novel backup strategy for Monte-Carlo Tree Search
(MCTS) designed for highly stochastic and partially observable Markov decision
processes. We adopt a probabilistic approach, modeling both value and
action-value nodes as Gaussian distributions. We introduce a novel backup
operator that computes value nodes as the Wasserstein barycenter of their
action-value children nodes; thus, propagating the uncertainty of the estimate
across the tree to the root node. We study our novel backup operator when using
a novel combination of $L^1$-Wasserstein barycenter with $\alpha$-divergence,
by drawing a notable connection to the generalized mean backup operator. We
complement our probabilistic backup operator with two sampling strategies,
based on optimistic selection and Thompson sampling, obtaining our Wasserstein
MCTS algorithm. We provide theoretical guarantees of asymptotic convergence to
the optimal policy, and an empirical evaluation on several stochastic and
partially observable environments, where our approach outperforms well-known
related baselines.
| [
{
"version": "v1",
"created": "Tue, 19 Sep 2023 16:32:04 GMT"
}
] | 1,695,168,000,000 | [
[
"Dam",
"Tuan",
""
],
[
"Stenger",
"Pascal",
""
],
[
"Schneider",
"Lukas",
""
],
[
"Pajarinen",
"Joni",
""
],
[
"D'Eramo",
"Carlo",
""
],
[
"Maillard",
"Odalric-Ambrym",
""
]
] |
2309.10871 | Arthur Van Der Staaij | Arthur van der Staaij, Jelmer Prins, Vincent L. Prins, Julian Poelsma,
Thera Smit, Matthias M\"uller-Brockhausen, Mike Preuss | Believable Minecraft Settlements by Means of Decentralised Iterative
Planning | 8 pages, 8 figures, to be published in "2023 IEEE Conference on Games
(CoG)" | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Procedural city generation that focuses on believability and adaptability to
random terrain is a difficult challenge in the field of Procedural Content
Generation (PCG). Dozens of researchers compete for a realistic approach in
challenges such as the Generative Settlement Design in Minecraft (GDMC), in
which our method has won the 2022 competition. This was achieved through a
decentralised, iterative planning process that is transferable to similar
generation processes that aims to produce "organic" content procedurally.
| [
{
"version": "v1",
"created": "Tue, 19 Sep 2023 18:32:49 GMT"
}
] | 1,695,254,400,000 | [
[
"van der Staaij",
"Arthur",
""
],
[
"Prins",
"Jelmer",
""
],
[
"Prins",
"Vincent L.",
""
],
[
"Poelsma",
"Julian",
""
],
[
"Smit",
"Thera",
""
],
[
"Müller-Brockhausen",
"Matthias",
""
],
[
"Preuss",
"Mike",
""
]
] |
2309.10982 | Bingzhe Wu | Bingzhe Wu | Is GPT4 a Good Trader? | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, large language models (LLMs), particularly GPT-4, have demonstrated
significant capabilities in various planning and reasoning tasks
\cite{cheng2023gpt4,bubeck2023sparks}. Motivated by these advancements, there
has been a surge of interest among researchers to harness the capabilities of
GPT-4 for the automated design of quantitative factors that do not overlap with
existing factor libraries, with an aspiration to achieve alpha returns
\cite{webpagequant}. In contrast to these work, this study aims to examine the
fidelity of GPT-4's comprehension of classic trading theories and its
proficiency in applying its code interpreter abilities to real-world trading
data analysis. Such an exploration is instrumental in discerning whether the
underlying logic GPT-4 employs for trading is intrinsically reliable.
Furthermore, given the acknowledged interpretative latitude inherent in most
trading theories, we seek to distill more precise methodologies of deploying
these theories from GPT-4's analytical process, potentially offering invaluable
insights to human traders.
To achieve this objective, we selected daily candlestick (K-line) data from
specific periods for certain assets, such as the Shanghai Stock Index. Through
meticulous prompt engineering, we guided GPT-4 to analyze the technical
structures embedded within this data, based on specific theories like the
Elliott Wave Theory. We then subjected its analytical output to manual
evaluation, assessing its interpretative depth and accuracy vis-\`a-vis these
trading theories from multiple dimensions. The results and findings from this
study could pave the way for a synergistic amalgamation of human expertise and
AI-driven insights in the realm of trading.
| [
{
"version": "v1",
"created": "Wed, 20 Sep 2023 00:47:52 GMT"
}
] | 1,695,254,400,000 | [
[
"Wu",
"Bingzhe",
""
]
] |
2309.11064 | Vipula Rawte | Vipula Rawte, Prachi Priya, S.M Towhidul Islam Tonmoy, S M Mehedi
Zaman, Amit Sheth, Amitava Das | Exploring the Relationship between LLM Hallucinations and Prompt
Linguistic Nuances: Readability, Formality, and Concreteness | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | As Large Language Models (LLMs) have advanced, they have brought forth new
challenges, with one of the prominent issues being LLM hallucination. While
various mitigation techniques are emerging to address hallucination, it is
equally crucial to delve into its underlying causes. Consequently, in this
preliminary exploratory investigation, we examine how linguistic factors in
prompts, specifically readability, formality, and concreteness, influence the
occurrence of hallucinations. Our experimental results suggest that prompts
characterized by greater formality and concreteness tend to result in reduced
hallucination. However, the outcomes pertaining to readability are somewhat
inconclusive, showing a mixed pattern.
| [
{
"version": "v1",
"created": "Wed, 20 Sep 2023 05:04:16 GMT"
}
] | 1,695,254,400,000 | [
[
"Rawte",
"Vipula",
""
],
[
"Priya",
"Prachi",
""
],
[
"Tonmoy",
"S. M Towhidul Islam",
""
],
[
"Zaman",
"S M Mehedi",
""
],
[
"Sheth",
"Amit",
""
],
[
"Das",
"Amitava",
""
]
] |
2309.11155 | Merel De Leeuw Den Bouter | Merel de Leeuw den Bouter, Javier Lloret Pardo, Zeno Geradts, Marcel
Worring | ProtoExplorer: Interpretable Forensic Analysis of Deepfake Videos using
Prototype Exploration and Refinement | 15 pages, 6 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In high-stakes settings, Machine Learning models that can provide predictions
that are interpretable for humans are crucial. This is even more true with the
advent of complex deep learning based models with a huge number of tunable
parameters. Recently, prototype-based methods have emerged as a promising
approach to make deep learning interpretable. We particularly focus on the
analysis of deepfake videos in a forensics context. Although prototype-based
methods have been introduced for the detection of deepfake videos, their use in
real-world scenarios still presents major challenges, in that prototypes tend
to be overly similar and interpretability varies between prototypes. This paper
proposes a Visual Analytics process model for prototype learning, and, based on
this, presents ProtoExplorer, a Visual Analytics system for the exploration and
refinement of prototype-based deepfake detection models. ProtoExplorer offers
tools for visualizing and temporally filtering prototype-based predictions when
working with video data. It disentangles the complexity of working with
spatio-temporal prototypes, facilitating their visualization. It further
enables the refinement of models by interactively deleting and replacing
prototypes with the aim to achieve more interpretable and less biased
predictions while preserving detection accuracy. The system was designed with
forensic experts and evaluated in a number of rounds based on both open-ended
think aloud evaluation and interviews. These sessions have confirmed the
strength of our prototype based exploration of deepfake videos while they
provided the feedback needed to continuously improve the system.
| [
{
"version": "v1",
"created": "Wed, 20 Sep 2023 09:03:56 GMT"
}
] | 1,695,254,400,000 | [
[
"Bouter",
"Merel de Leeuw den",
""
],
[
"Pardo",
"Javier Lloret",
""
],
[
"Geradts",
"Zeno",
""
],
[
"Worring",
"Marcel",
""
]
] |
2309.11202 | Uduak Uboh | Uduak Uboh | Using Artificial Intelligence for the Automation of Knitting Patterns | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Knitting patterns are a crucial component in the creation and design of
knitted materials. Traditionally, these patterns were taught informally, but
thanks to advancements in technology, anyone interested in knitting can use the
patterns as a guide to start knitting. Perhaps because knitting is mostly a
hobby, with the exception of industrial manufacturing utilising specialised
knitting machines, the use of Al in knitting is less widespread than its
application in other fields. However, it is important to determine whether
knitted pattern classification using an automated system is viable. In order to
recognise and classify knitting patterns. Using data augmentation and a
transfer learning technique, this study proposes a deep learning model. The
Inception ResNet-V2 is the main feature extraction and classification algorithm
used in the model. Metrics like accuracy, logarithmic loss, F1-score,
precision, and recall score were used to evaluate the model. The model
evaluation's findings demonstrate high model accuracy, precision, recall, and
F1 score. In addition, the AUC score for majority of the classes was in the
range (0.7-0.9). A comparative analysis was done using other pretrained models
and a ResNet-50 model with transfer learning and the proposed model evaluation
results surpassed all others. The major limitation for this project is time, as
with more time, there might have been better accuracy over a larger number of
epochs.
| [
{
"version": "v1",
"created": "Wed, 20 Sep 2023 10:38:08 GMT"
}
] | 1,695,254,400,000 | [
[
"Uboh",
"Uduak",
""
]
] |
2309.11224 | Nardine Osman | Nardine Osman and Bruno Rosell i Gui and Carles Sierra | Leveraging Diversity in Online Interactions | null | Workshops at the Second International Conference on Hybrid
Human-Artificial Intelligence (HHAI-WS 2023), June 26-27, 2023, Munich,
Germany | null | https://ceur-ws.org/Vol-3456/short5-9.pdf | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This paper addresses the issue of connecting people online to help them find
support with their day-to-day problems. We make use of declarative norms for
mediating online interactions, and we specifically focus on the issue of
leveraging diversity when connecting people. We run pilots at different
university sites, and the results show relative success in the diversity of the
selected profiles, backed by high user satisfaction.
| [
{
"version": "v1",
"created": "Wed, 20 Sep 2023 11:28:39 GMT"
}
] | 1,707,350,400,000 | [
[
"Osman",
"Nardine",
""
],
[
"Gui",
"Bruno Rosell i",
""
],
[
"Sierra",
"Carles",
""
]
] |
2309.11231 | Jonnathan Berrezueta-Guzman | Jonnathan Berrezueta-Guzman, Laura Malache-Silva and Stephan Krusche | ChatGPT-4 as a Tool for Reviewing Academic Books in Spanish | Preprint. Paper accepted in the 18\textsuperscript{th} Latin American
Conference on Learning Technologies (LACLO 2023), 14 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This study evaluates the potential of ChatGPT-4, an artificial intelligence
language model developed by OpenAI, as an editing tool for Spanish literary and
academic books. The need for efficient and accessible reviewing and editing
processes in the publishing industry has driven the search for automated
solutions. ChatGPT-4, being one of the most advanced language models, offers
notable capabilities in text comprehension and generation. In this study, the
features and capabilities of ChatGPT-4 are analyzed in terms of grammatical
correction, stylistic coherence, and linguistic enrichment of texts in Spanish.
Tests were conducted with 100 literary and academic texts, where the edits made
by ChatGPT-4 were compared to those made by expert human reviewers and editors.
The results show that while ChatGPT-4 is capable of making grammatical and
orthographic corrections with high accuracy and in a very short time, it still
faces challenges in areas such as context sensitivity, bibliometric analysis,
deep contextual understanding, and interaction with visual content like graphs
and tables. However, it is observed that collaboration between ChatGPT-4 and
human reviewers and editors can be a promising strategy for improving
efficiency without compromising quality. Furthermore, the authors consider that
ChatGPT-4 represents a valuable tool in the editing process, but its use should
be complementary to the work of human editors to ensure high-caliber editing in
Spanish literary and academic books.
| [
{
"version": "v1",
"created": "Wed, 20 Sep 2023 11:44:45 GMT"
}
] | 1,695,254,400,000 | [
[
"Berrezueta-Guzman",
"Jonnathan",
""
],
[
"Malache-Silva",
"Laura",
""
],
[
"Krusche",
"Stephan",
""
]
] |
2309.11236 | Malte Luttermann | Malte Luttermann, Tanya Braun, Ralf M\"oller, Marcel Gehrke | Colour Passing Revisited: Lifted Model Construction with Commutative
Factors | Extended version of paper accepted to the Proceedings of the 38th
AAAI Conference on Artificial Intelligence (AAAI-2024) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Lifted probabilistic inference exploits symmetries in a probabilistic model
to allow for tractable probabilistic inference with respect to domain sizes. To
apply lifted inference, a lifted representation has to be obtained, and to do
so, the so-called colour passing algorithm is the state of the art. The colour
passing algorithm, however, is bound to a specific inference algorithm and we
found that it ignores commutativity of factors while constructing a lifted
representation. We contribute a modified version of the colour passing
algorithm that uses logical variables to construct a lifted representation
independent of a specific inference algorithm while at the same time exploiting
commutativity of factors during an offline-step. Our proposed algorithm
efficiently detects more symmetries than the state of the art and thereby
drastically increases compression, yielding significantly faster online query
times for probabilistic inference when the resulting model is applied.
| [
{
"version": "v1",
"created": "Wed, 20 Sep 2023 11:57:19 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Dec 2023 15:28:09 GMT"
}
] | 1,702,857,600,000 | [
[
"Luttermann",
"Malte",
""
],
[
"Braun",
"Tanya",
""
],
[
"Möller",
"Ralf",
""
],
[
"Gehrke",
"Marcel",
""
]
] |
2309.11274 | Bestoun Ahmed Dr. | Manal Rahal and Bestoun S. Ahmed and Jorgen Samuelsson | Machine Learning Data Suitability and Performance Testing Using Fault
Injection Testing Framework | 18 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Creating resilient machine learning (ML) systems has become necessary to
ensure production-ready ML systems that acquire user confidence seamlessly. The
quality of the input data and the model highly influence the successful
end-to-end testing in data-sensitive systems. However, the testing approaches
of input data are not as systematic and are few compared to model testing. To
address this gap, this paper presents the Fault Injection for Undesirable
Learning in input Data (FIUL-Data) testing framework that tests the resilience
of ML models to multiple intentionally-triggered data faults. Data mutators
explore vulnerabilities of ML systems against the effects of different fault
injections. The proposed framework is designed based on three main ideas: The
mutators are not random; one data mutator is applied at an instance of time,
and the selected ML models are optimized beforehand. This paper evaluates the
FIUL-Data framework using data from analytical chemistry, comprising retention
time measurements of anti-sense oligonucleotide. Empirical evaluation is
carried out in a two-step process in which the responses of selected ML models
to data mutation are analyzed individually and then compared with each other.
The results show that the FIUL-Data framework allows the evaluation of the
resilience of ML models. In most experiments cases, ML models show higher
resilience at larger training datasets, where gradient boost performed better
than support vector regression in smaller training sets. Overall, the mean
squared error metric is useful in evaluating the resilience of models due to
its higher sensitivity to data mutation.
| [
{
"version": "v1",
"created": "Wed, 20 Sep 2023 12:58:35 GMT"
}
] | 1,695,254,400,000 | [
[
"Rahal",
"Manal",
""
],
[
"Ahmed",
"Bestoun S.",
""
],
[
"Samuelsson",
"Jorgen",
""
]
] |
2309.11284 | Ma Qian | Qian Ma, Zijian Zhang, Xiangyu Zhao, Haoliang Li, Hongwei Zhao, Yiqi
Wang, Zitao Liu, and Wanyu Wang | Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic
Forecasting | 9 pages, accepted by CIKM'23 | null | 10.1145/3583780.3614910 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the acceleration of urbanization, traffic forecasting has become an
essential role in smart city construction. In the context of spatio-temporal
prediction, the key lies in how to model the dependencies of sensors. However,
existing works basically only consider the micro relationships between sensors,
where the sensors are treated equally, and their macroscopic dependencies are
neglected. In this paper, we argue to rethink the sensor's dependency modeling
from two hierarchies: regional and global perspectives. Particularly, we merge
original sensors with high intra-region correlation as a region node to
preserve the inter-region dependency. Then, we generate representative and
common spatio-temporal patterns as global nodes to reflect a global dependency
between sensors and provide auxiliary information for spatio-temporal
dependency learning. In pursuit of the generality and reality of node
representations, we incorporate a Meta GCN to calibrate the regional and global
nodes in the physical data space. Furthermore, we devise the cross-hierarchy
graph convolution to propagate information from different hierarchies. In a
nutshell, we propose a Hierarchical Information Enhanced Spatio-Temporal
prediction method, HIEST, to create and utilize the regional dependency and
common spatio-temporal patterns. Extensive experiments have verified the
leading performance of our HIEST against state-of-the-art baselines. We
publicize the code to ease reproducibility.
| [
{
"version": "v1",
"created": "Wed, 20 Sep 2023 13:08:34 GMT"
}
] | 1,695,254,400,000 | [
[
"Ma",
"Qian",
""
],
[
"Zhang",
"Zijian",
""
],
[
"Zhao",
"Xiangyu",
""
],
[
"Li",
"Haoliang",
""
],
[
"Zhao",
"Hongwei",
""
],
[
"Wang",
"Yiqi",
""
],
[
"Liu",
"Zitao",
""
],
[
"Wang",
"Wanyu",
""
]
] |
2309.11356 | Ruwan Wickramarachchi | Chathurangi Shyalika, Ruwan Wickramarachchi, Amit Sheth | A Comprehensive Survey on Rare Event Prediction | 44 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Rare event prediction involves identifying and forecasting events with a low
probability using machine learning and data analysis. Due to the imbalanced
data distributions, where the frequency of common events vastly outweighs that
of rare events, it requires using specialized methods within each step of the
machine learning pipeline, i.e., from data processing to algorithms to
evaluation protocols. Predicting the occurrences of rare events is important
for real-world applications, such as Industry 4.0, and is an active research
area in statistical and machine learning. This paper comprehensively reviews
the current approaches for rare event prediction along four dimensions: rare
event data, data processing, algorithmic approaches, and evaluation approaches.
Specifically, we consider 73 datasets from different modalities (i.e.,
numerical, image, text, and audio), four major categories of data processing,
five major algorithmic groupings, and two broader evaluation approaches. This
paper aims to identify gaps in the current literature and highlight the
challenges of predicting rare events. It also suggests potential research
directions, which can help guide practitioners and researchers.
| [
{
"version": "v1",
"created": "Wed, 20 Sep 2023 14:36:57 GMT"
}
] | 1,695,254,400,000 | [
[
"Shyalika",
"Chathurangi",
""
],
[
"Wickramarachchi",
"Ruwan",
""
],
[
"Sheth",
"Amit",
""
]
] |
2309.11361 | Yuan An | Yuan An, Jane Greenberg, Alex Kalinowski, Xintong Zhao, Xiaohua Hu,
Fernando J. Uribe-Romo, Kyle Langlois, Jacob Furst, Diego A.
G\'omez-Gualdr\'on | Knowledge Graph Question Answering for Materials Science (KGQA4MAT):
Developing Natural Language Interface for Metal-Organic Frameworks Knowledge
Graph (MOF-KG) Using LLM | In 17th International Conference on Metadata and Semantics Research,
October 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We present a comprehensive benchmark dataset for Knowledge Graph Question
Answering in Materials Science (KGQA4MAT), with a focus on metal-organic
frameworks (MOFs). A knowledge graph for metal-organic frameworks (MOF-KG) has
been constructed by integrating structured databases and knowledge extracted
from the literature. To enhance MOF-KG accessibility for domain experts, we aim
to develop a natural language interface for querying the knowledge graph. We
have developed a benchmark comprised of 161 complex questions involving
comparison, aggregation, and complicated graph structures. Each question is
rephrased in three additional variations, resulting in 644 questions and 161 KG
queries. To evaluate the benchmark, we have developed a systematic approach for
utilizing the LLM, ChatGPT, to translate natural language questions into formal
KG queries. We also apply the approach to the well-known QALD-9 dataset,
demonstrating ChatGPT's potential in addressing KGQA issues for different
platforms and query languages. The benchmark and the proposed approach aim to
stimulate further research and development of user-friendly and efficient
interfaces for querying domain-specific materials science knowledge graphs,
thereby accelerating the discovery of novel materials.
| [
{
"version": "v1",
"created": "Wed, 20 Sep 2023 14:43:43 GMT"
},
{
"version": "v2",
"created": "Thu, 6 Jun 2024 15:35:09 GMT"
}
] | 1,717,718,400,000 | [
[
"An",
"Yuan",
""
],
[
"Greenberg",
"Jane",
""
],
[
"Kalinowski",
"Alex",
""
],
[
"Zhao",
"Xintong",
""
],
[
"Hu",
"Xiaohua",
""
],
[
"Uribe-Romo",
"Fernando J.",
""
],
[
"Langlois",
"Kyle",
""
],
[
"Furst",
"Jacob",
""
],
[
"Gómez-Gualdrón",
"Diego A.",
""
]
] |
2309.11469 | Zhaohong Deng | Qiongdan Lou, Zhaohong Deng, Zhiyong Xiao, Kup-Sze Choi, Shitong Wang | Multi-Label Takagi-Sugeno-Kang Fuzzy System | This work has been accepted by IEEE Transactions on Fuzzy Systems | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-label classification can effectively identify the relevant labels of an
instance from a given set of labels. However,the modeling of the relationship
between the features and the labels is critical to the classification
performance. To this end, we propose a new multi-label classification method,
called Multi-Label Takagi-Sugeno-Kang Fuzzy System (ML-TSK FS), to improve the
classification performance. The structure of ML-TSK FS is designed using fuzzy
rules to model the relationship between features and labels. The fuzzy system
is trained by integrating fuzzy inference based multi-label correlation
learning with multi-label regression loss. The proposed ML-TSK FS is evaluated
experimentally on 12 benchmark multi-label datasets. 1 The results show that
the performance of ML-TSK FS is competitive with existing methods in terms of
various evaluation metrics, indicating that it is able to model the
feature-label relationship effectively using fuzzy inference rules and enhances
the classification performance.
| [
{
"version": "v1",
"created": "Wed, 20 Sep 2023 17:09:09 GMT"
}
] | 1,695,254,400,000 | [
[
"Lou",
"Qiongdan",
""
],
[
"Deng",
"Zhaohong",
""
],
[
"Xiao",
"Zhiyong",
""
],
[
"Choi",
"Kup-Sze",
""
],
[
"Wang",
"Shitong",
""
]
] |
2309.11473 | Zhaohong Deng | Wei Zhang, Zhaohong Deng, Te Zhang, Kup-Sze Choi, Shitong Wang | Multi-view Fuzzy Representation Learning with Rules based Model | This work has been accepted by IEEE Transactions on Knowledge and
Data Engineering | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Unsupervised multi-view representation learning has been extensively studied
for mining multi-view data. However, some critical challenges remain. On the
one hand, the existing methods cannot explore multi-view data comprehensively
since they usually learn a common representation between views, given that
multi-view data contains both the common information between views and the
specific information within each view. On the other hand, to mine the nonlinear
relationship between data, kernel or neural network methods are commonly used
for multi-view representation learning. However, these methods are lacking in
interpretability. To this end, this paper proposes a new multi-view fuzzy
representation learning method based on the interpretable Takagi-Sugeno-Kang
(TSK) fuzzy system (MVRL_FS). The method realizes multi-view representation
learning from two aspects. First, multi-view data are transformed into a
high-dimensional fuzzy feature space, while the common information between
views and specific information of each view are explored simultaneously.
Second, a new regularization method based on L_(2,1)-norm regression is
proposed to mine the consistency information between views, while the geometric
structure of the data is preserved through the Laplacian graph. Finally,
extensive experiments on many benchmark multi-view datasets are conducted to
validate the superiority of the proposed method.
| [
{
"version": "v1",
"created": "Wed, 20 Sep 2023 17:13:15 GMT"
}
] | 1,695,254,400,000 | [
[
"Zhang",
"Wei",
""
],
[
"Deng",
"Zhaohong",
""
],
[
"Zhang",
"Te",
""
],
[
"Choi",
"Kup-Sze",
""
],
[
"Wang",
"Shitong",
""
]
] |
2309.11478 | Hanyi Wang | Yuqian Sun, Hanyi Wang, Pok Man Chan, Morteza Tabibi, Yan Zhang, Huan
Lu, Yuheng Chen, Chang Hee Lee, Ali Asadipour | Fictional Worlds, Real Connections: Developing Community Storytelling
Social Chatbots through LLMs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We address the integration of storytelling and Large Language Models (LLMs)
to develop engaging and believable Social Chatbots (SCs) in community settings.
Motivated by the potential of fictional characters to enhance social
interactions, we introduce Storytelling Social Chatbots (SSCs) and the concept
of story engineering to transform fictional game characters into "live" social
entities within player communities. Our story engineering process includes
three steps: (1) Character and story creation, defining the SC's personality
and worldview, (2) Presenting Live Stories to the Community, allowing the SC to
recount challenges and seek suggestions, and (3) Communication with community
members, enabling interaction between the SC and users. We employed the LLM
GPT-3 to drive our SSC prototypes, "David" and "Catherine," and evaluated their
performance in an online gaming community, "DE (Alias)," on Discord. Our
mixed-method analysis, based on questionnaires (N=15) and interviews (N=8) with
community members, reveals that storytelling significantly enhances the
engagement and believability of SCs in community settings.
| [
{
"version": "v1",
"created": "Wed, 20 Sep 2023 17:23:05 GMT"
}
] | 1,695,254,400,000 | [
[
"Sun",
"Yuqian",
""
],
[
"Wang",
"Hanyi",
""
],
[
"Chan",
"Pok Man",
""
],
[
"Tabibi",
"Morteza",
""
],
[
"Zhang",
"Yan",
""
],
[
"Lu",
"Huan",
""
],
[
"Chen",
"Yuheng",
""
],
[
"Lee",
"Chang Hee",
""
],
[
"Asadipour",
"Ali",
""
]
] |
2309.11528 | Hanzhu Chen | Jie Wang, Hanzhu Chen, Qitan Lv, Zhihao Shi, Jiajun Chen, Huarui He,
Hongtao Xie, Yongdong Zhang, and Feng Wu | Learning Complete Topology-Aware Correlations Between Relations for
Inductive Link Prediction | arXiv admin note: text overlap with arXiv:2103.03642 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inductive link prediction -- where entities during training and inference
stages can be different -- has shown great potential for completing evolving
knowledge graphs in an entity-independent manner. Many popular methods mainly
focus on modeling graph-level features, while the edge-level interactions --
especially the semantic correlations between relations -- have been less
explored. However, we notice a desirable property of semantic correlations
between relations is that they are inherently edge-level and
entity-independent. This implies the great potential of the semantic
correlations for the entity-independent inductive link prediction task.
Inspired by this observation, we propose a novel subgraph-based method, namely
TACO, to model Topology-Aware COrrelations between relations that are highly
correlated to their topological structures within subgraphs. Specifically, we
prove that semantic correlations between any two relations can be categorized
into seven topological patterns, and then proposes Relational Correlation
Network (RCN) to learn the importance of each pattern. To further exploit the
potential of RCN, we propose Complete Common Neighbor induced subgraph that can
effectively preserve complete topological patterns within the subgraph.
Extensive experiments demonstrate that TACO effectively unifies the graph-level
information and edge-level interactions to jointly perform reasoning, leading
to a superior performance over existing state-of-the-art methods for the
inductive link prediction task.
| [
{
"version": "v1",
"created": "Wed, 20 Sep 2023 08:11:58 GMT"
},
{
"version": "v2",
"created": "Sun, 24 Mar 2024 10:27:10 GMT"
}
] | 1,711,411,200,000 | [
[
"Wang",
"Jie",
""
],
[
"Chen",
"Hanzhu",
""
],
[
"Lv",
"Qitan",
""
],
[
"Shi",
"Zhihao",
""
],
[
"Chen",
"Jiajun",
""
],
[
"He",
"Huarui",
""
],
[
"Xie",
"Hongtao",
""
],
[
"Zhang",
"Yongdong",
""
],
[
"Wu",
"Feng",
""
]
] |
2309.11608 | Ryan Turner | Daniel Kharitonov and Ryan Turner | Dataset Factory: A Toolchain For Generative Computer Vision Datasets | Presented at the datacomp.ai workshop at ICCV 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Generative AI workflows heavily rely on data-centric tasks - such as
filtering samples by annotation fields, vector distances, or scores produced by
custom classifiers. At the same time, computer vision datasets are quickly
approaching petabyte volumes, rendering data wrangling difficult. In addition,
the iterative nature of data preparation necessitates robust dataset sharing
and versioning mechanisms, both of which are hard to implement ad-hoc. To solve
these challenges, we propose a "dataset factory" approach that separates the
storage and processing of samples from metadata and enables data-centric
operations at scale for machine learning teams and individual researchers.
| [
{
"version": "v1",
"created": "Wed, 20 Sep 2023 19:43:37 GMT"
}
] | 1,695,340,800,000 | [
[
"Kharitonov",
"Daniel",
""
],
[
"Turner",
"Ryan",
""
]
] |
2309.11724 | Rui Liu | Rui Liu, Bin Liu, Haizhou Li | Emotion-Aware Prosodic Phrasing for Expressive Text-to-Speech | Submitted to ICASSP'2024 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Prosodic phrasing is crucial to the naturalness and intelligibility of
end-to-end Text-to-Speech (TTS). There exist both linguistic and emotional
prosody in natural speech. As the study of prosodic phrasing has been
linguistically motivated, prosodic phrasing for expressive emotion rendering
has not been well studied. In this paper, we propose an emotion-aware prosodic
phrasing model, termed \textit{EmoPP}, to mine the emotional cues of utterance
accurately and predict appropriate phrase breaks. We first conduct objective
observations on the ESD dataset to validate the strong correlation between
emotion and prosodic phrasing. Then the objective and subjective evaluations
show that the EmoPP outperforms all baselines and achieves remarkable
performance in terms of emotion expressiveness. The audio samples and the code
are available at \url{https://github.com/AI-S2-Lab/EmoPP}.
| [
{
"version": "v1",
"created": "Thu, 21 Sep 2023 01:51:10 GMT"
}
] | 1,695,340,800,000 | [
[
"Liu",
"Rui",
""
],
[
"Liu",
"Bin",
""
],
[
"Li",
"Haizhou",
""
]
] |
2309.11737 | Zhaoyi Hou | Zhaoyi Joey Hou, Li Zhang, Chris Callison-Burch | Choice-75: A Dataset on Decision Branching in Script Learning | To be published in LREC-COLING-2024 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Script learning studies how stereotypical events unfold, enabling machines to
reason about narratives with implicit information. Previous works mostly
consider a script as a linear sequence of events while ignoring the potential
branches that arise due to people's circumstantial choices. We hence propose
Choice-75, the first benchmark that challenges intelligent systems to make
decisions given descriptive scenarios, containing 75 scripts and more than 600
scenarios. We also present preliminary results with current large language
models (LLM). Although they demonstrate overall decent performance, there is
still notable headroom in hard scenarios.
| [
{
"version": "v1",
"created": "Thu, 21 Sep 2023 02:23:44 GMT"
},
{
"version": "v2",
"created": "Mon, 18 Mar 2024 01:35:48 GMT"
}
] | 1,710,806,400,000 | [
[
"Hou",
"Zhaoyi Joey",
""
],
[
"Zhang",
"Li",
""
],
[
"Callison-Burch",
"Chris",
""
]
] |
2309.11753 | Zhourui Guo | Zhourui Guo, Meng Yao, Yang Yu, Qiyue Yin | Improve the efficiency of deep reinforcement learning through semantic
exploration guided by natural language | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement learning is a powerful technique for learning from trial and
error, but it often requires a large number of interactions to achieve good
performance. In some domains, such as sparse-reward tasks, an oracle that can
provide useful feedback or guidance to the agent during the learning process is
really of great importance. However, querying the oracle too frequently may be
costly or impractical, and the oracle may not always have a clear answer for
every situation. Therefore, we propose a novel method for interacting with the
oracle in a selective and efficient way, using a retrieval-based approach. We
assume that the interaction can be modeled as a sequence of templated questions
and answers, and that there is a large corpus of previous interactions
available. We use a neural network to encode the current state of the agent and
the oracle, and retrieve the most relevant question from the corpus to ask the
oracle. We then use the oracle's answer to update the agent's policy and value
function. We evaluate our method on an object manipulation task. We show that
our method can significantly improve the efficiency of RL by reducing the
number of interactions needed to reach a certain level of performance, compared
to baselines that do not use the oracle or use it in a naive way.
| [
{
"version": "v1",
"created": "Thu, 21 Sep 2023 03:25:35 GMT"
}
] | 1,695,340,800,000 | [
[
"Guo",
"Zhourui",
""
],
[
"Yao",
"Meng",
""
],
[
"Yu",
"Yang",
""
],
[
"Yin",
"Qiyue",
""
]
] |
2309.11805 | Preetam Ghosh | Preetam Ghosh, Vaishali Sadaphal | JobRecoGPT -- Explainable job recommendations using LLMs | 10 pages, 29 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In today's rapidly evolving job market, finding the right opportunity can be
a daunting challenge. With advancements in the field of AI, computers can now
recommend suitable jobs to candidates. However, the task of recommending jobs
is not same as recommending movies to viewers. Apart from must-have criteria,
like skills and experience, there are many subtle aspects to a job which can
decide if it is a good fit or not for a given candidate. Traditional approaches
can capture the quantifiable aspects of jobs and candidates, but a substantial
portion of the data that is present in unstructured form in the job
descriptions and resumes is lost in the process of conversion to structured
format. As of late, Large Language Models (LLMs) have taken over the AI field
by storm with extraordinary performance in fields where text-based data is
available. Inspired by the superior performance of LLMs, we leverage their
capability to understand natural language for capturing the information that
was previously getting lost during the conversion of unstructured data to
structured form. To this end, we compare performance of four different
approaches for job recommendations namely, (i) Content based deterministic,
(ii) LLM guided, (iii) LLM unguided, and (iv) Hybrid. In this study, we present
advantages and limitations of each method and evaluate their performance in
terms of time requirements.
| [
{
"version": "v1",
"created": "Thu, 21 Sep 2023 06:25:28 GMT"
}
] | 1,695,340,800,000 | [
[
"Ghosh",
"Preetam",
""
],
[
"Sadaphal",
"Vaishali",
""
]
] |
2309.11907 | Haoyu Wang | Haoyu Wang, Xin Yuan, Qinqing Ren | Learning to Recover for Safe Reinforcement Learning | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Safety controllers is widely used to achieve safe reinforcement learning.
Most methods that apply a safety controller are using handcrafted safety
constraints to construct the safety controller. However, when the environment
dynamics are sophisticated, handcrafted safety constraints become unavailable.
Therefore, it worth to research on constructing safety controllers by learning
algorithms. We propose a three-stage architecture for safe reinforcement
learning, namely TU-Recovery Architecture. A safety critic and a recovery
policy is learned before task training. They form a safety controller to ensure
safety in task training. Then a phenomenon induced by disagreement between task
policy and recovery policy, called adversarial phenomenon, which reduces
learning efficiency and model performance, is described. Auxiliary reward is
proposed to mitigate adversarial phenomenon, while help the task policy to
learn to recover from high-risk states. A series of experiments are conducted
in a robot navigation environment. Experiments demonstrate that TU-Recovery
outperforms unconstrained counterpart in both reward gaining and constraint
violations during task training, and auxiliary reward further improve
TU-Recovery in reward-to-cost ratio by significantly reduce constraint
violations.
| [
{
"version": "v1",
"created": "Thu, 21 Sep 2023 09:17:38 GMT"
}
] | 1,695,340,800,000 | [
[
"Wang",
"Haoyu",
""
],
[
"Yuan",
"Xin",
""
],
[
"Ren",
"Qinqing",
""
]
] |
2309.11937 | Helena L\"ofstr\"om HeLo | Helena L\"ofstr\"om | On the Definition of Appropriate Trust and the Tools that Come with it | 8 pages, 3 figures, Conference: ICDATA 2023 | 2023 Congress in Computer Science, Computer Engineering, & Applied
Computing (CSCE) | 10.1109/CSCE60160.2023.00256 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Evaluating the efficiency of human-AI interactions is challenging, including
subjective and objective quality aspects. With the focus on the human
experience of the explanations, evaluations of explanation methods have become
mostly subjective, making comparative evaluations almost impossible and highly
linked to the individual user. However, it is commonly agreed that one aspect
of explanation quality is how effectively the user can detect if the
predictions are trustworthy and correct, i.e., if the explanations can increase
the user's appropriate trust in the model. This paper starts with the
definitions of appropriate trust from the literature. It compares the
definitions with model performance evaluation, showing the strong similarities
between appropriate trust and model performance evaluation. The paper's main
contribution is a novel approach to evaluating appropriate trust by taking
advantage of the likenesses between definitions. The paper offers several
straightforward evaluation methods for different aspects of user performance,
including suggesting a method for measuring uncertainty and appropriate trust
in regression.
| [
{
"version": "v1",
"created": "Thu, 21 Sep 2023 09:52:06 GMT"
}
] | 1,715,299,200,000 | [
[
"Löfström",
"Helena",
""
]
] |
2309.11975 | John Burden | John Burden, Konstantinos Voudouris, Ryan Burnell, Danaja Rutar, Lucy
Cheke, Jos\'e Hern\'andez-Orallo | Inferring Capabilities from Task Performance with Bayesian Triangulation | 8 Pages + 14 pages of Appendices. 15 Figures. Submitted to AAAI 2024.
Preprint | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As machine learning models become more general, we need to characterise them
in richer, more meaningful ways. We describe a method to infer the cognitive
profile of a system from diverse experimental data. To do so, we introduce
measurement layouts that model how task-instance features interact with system
capabilities to affect performance. These features must be triangulated in
complex ways to be able to infer capabilities from non-populational data -- a
challenge for traditional psychometric and inferential tools. Using the
Bayesian probabilistic programming library PyMC, we infer different cognitive
profiles for agents in two scenarios: 68 actual contestants in the AnimalAI
Olympics and 30 synthetic agents for O-PIAAGETS, an object permanence battery.
We showcase the potential for capability-oriented evaluation.
| [
{
"version": "v1",
"created": "Thu, 21 Sep 2023 11:19:26 GMT"
}
] | 1,695,340,800,000 | [
[
"Burden",
"John",
""
],
[
"Voudouris",
"Konstantinos",
""
],
[
"Burnell",
"Ryan",
""
],
[
"Rutar",
"Danaja",
""
],
[
"Cheke",
"Lucy",
""
],
[
"Hernández-Orallo",
"José",
""
]
] |
2309.12113 | Feng Li | Feng Li, Yuqi Chai, Huan Yang, Pengfei Hu, Lingjie Duan | Incentivizing Massive Unknown Workers for Budget-Limited Crowdsensing:
From Off-Line and On-Line Perspectives | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | How to incentivize strategic workers using limited budget is a very
fundamental problem for crowdsensing systems; nevertheless, since the sensing
abilities of the workers may not always be known as prior knowledge due to the
diversities of their sensor devices and behaviors, it is difficult to properly
select and pay the unknown workers. Although the uncertainties of the workers
can be addressed by the standard Combinatorial Multi-Armed Bandit (CMAB)
framework in existing proposals through a trade-off between exploration and
exploitation, we may not have sufficient budget to enable the trade-off among
the individual workers, especially when the number of the workers is huge while
the budget is limited. Moreover, the standard CMAB usually assumes the workers
always stay in the system, whereas the workers may join in or depart from the
system over time, such that what we have learnt for an individual worker cannot
be applied after the worker leaves. To address the above challenging issues, in
this paper, we first propose an off-line Context-Aware CMAB-based Incentive
(CACI) mechanism. We innovate in leveraging the exploration-exploitation
trade-off in an elaborately partitioned context space instead of the individual
workers, to effectively incentivize the massive unknown workers with a very
limited budget. We also extend the above basic idea to the on-line setting
where unknown workers may join in or depart from the systems dynamically, and
propose an on-line version of the CACI mechanism. We perform rigorous
theoretical analysis to reveal the upper bounds on the regrets of our CACI
mechanisms and to prove their truthfulness and individual rationality,
respectively. Extensive experiments on both synthetic and real datasets are
also conducted to verify the efficacy of our mechanisms.
| [
{
"version": "v1",
"created": "Thu, 21 Sep 2023 14:30:42 GMT"
},
{
"version": "v2",
"created": "Wed, 3 Jan 2024 02:53:30 GMT"
}
] | 1,704,326,400,000 | [
[
"Li",
"Feng",
""
],
[
"Chai",
"Yuqi",
""
],
[
"Yang",
"Huan",
""
],
[
"Hu",
"Pengfei",
""
],
[
"Duan",
"Lingjie",
""
]
] |
2309.12132 | Chunmo Zheng | Chunmo Zheng, Saika Wong, Xing Su, Yinqiu Tang | A knowledge representation approach for construction contract knowledge
modeling | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The emergence of large language models (LLMs) presents an unprecedented
opportunity to automate construction contract management, reducing human errors
and saving significant time and costs. However, LLMs may produce convincing yet
inaccurate and misleading content due to a lack of domain expertise. To address
this issue, expert-driven contract knowledge can be represented in a structured
manner to constrain the automatic contract management process. This paper
introduces the Nested Contract Knowledge Graph (NCKG), a knowledge
representation approach that captures the complexity of contract knowledge
using a nested structure. It includes a nested knowledge representation
framework, a NCKG ontology built on the framework, and an implementation
method. Furthermore, we present the LLM-assisted contract review pipeline
enhanced with external knowledge in NCKG. Our pipeline achieves a promising
performance in contract risk reviewing, shedding light on the combination of
LLM and KG towards more reliable and interpretable contract management.
| [
{
"version": "v1",
"created": "Thu, 21 Sep 2023 14:53:36 GMT"
}
] | 1,695,340,800,000 | [
[
"Zheng",
"Chunmo",
""
],
[
"Wong",
"Saika",
""
],
[
"Su",
"Xing",
""
],
[
"Tang",
"Yinqiu",
""
]
] |
2309.12177 | Senthil Kumar Jagatheesaperumal Dr. | Roohallah Alizadehsani, Solomon Sunday Oyelere, Sadiq Hussain, Rene
Ripardo Calixto, Victor Hugo C. de Albuquerque, Mohamad Roshanzamir, Mohamed
Rahouti, and Senthil Kumar Jagatheesaperumal | Explainable Artificial Intelligence for Drug Discovery and Development
-- A Comprehensive Survey | 13 pages, 3 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The field of drug discovery has experienced a remarkable transformation with
the advent of artificial intelligence (AI) and machine learning (ML)
technologies. However, as these AI and ML models are becoming more complex,
there is a growing need for transparency and interpretability of the models.
Explainable Artificial Intelligence (XAI) is a novel approach that addresses
this issue and provides a more interpretable understanding of the predictions
made by machine learning models. In recent years, there has been an increasing
interest in the application of XAI techniques to drug discovery. This review
article provides a comprehensive overview of the current state-of-the-art in
XAI for drug discovery, including various XAI methods, their application in
drug discovery, and the challenges and limitations of XAI techniques in drug
discovery. The article also covers the application of XAI in drug discovery,
including target identification, compound design, and toxicity prediction.
Furthermore, the article suggests potential future research directions for the
application of XAI in drug discovery. The aim of this review article is to
provide a comprehensive understanding of the current state of XAI in drug
discovery and its potential to transform the field.
| [
{
"version": "v1",
"created": "Thu, 21 Sep 2023 15:36:06 GMT"
},
{
"version": "v2",
"created": "Thu, 2 Nov 2023 11:06:27 GMT"
}
] | 1,698,969,600,000 | [
[
"Alizadehsani",
"Roohallah",
""
],
[
"Oyelere",
"Solomon Sunday",
""
],
[
"Hussain",
"Sadiq",
""
],
[
"Calixto",
"Rene Ripardo",
""
],
[
"de Albuquerque",
"Victor Hugo C.",
""
],
[
"Roshanzamir",
"Mohamad",
""
],
[
"Rahouti",
"Mohamed",
""
],
[
"Jagatheesaperumal",
"Senthil Kumar",
""
]
] |
2309.12423 | Oktie Hassanzadeh | Sola Shirai, Debarun Bhattacharjya, Oktie Hassanzadeh | Event Prediction using Case-Based Reasoning over Knowledge Graphs | published at WWW '23: Proceedings of the ACM Web Conference 2023.
Code base: https://github.com/solashirai/WWW-EvCBR | null | 10.1145/3543507.3583201 | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Applying link prediction (LP) methods over knowledge graphs (KG) for tasks
such as causal event prediction presents an exciting opportunity. However,
typical LP models are ill-suited for this task as they are incapable of
performing inductive link prediction for new, unseen event entities and they
require retraining as knowledge is added or changed in the underlying KG. We
introduce a case-based reasoning model, EvCBR, to predict properties about new
consequent events based on similar cause-effect events present in the KG. EvCBR
uses statistical measures to identify similar events and performs path-based
predictions, requiring no training step. To generalize our methods beyond the
domain of event prediction, we frame our task as a 2-hop LP task, where the
first hop is a causal relation connecting a cause event to a new effect event
and the second hop is a property about the new event which we wish to predict.
The effectiveness of our method is demonstrated using a novel dataset of
newsworthy events with causal relations curated from Wikidata, where EvCBR
outperforms baselines including translational-distance-based, GNN-based, and
rule-based LP models.
| [
{
"version": "v1",
"created": "Thu, 21 Sep 2023 18:46:29 GMT"
}
] | 1,695,600,000,000 | [
[
"Shirai",
"Sola",
""
],
[
"Bhattacharjya",
"Debarun",
""
],
[
"Hassanzadeh",
"Oktie",
""
]
] |
2309.12529 | Shuang Ao | Shuang Ao, Tianyi Zhou, Guodong Long, Xuan Song, Jing Jiang | Curriculum Reinforcement Learning via Morphology-Environment
Co-Evolution | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Throughout long history, natural species have learned to survive by evolving
their physical structures adaptive to the environment changes. In contrast,
current reinforcement learning (RL) studies mainly focus on training an agent
with a fixed morphology (e.g., skeletal structure and joint attributes) in a
fixed environment, which can hardly generalize to changing environments or new
tasks. In this paper, we optimize an RL agent and its morphology through
``morphology-environment co-evolution (MECE)'', in which the morphology keeps
being updated to adapt to the changing environment, while the environment is
modified progressively to bring new challenges and stimulate the improvement of
the morphology. This leads to a curriculum to train generalizable RL, whose
morphology and policy are optimized for different environments. Instead of
hand-crafting the curriculum, we train two policies to automatically change the
morphology and the environment. To this end, (1) we develop two novel and
effective rewards for the two policies, which are solely based on the learning
dynamics of the RL agent; (2) we design a scheduler to automatically determine
when to change the environment and the morphology. In experiments on two
classes of tasks, the morphology and RL policies trained via MECE exhibit
significantly better generalization performance in unseen test environments
than SOTA morphology optimization methods. Our ablation studies on the two MECE
policies further show that the co-evolution between the morphology and
environment is the key to the success.
| [
{
"version": "v1",
"created": "Thu, 21 Sep 2023 22:58:59 GMT"
}
] | 1,695,600,000,000 | [
[
"Ao",
"Shuang",
""
],
[
"Zhou",
"Tianyi",
""
],
[
"Long",
"Guodong",
""
],
[
"Song",
"Xuan",
""
],
[
"Jiang",
"Jing",
""
]
] |
2309.12579 | Parag Saxena | Parag Saxena | From Text to Trends: A Unique Garden Analytics Perspective on the Future
of Modern Agriculture | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Data-driven insights are essential for modern agriculture. This research
paper introduces a machine learning framework designed to improve how we
educate and reach out to people in the field of horticulture. The framework
relies on data from the Horticulture Online Help Desk (HOHD), which is like a
big collection of questions from people who love gardening and are part of the
Extension Master Gardener Program (EMGP). This framework has two main parts.
First, it uses special computer programs (machine learning models) to sort
questions into categories. This helps us quickly send each question to the
right expert, so we can answer it faster. Second, it looks at when questions
are asked and uses that information to guess how many questions we might get in
the future and what they will be about. This helps us plan on topics that will
be really important. It's like knowing what questions will be popular in the
coming months. We also take into account where the questions come from by
looking at the Zip Code. This helps us make research that fits the challenges
faced by gardeners in different places. In this paper, we demonstrate the
potential of machine learning techniques to predict trends in horticulture by
analyzing textual queries from homeowners. We show that NLP, classification,
and time series analysis can be used to identify patterns in homeowners'
queries and predict future trends in horticulture. Our results suggest that
machine learning could be used to predict trends in other agricultural sectors
as well. If large-scale agriculture industries curate and maintain a comparable
repository of textual data, the potential for trend prediction and strategic
agricultural planning could be revolutionized. This convergence of technology
and agriculture offers a promising pathway for the future of sustainable
farming and data-informed agricultural practices
| [
{
"version": "v1",
"created": "Fri, 22 Sep 2023 02:15:12 GMT"
}
] | 1,695,600,000,000 | [
[
"Saxena",
"Parag",
""
]
] |
2309.12655 | Paolo Liberatore | Paolo Liberatore | Natural revision is contingently-conditionalized revision | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Natural revision seems so natural: it changes beliefs as little as possible
to incorporate new information. Yet, some counterexamples show it wrong. It is
so conservative that it never fully believes. It only believes in the current
conditions. This is right in some cases and wrong in others. Which is which?
The answer requires extending natural revision from simple formulae expressing
universal truths (something holds) to conditionals expressing conditional truth
(something holds in certain conditions). The extension is based on the basic
principles natural revision follows, identified as minimal change, indifference
and naivety: change beliefs as little as possible; equate the likeliness of
scenarios by default; believe all until contradicted. The extension says that
natural revision restricts changes to the current conditions. A comparison with
an unrestricting revision shows what exactly the current conditions are. It is
not what currently considered true if it contradicts the new information. It
includes something more and more unlikely until the new information is at least
possible.
| [
{
"version": "v1",
"created": "Fri, 22 Sep 2023 06:52:30 GMT"
}
] | 1,695,600,000,000 | [
[
"Liberatore",
"Paolo",
""
]
] |
2309.12696 | Yun Qu | Jianzhun Shao, Yun Qu, Chen Chen, Hongchang Zhang, Xiangyang Ji | Counterfactual Conservative Q Learning for Offline Multi-agent
Reinforcement Learning | 37th Conference on Neural Information Processing Systems (NeurIPS
2023) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Offline multi-agent reinforcement learning is challenging due to the coupling
effect of both distribution shift issue common in offline setting and the high
dimension issue common in multi-agent setting, making the action
out-of-distribution (OOD) and value overestimation phenomenon excessively
severe. Tomitigate this problem, we propose a novel multi-agent offline RL
algorithm, named CounterFactual Conservative Q-Learning (CFCQL) to conduct
conservative value estimation. Rather than regarding all the agents as a high
dimensional single one and directly applying single agent methods to it, CFCQL
calculates conservative regularization for each agent separately in a
counterfactual way and then linearly combines them to realize an overall
conservative value estimation. We prove that it still enjoys the
underestimation property and the performance guarantee as those single agent
conservative methods do, but the induced regularization and safe policy
improvement bound are independent of the agent number, which is therefore
theoretically superior to the direct treatment referred to above, especially
when the agent number is large. We further conduct experiments on four
environments including both discrete and continuous action settings on both
existing and our man-made datasets, demonstrating that CFCQL outperforms
existing methods on most datasets and even with a remarkable margin on some of
them.
| [
{
"version": "v1",
"created": "Fri, 22 Sep 2023 08:10:25 GMT"
}
] | 1,695,600,000,000 | [
[
"Shao",
"Jianzhun",
""
],
[
"Qu",
"Yun",
""
],
[
"Chen",
"Chen",
""
],
[
"Zhang",
"Hongchang",
""
],
[
"Ji",
"Xiangyang",
""
]
] |
2309.12711 | Tristan Cazenave | Marc Pierre and Quentin Cohen-Solal and Tristan Cazenave | The Mathematical Game | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Monte Carlo Tree Search can be used for automated theorem proving. Holophrasm
is a neural theorem prover using MCTS combined with neural networks for the
policy and the evaluation. In this paper we propose to improve the performance
of the Holophrasm theorem prover using other game tree search algorithms.
| [
{
"version": "v1",
"created": "Fri, 22 Sep 2023 08:43:57 GMT"
}
] | 1,695,600,000,000 | [
[
"Pierre",
"Marc",
""
],
[
"Cohen-Solal",
"Quentin",
""
],
[
"Cazenave",
"Tristan",
""
]
] |
2309.12731 | Dave Raggett | Dave Raggett | Defeasible Reasoning with Knowledge Graphs | Accepted for: Knowledge Graph and Semantic Web Conference
(KGSWC-2023), 13-15 September, 2023, Zaragoza, Spain | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Human knowledge is subject to uncertainties, imprecision, incompleteness and
inconsistencies. Moreover, the meaning of many everyday terms is dependent on
the context. That poses a huge challenge for the Semantic Web. This paper
introduces work on an intuitive notation and model for defeasible reasoning
with imperfect knowledge, and relates it to previous work on argumentation
theory. PKN is to N3 as defeasible reasoning is to deductive logic. Further
work is needed on an intuitive syntax for describing reasoning strategies and
tactics in declarative terms, drawing upon the AIF ontology for inspiration.
The paper closes with observations on symbolic approaches in the era of large
language models.
| [
{
"version": "v1",
"created": "Fri, 22 Sep 2023 09:27:26 GMT"
}
] | 1,695,600,000,000 | [
[
"Raggett",
"Dave",
""
]
] |
2309.13218 | Pivithuru Thejan Amarasinghe | Pivithuru Thejan Amarasinghe, Su Nguyen, Yuan Sun and Damminda
Alahakoon | AI-Copilot for Business Optimisation: A Framework and A Case Study in
Production Scheduling | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Business optimisation refers to the process of finding and implementing
efficient and cost-effective means of operation to bring a competitive
advantage for businesses. Synthesizing problem formulations is an integral part
of business optimisation, which relies on human expertise to construct problem
formulations using optimisation languages. Interestingly, with advancements in
Large Language Models (LLMs), the human expertise needed in problem formulation
can be minimized. However, developing an LLM for problem formulation is
challenging, due to training data, token limitations, and lack of appropriate
performance metrics. For the requirement of training data, recent attention has
been directed towards fine-tuning pre-trained LLMs for downstream tasks rather
than training an LLM from scratch for a specific task. In this paper, we adopt
an LLM fine-tuning approach and propose an AI-Copilot for business optimisation
problem formulation. For token limitations, we introduce modularization and
prompt engineering techniques to synthesize complex problem formulations as
modules that fit into the token limits of LLMs. Additionally, we design
performance evaluation metrics that are better suited for assessing the
accuracy and quality of problem formulations. The experiment results
demonstrate that with this approach we can synthesize complex and large problem
formulations for a typical business optimisation problem in production
scheduling.
| [
{
"version": "v1",
"created": "Fri, 22 Sep 2023 23:45:21 GMT"
},
{
"version": "v2",
"created": "Fri, 29 Sep 2023 04:19:39 GMT"
},
{
"version": "v3",
"created": "Wed, 18 Oct 2023 23:34:15 GMT"
}
] | 1,697,760,000,000 | [
[
"Amarasinghe",
"Pivithuru Thejan",
""
],
[
"Nguyen",
"Su",
""
],
[
"Sun",
"Yuan",
""
],
[
"Alahakoon",
"Damminda",
""
]
] |
2309.13229 | Jiaqi Wen | Jiaqi Wen, Bogdan Gabrys, Katarzyna Musial | Heterogeneous Feature Representation for Digital Twin-Oriented Complex
Networked Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Building models of Complex Networked Systems (CNS) that can accurately
represent reality forms an important research area. To be able to reflect real
world systems, the modelling needs to consider not only the intensity of
interactions between the entities but also features of all the elements of the
system. This study aims to improve the expressive power of node features in
Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with heterogeneous
feature representation principles. This involves representing features with
crisp feature values and fuzzy sets, each describing the objective and the
subjective inductions of the nodes' features and feature differences. Our
empirical analysis builds DT-CNSs to recreate realistic physical contact
networks in different countries from real node feature distributions based on
various representation principles and an optimised feature preference. We also
investigate their respective disaster resilience to an epidemic outbreak
starting from the most popular node. The results suggest that the increasing
flexibility of feature representation with fuzzy sets improves the expressive
power and enables more accurate modelling. In addition, the heterogeneous
features influence the network structure and the speed of the epidemic
outbreak, requiring various mitigation policies targeted at different people.
| [
{
"version": "v1",
"created": "Sat, 23 Sep 2023 01:40:56 GMT"
}
] | 1,695,686,400,000 | [
[
"Wen",
"Jiaqi",
""
],
[
"Gabrys",
"Bogdan",
""
],
[
"Musial",
"Katarzyna",
""
]
] |
2309.13834 | Ruilin Luo | Jiayi Li, Ruilin Luo, Jiaqi Sun, Jing Xiao, Yujiu Yang | Prior Bilinear Based Models for Knowledge Graph Completion | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Bilinear based models are powerful and widely used approaches for Knowledge
Graphs Completion (KGC). Although bilinear based models have achieved
significant advances, these studies mainly concentrate on posterior properties
(based on evidence, e.g. symmetry pattern) while neglecting the prior
properties. In this paper, we find a prior property named "the law of identity"
that cannot be captured by bilinear based models, which hinders them from
comprehensively modeling the characteristics of KGs. To address this issue, we
introduce a solution called Unit Ball Bilinear Model (UniBi). This model not
only achieves theoretical superiority but also offers enhanced interpretability
and performance by minimizing ineffective learning through minimal constraints.
Experiments demonstrate that UniBi models the prior property and verify its
interpretability and performance.
| [
{
"version": "v1",
"created": "Mon, 25 Sep 2023 02:44:33 GMT"
}
] | 1,695,686,400,000 | [
[
"Li",
"Jiayi",
""
],
[
"Luo",
"Ruilin",
""
],
[
"Sun",
"Jiaqi",
""
],
[
"Xiao",
"Jing",
""
],
[
"Yang",
"Yujiu",
""
]
] |
2309.13939 | Irene Celino | Irene Celino and Heiko Paulheim | The Time Traveler's Guide to Semantic Web Research: Analyzing Fictitious
Research Themes in the ESWC "Next 20 Years" Track | 13 pages, 8 figures, 2 tables | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | What will Semantic Web research focus on in 20 years from now? We asked this
question to the community and collected their visions in the "Next 20 years"
track of ESWC 2023. We challenged the participants to submit "future" research
papers, as if they were submitting to the 2043 edition of the conference. The
submissions - entirely fictitious - were expected to be full scientific papers,
with research questions, state of the art references, experimental results and
future work, with the goal to get an idea of the research agenda for the late
2040s and early 2050s. We received ten submissions, eight of which were
accepted for presentation at the conference, that mixed serious ideas of
potential future research themes and discussion topics with some fun and irony.
In this paper, we intend to provide a survey of those "science fiction"
papers, considering the emerging research themes and topics, analysing the
research methods applied by the authors in these very special submissions, and
investigating also the most fictitious parts (e.g., neologisms, fabricated
references). Our goal is twofold: on the one hand, we investigate what this
special track tells us about the Semantic Web community and, on the other hand,
we aim at getting some insights on future research practices and directions.
| [
{
"version": "v1",
"created": "Mon, 25 Sep 2023 08:20:06 GMT"
}
] | 1,695,686,400,000 | [
[
"Celino",
"Irene",
""
],
[
"Paulheim",
"Heiko",
""
]
] |
2309.14663 | Pranav Rajbhandari | Pranav Rajbhandari, Donald Sofge | Learning Emergent Behavior in Robot Swarms with NEAT | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | When researching robot swarms, many studies observe complex group behavior
emerging from the individual agents' simple local actions. However, the task of
learning an individual policy to produce a desired emergent behavior remains a
challenging and largely unsolved problem. We present a method of training
distributed robotic swarm algorithms to produce emergent behavior. Inspired by
the biological evolution of emergent behavior in animals, we use an
evolutionary algorithm to train a 'population' of individual behaviors to
approximate a desired group behavior. We perform experiments using simulations
of the Georgia Tech Miniature Autonomous Blimps (GT-MABs) aerial robotics
platforms conducted in the CoppeliaSim simulator. Additionally, we test on
simulations of Anki Vector robots to display our algorithm's effectiveness on
various modes of actuation. We evaluate our algorithm on various tasks where a
somewhat complex group behavior is required for success. These tasks include an
Area Coverage task, a Surround Target task, and a Wall Climb task. We compare
behaviors evolved using our algorithm against 'designed policies', which we
create in order to exhibit the emergent behaviors we desire.
| [
{
"version": "v1",
"created": "Tue, 26 Sep 2023 04:40:52 GMT"
}
] | 1,695,772,800,000 | [
[
"Rajbhandari",
"Pranav",
""
],
[
"Sofge",
"Donald",
""
]
] |
2309.14718 | Andrew Fuchs | Andrew Fuchs, Andrea Passarella, Marco Conti | Optimizing delegation between human and AI collaborative agents | This work has been accepted to the 'Towards Hybrid Human-Machine
Learning and Decision Making (HLDM)' workshop at ECML PKDD 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In the context of humans operating with artificial or autonomous agents in a
hybrid team, it is essential to accurately identify when to authorize those
team members to perform actions. Given past examples where humans and
autonomous systems can either succeed or fail at tasks, we seek to train a
delegating manager agent to make delegation decisions with respect to these
potential performance deficiencies. Additionally, we cannot always expect the
various agents to operate within the same underlying model of the environment.
It is possible to encounter cases where the actions and transitions would vary
between agents. Therefore, our framework provides a manager model which learns
through observations of team performance without restricting agents to matching
dynamics. Our results show our manager learns to perform delegation decisions
with teams of agents operating under differing representations of the
environment, significantly outperforming alternative methods to manage the
team.
| [
{
"version": "v1",
"created": "Tue, 26 Sep 2023 07:23:26 GMT"
},
{
"version": "v2",
"created": "Wed, 11 Oct 2023 07:28:04 GMT"
}
] | 1,697,068,800,000 | [
[
"Fuchs",
"Andrew",
""
],
[
"Passarella",
"Andrea",
""
],
[
"Conti",
"Marco",
""
]
] |
2309.14796 | Eunseong Choi | Yoonjin Im, Eunseong Choi, Heejin Kook, Jongwuk Lee | Forgetting-aware Linear Bias for Attentive Knowledge Tracing | In Proceedings of the 32nd ACM International Conference on
Information and Knowledge Management (CIKM'23), 5 pages, 3 figures, 2 tables | null | 10.1145/3583780.3615191 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Knowledge Tracing (KT) aims to track proficiency based on a question-solving
history, allowing us to offer a streamlined curriculum. Recent studies actively
utilize attention-based mechanisms to capture the correlation between questions
and combine it with the learner's characteristics for responses. However, our
empirical study shows that existing attention-based KT models neglect the
learner's forgetting behavior, especially as the interaction history becomes
longer. This problem arises from the bias that overprioritizes the correlation
of questions while inadvertently ignoring the impact of forgetting behavior.
This paper proposes a simple-yet-effective solution, namely Forgetting-aware
Linear Bias (FoLiBi), to reflect forgetting behavior as a linear bias. Despite
its simplicity, FoLiBi is readily equipped with existing attentive KT models by
effectively decomposing question correlations with forgetting behavior. FoLiBi
plugged with several KT models yields a consistent improvement of up to 2.58%
in AUC over state-of-the-art KT models on four benchmark datasets.
| [
{
"version": "v1",
"created": "Tue, 26 Sep 2023 09:48:30 GMT"
}
] | 1,695,772,800,000 | [
[
"Im",
"Yoonjin",
""
],
[
"Choi",
"Eunseong",
""
],
[
"Kook",
"Heejin",
""
],
[
"Lee",
"Jongwuk",
""
]
] |
2309.15242 | Yi Wang | Yi Wang, Jieliang Luo, Adam Gaier, Evan Atherton, Hilmar Koch | PlotMap: Automated Layout Design for Building Game Worlds | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | World-building, the process of developing both the narrative and physical
world of a game, plays a vital role in the game's experience. Critically
acclaimed independent and AAA video games are praised for strong world
building, with game maps that masterfully intertwine with and elevate the
narrative, captivating players and leaving a lasting impression. However,
designing game maps that support a desired narrative is challenging, as it
requires satisfying complex constraints from various considerations. Most
existing map generation methods focus on considerations about gameplay
mechanics or map topography, while the need to support the story is typically
neglected. As a result, extensive manual adjustment is still required to design
a game world that facilitates particular stories. In this work, we approach
this problem by introducing an extra layer of plot facility layout design that
is independent of the underlying map generation method in a world-building
pipeline. Concretely, we present a system that leverages Reinforcement Learning
(RL) to automatically assign concrete locations on a game map to abstract
locations mentioned in a given story (plot facilities), following spatial
constraints derived from the story. A decision-making agent moves the plot
facilities around, considering their relationship to the map and each other, to
locations on the map that best satisfy the constraints of the story. Our system
considers input from multiple modalities: map images as pixels, facility
locations as real values, and story constraints expressed in natural language.
We develop a method of generating datasets of facility layout tasks, create an
RL environment to train and evaluate RL models, and further analyze the
behaviors of the agents through a group of comprehensive experiments and
ablation studies, aiming to provide insights for RL-based plot facility layout
design.
| [
{
"version": "v1",
"created": "Tue, 26 Sep 2023 20:13:10 GMT"
}
] | 1,695,859,200,000 | [
[
"Wang",
"Yi",
""
],
[
"Luo",
"Jieliang",
""
],
[
"Gaier",
"Adam",
""
],
[
"Atherton",
"Evan",
""
],
[
"Koch",
"Hilmar",
""
]
] |
2309.15484 | Kuo-Hao Ho | Kuo-Hao Ho, Ping-Chun Hsieh, Chiu-Chou Lin, You-Ren Luo, Feng-Jian
Wang, I-Chen Wu | Towards Human-Like RL: Taming Non-Naturalistic Behavior in Deep RL via
Adaptive Behavioral Costs in 3D Games | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a new approach called Adaptive Behavioral Costs in
Reinforcement Learning (ABC-RL) for training a human-like agent with
competitive strength. While deep reinforcement learning agents have recently
achieved superhuman performance in various video games, some of these
unconstrained agents may exhibit actions, such as shaking and spinning, that
are not typically observed in human behavior, resulting in peculiar gameplay
experiences. To behave like humans and retain similar performance, ABC-RL
augments behavioral limitations as cost signals in reinforcement learning with
dynamically adjusted weights. Unlike traditional constrained policy
optimization, we propose a new formulation that minimizes the behavioral costs
subject to a constraint of the value function. By leveraging the augmented
Lagrangian, our approach is an approximation of the Lagrangian adjustment,
which handles the trade-off between the performance and the human-like
behavior. Through experiments conducted on 3D games in DMLab-30 and Unity
ML-Agents Toolkit, we demonstrate that ABC-RL achieves the same performance
level while significantly reducing instances of shaking and spinning. These
findings underscore the effectiveness of our proposed approach in promoting
more natural and human-like behavior during gameplay.
| [
{
"version": "v1",
"created": "Wed, 27 Sep 2023 08:28:59 GMT"
}
] | 1,695,859,200,000 | [
[
"Ho",
"Kuo-Hao",
""
],
[
"Hsieh",
"Ping-Chun",
""
],
[
"Lin",
"Chiu-Chou",
""
],
[
"Luo",
"You-Ren",
""
],
[
"Wang",
"Feng-Jian",
""
],
[
"Wu",
"I-Chen",
""
]
] |
2309.15517 | Kuo-Hao Ho | Kuo-Hao Ho, Ruei-Yu Jheng, Ji-Han Wu, Fan Chiang, Yen-Chi Chen,
Yuan-Yu Wu, I-Chen Wu | Residual Scheduling: A New Reinforcement Learning Approach to Solving
Job Shop Scheduling Problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Job-shop scheduling problem (JSP) is a mathematical optimization problem
widely used in industries like manufacturing, and flexible JSP (FJSP) is also a
common variant. Since they are NP-hard, it is intractable to find the optimal
solution for all cases within reasonable times. Thus, it becomes important to
develop efficient heuristics to solve JSP/FJSP. A kind of method of solving
scheduling problems is construction heuristics, which constructs scheduling
solutions via heuristics. Recently, many methods for construction heuristics
leverage deep reinforcement learning (DRL) with graph neural networks (GNN). In
this paper, we propose a new approach, named residual scheduling, to solving
JSP/FJSP. In this new approach, we remove irrelevant machines and jobs such as
those finished, such that the states include the remaining (or relevant)
machines and jobs only. Our experiments show that our approach reaches
state-of-the-art (SOTA) among all known construction heuristics on most
well-known open JSP and FJSP benchmarks. In addition, we also observe that even
though our model is trained for scheduling problems of smaller sizes, our
method still performs well for scheduling problems of large sizes.
Interestingly in our experiments, our approach even reaches zero gap for 49
among 50 JSP instances whose job numbers are more than 150 on 20 machines.
| [
{
"version": "v1",
"created": "Wed, 27 Sep 2023 09:33:56 GMT"
},
{
"version": "v2",
"created": "Tue, 3 Oct 2023 01:28:16 GMT"
}
] | 1,696,377,600,000 | [
[
"Ho",
"Kuo-Hao",
""
],
[
"Jheng",
"Ruei-Yu",
""
],
[
"Wu",
"Ji-Han",
""
],
[
"Chiang",
"Fan",
""
],
[
"Chen",
"Yen-Chi",
""
],
[
"Wu",
"Yuan-Yu",
""
],
[
"Wu",
"I-Chen",
""
]
] |
2309.15577 | Anthony Cohn | Anthony G Cohn | An Evaluation of ChatGPT-4's Qualitative Spatial Reasoning Capabilities
in RCC-8 | 10 figures. 8 pages. Accepted for presentation at 36th International
Workshop on Qualitative Reasoning (QR-23), in conjunction with ECAI2023 in
Krakow, Poland | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Qualitative Spatial Reasoning (QSR) is well explored area of Commonsense
Reasoning and has multiple applications ranging from Geographical Information
Systems to Robotics and Computer Vision. Recently many claims have been made
for the capabilities of Large Language Models (LLMs). In this paper we
investigate the extent to which one particular LLM can perform classical
qualitative spatial reasoning tasks on the mereotopological calculus, RCC-8.
| [
{
"version": "v1",
"created": "Wed, 27 Sep 2023 11:23:15 GMT"
}
] | 1,695,859,200,000 | [
[
"Cohn",
"Anthony G",
""
]
] |
2309.16146 | Ming Wang | Ming Wang, Daling Wang, Wenfang Wu, Shi Feng, Yifei Zhang | T-COL: Generating Counterfactual Explanations for General User
Preferences on Variable Machine Learning Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To address the interpretability challenge in machine learning (ML) systems,
counterfactual explanations (CEs) have emerged as a promising solution. CEs are
unique as they provide workable suggestions to users, in addition to explaining
why a certain outcome was predicted. The application of CEs encounters two main
challenges: general user preferences and variable ML systems. User preferences
tend to be general rather than specific, and CEs need to be adaptable to
variable ML models while maintaining robustness even as these models change.
Facing these challenges, we present a solution rooted in validated general user
preferences, which are derived from thorough user research. We map these
preferences to the properties of CEs. Additionally, we introduce a novel
method, \uline{T}ree-based \uline{C}onditions \uline{O}ptional \uline{L}inks
(T-COL), which incorporates two optional structures and multiple condition
groups for generating CEs adaptable to general user preferences. Meanwhile, we
employ T-COL to enhance the robustness of CEs with specific conditions, making
them more valid even when the ML model is replaced. Our experimental
comparisons under different user preferences show that T-COL outperforms all
baselines, including Large Language Models which are shown to be able to
generate counterfactuals.
| [
{
"version": "v1",
"created": "Thu, 28 Sep 2023 03:51:49 GMT"
},
{
"version": "v2",
"created": "Thu, 4 Apr 2024 05:59:22 GMT"
}
] | 1,712,275,200,000 | [
[
"Wang",
"Ming",
""
],
[
"Wang",
"Daling",
""
],
[
"Wu",
"Wenfang",
""
],
[
"Feng",
"Shi",
""
],
[
"Zhang",
"Yifei",
""
]
] |
2309.16166 | Stuart Armstrong | Stuart Armstrong and Alexandre Maranh\~ao and Oliver Daniels-Koch and
Patrick Leask and Rebecca Gorman | CoinRun: Solving Goal Misgeneralisation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Goal misgeneralisation is a key challenge in AI alignment -- the task of
getting powerful Artificial Intelligences to align their goals with human
intentions and human morality. In this paper, we show how the ACE (Algorithm
for Concept Extrapolation) agent can solve one of the key standard challenges
in goal misgeneralisation: the CoinRun challenge. It uses no new reward
information in the new environment. This points to how autonomous agents could
be trusted to act in human interests, even in novel and critical situations.
| [
{
"version": "v1",
"created": "Thu, 28 Sep 2023 04:43:39 GMT"
},
{
"version": "v2",
"created": "Sat, 21 Oct 2023 20:08:46 GMT"
},
{
"version": "v3",
"created": "Wed, 1 Nov 2023 17:23:46 GMT"
}
] | 1,698,883,200,000 | [
[
"Armstrong",
"Stuart",
""
],
[
"Maranhão",
"Alexandre",
""
],
[
"Daniels-Koch",
"Oliver",
""
],
[
"Leask",
"Patrick",
""
],
[
"Gorman",
"Rebecca",
""
]
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.