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2210.16502 | Xue-Ping Wang | Meng Li, Xue-Ping Wang | The solution set of fuzzy relation equations with addition-min
composition | 19 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper deals with the resolutions of fuzzy relation equations with
addition-min composition. When the fuzzy relation equations have a solution, we
first propose an algorithm to find all minimal solutions of the fuzzy relation
equations and also supply an algorithm to find all maximal solutions of the
fuzzy relation equations, which will be illustrated, respectively, by numeral
examples. Then we prove that every solution of the fuzzy relation equations is
between a minimal solution and a maximal one, so that we describe the solution
set of the fuzzy relation equations completely.
| [
{
"version": "v1",
"created": "Sat, 29 Oct 2022 05:39:04 GMT"
}
] | 1,667,260,800,000 | [
[
"Li",
"Meng",
""
],
[
"Wang",
"Xue-Ping",
""
]
] |
2211.00486 | Robert R. Tucci | Robert R. Tucci | Causal DAG extraction from a library of books or videos/movies | 11 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Determining a causal DAG (directed acyclic graph) for a problem under
consideration, is a major roadblock when doing Judea Pearl's Causal Inference
(CI) in Statistics. The same problem arises when doing CI in Artificial
Intelligence (AI) and Machine Learning (ML). As with many problems in Science,
we think Nature has found an effective solution to this problem. We argue that
human and animal brains contain an explicit engine for doing CI, and that such
an engine uses as input an atlas (i.e., collection) of causal DAGs. We propose
a simple algorithm for constructing such an atlas from a library of books or
videos/movies. We illustrate our method by applying it to a database of
randomly generated Tic-Tac-Toe games. The software used to generate this
Tic-Tac-Toe example is open source and available at GitHub.
| [
{
"version": "v1",
"created": "Sat, 29 Oct 2022 16:09:22 GMT"
}
] | 1,667,347,200,000 | [
[
"Tucci",
"Robert R.",
""
]
] |
2211.00901 | Lei Kou | Jian Wang, Xi Wang, Chaoqun Ma, Lei Kou | A survey on the development status and application prospects of
knowledge graph in smart grids | IET Generation, Transmission & Distribution | null | 10.1049/gtd2.12040 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the advent of the electric power big data era, semantic interoperability
and interconnection of power data have received extensive attention. Knowledge
graph technology is a new method describing the complex relationships between
concepts and entities in the objective world, which is widely concerned because
of its robust knowledge inference ability. Especially with the proliferation of
measurement devices and exponential growth of electric power data empowers,
electric power knowledge graph provides new opportunities to solve the
contradictions between the massive power resources and the continuously
increasing demands for intelligent applications. In an attempt to fulfil the
potential of knowledge graph and deal with the various challenges faced, as
well as to obtain insights to achieve business applications of smart grids,
this work first presents a holistic study of knowledge-driven intelligent
application integration. Specifically, a detailed overview of electric power
knowledge mining is provided. Then, the overview of the knowledge graph in
smart grids is introduced. Moreover, the architecture of the big knowledge
graph platform for smart grids and critical technologies are described.
Furthermore, this paper comprehensively elaborates on the application prospects
leveraged by knowledge graph oriented to smart grids, power consumer service,
decision-making in dispatching, and operation and maintenance of power
equipment. Finally, issues and challenges are summarised.
| [
{
"version": "v1",
"created": "Wed, 2 Nov 2022 05:57:05 GMT"
}
] | 1,667,433,600,000 | [
[
"Wang",
"Jian",
""
],
[
"Wang",
"Xi",
""
],
[
"Ma",
"Chaoqun",
""
],
[
"Kou",
"Lei",
""
]
] |
2211.01496 | Yu Zhang | Yu Zhang, Mitchell Bucklew | Max Markov Chain | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we introduce Max Markov Chain (MMC), a novel representation
for a useful subset of High-order Markov Chains (HMCs) with sparse correlations
among the states. MMC is parsimony while retaining the expressiveness of HMCs.
Even though parameter optimization is generally intractable as with HMC
approximate models, it has an analytical solution, better sample efficiency,
and the desired spatial and computational advantages over HMCs and approximate
HMCs. Simultaneously, efficient approximate solutions exist for this type of
chains as we show empirically, which allow MMCs to scale to large domains where
HMCs and approximate HMCs would struggle to perform. We compare MMC with HMC,
first-order Markov chain, and an approximate HMC model in synthetic domains
with various data types to demonstrate that MMC is a valuable alternative for
modeling stochastic processes and has many potential applications.
| [
{
"version": "v1",
"created": "Wed, 2 Nov 2022 21:50:54 GMT"
}
] | 1,667,520,000,000 | [
[
"Zhang",
"Yu",
""
],
[
"Bucklew",
"Mitchell",
""
]
] |
2211.02849 | Xiang Li | Hongmin Cai, Wenxiong Liao, Zhengliang Liu, Yiyang Zhang, Xiaoke
Huang, Siqi Ding, Hui Ren, Zihao Wu, Haixing Dai, Sheng Li, Lingfei Wu,
Ninghao Liu, Quanzheng Li, Tianming Liu, Xiang Li | Coarse-to-fine Knowledge Graph Domain Adaptation based on
Distantly-supervised Iterative Training | null | null | 10.1109/BIBM58861.2023.10385649 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modern supervised learning neural network models require a large amount of
manually labeled data, which makes the construction of domain-specific
knowledge graphs time-consuming and labor-intensive. In parallel, although
there has been much research on named entity recognition and relation
extraction based on distantly supervised learning, constructing a
domain-specific knowledge graph from large collections of textual data without
manual annotations is still an urgent problem to be solved. In response, we
propose an integrated framework for adapting and re-learning knowledge graphs
from one coarse domain (biomedical) to a finer-define domain (oncology). In
this framework, we apply distant-supervision on cross-domain knowledge graph
adaptation. Consequently, no manual data annotation is required to train the
model. We introduce a novel iterative training strategy to facilitate the
discovery of domain-specific named entities and triples. Experimental results
indicate that the proposed framework can perform domain adaptation and
construction of knowledge graph efficiently.
| [
{
"version": "v1",
"created": "Sat, 5 Nov 2022 08:16:38 GMT"
},
{
"version": "v2",
"created": "Thu, 9 Mar 2023 01:29:46 GMT"
}
] | 1,707,350,400,000 | [
[
"Cai",
"Hongmin",
""
],
[
"Liao",
"Wenxiong",
""
],
[
"Liu",
"Zhengliang",
""
],
[
"Zhang",
"Yiyang",
""
],
[
"Huang",
"Xiaoke",
""
],
[
"Ding",
"Siqi",
""
],
[
"Ren",
"Hui",
""
],
[
"Wu",
"Zihao",
""
],
[
"Dai",
"Haixing",
""
],
[
"Li",
"Sheng",
""
],
[
"Wu",
"Lingfei",
""
],
[
"Liu",
"Ninghao",
""
],
[
"Li",
"Quanzheng",
""
],
[
"Liu",
"Tianming",
""
],
[
"Li",
"Xiang",
""
]
] |
2211.02992 | Ujwal Saini | Ujwal Saini | Foon Creation | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We have designed three search methods for producing the task trees for the
provided goal nodes using the Functional Object-Oriented Network. This paper
details the strategy, the procedure, and the outcomes.
| [
{
"version": "v1",
"created": "Sun, 6 Nov 2022 00:03:44 GMT"
}
] | 1,667,865,600,000 | [
[
"Saini",
"Ujwal",
""
]
] |
2211.03219 | Mikhail Genkin | Mikhail Genkin and J. J. McArthur | B-SMART: A Reference Architecture for Artificially Intelligent Autonomic
Smart Buildings | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The pervasive application of artificial intelligence and machine learning
algorithms is transforming many industries and aspects of the human experience.
One very important industry trend is the move to convert existing human
dwellings to smart buildings, and to create new smart buildings. Smart
buildings aim to mitigate climate change by reducing energy consumption and
associated carbon emissions. To accomplish this, they leverage artificial
intelligence, big data, and machine learning algorithms to learn and optimize
system performance. These fields of research are currently very rapidly
evolving and advancing, but there has been very little guidance to help
engineers and architects working on smart buildings apply artificial
intelligence algorithms and technologies in a systematic and effective manner.
In this paper we present B-SMART: the first reference architecture for
autonomic smart buildings. B-SMART facilitates the application of artificial
intelligence techniques and technologies to smart buildings by decoupling
conceptually distinct layers of functionality and organizing them into an
autonomic control loop. We also present a case study illustrating how B-SMART
can be applied to accelerate the introduction of artificial intelligence into
an existing smart building.
| [
{
"version": "v1",
"created": "Sun, 6 Nov 2022 20:56:25 GMT"
}
] | 1,667,865,600,000 | [
[
"Genkin",
"Mikhail",
""
],
[
"McArthur",
"J. J.",
""
]
] |
2211.03461 | Gavin Rens | Gavin Rens, Wen-Chi Yang, Jean-Fran\c{c}ois Raskin, Luc De Raedt | Learning Probabilistic Temporal Safety Properties from Examples in
Relational Domains | 25 pages, 3 figures, 5 tables, 2 algorithms, preprint | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a framework for learning a fragment of probabilistic computation
tree logic (pCTL) formulae from a set of states that are labeled as safe or
unsafe. We work in a relational setting and combine ideas from relational
Markov Decision Processes with pCTL model-checking. More specifically, we
assume that there is an unknown relational pCTL target formula that is
satisfied by only safe states, and has a horizon of maximum $k$ steps and a
threshold probability $\alpha$. The task then consists of learning this unknown
formula from states that are labeled as safe or unsafe by a domain expert. We
apply principles of relational learning to induce a pCTL formula that is
satisfied by all safe states and none of the unsafe ones. This formula can then
be used as a safety specification for this domain, so that the system can avoid
getting into dangerous situations in future. Following relational learning
principles, we introduce a candidate formula generation process, as well as a
method for deciding which candidate formula is a satisfactory specification for
the given labeled states. The cases where the expert knows and does not know
the system policy are treated, however, much of the learning process is the
same for both cases. We evaluate our approach on a synthetic relational domain.
| [
{
"version": "v1",
"created": "Mon, 7 Nov 2022 11:24:53 GMT"
}
] | 1,667,865,600,000 | [
[
"Rens",
"Gavin",
""
],
[
"Yang",
"Wen-Chi",
""
],
[
"Raskin",
"Jean-François",
""
],
[
"De Raedt",
"Luc",
""
]
] |
2211.03521 | Alexis Jacq | Alexis Jacq, Manu Orsini, Gabriel Dulac-Arnold, Olivier Pietquin,
Matthieu Geist, Olivier Bachem | On the importance of data collection for training general goal-reaching
policies | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Recent advances in ML suggest that the quantity of data available to a model
is one of the primary bottlenecks to high performance. Although for
language-based tasks there exist almost unlimited amounts of reasonably
coherent data to train from, this is generally not the case for Reinforcement
Learning, especially when dealing with a novel environment. In effect, even a
relatively trivial continuous environment has an almost limitless number of
states, but simply sampling random states and actions will likely not provide
transitions that are interesting or useful for any potential downstream task.
How should one generate massive amounts of useful data given only an MDP with
no indication of downstream tasks? Are the quantity and quality of data truly
transformative to the performance of a general controller? We propose to answer
both of these questions. First, we introduce a principled unsupervised
exploration method, ChronoGEM, which aims to achieve uniform coverage over the
manifold of achievable states, which we believe is the most reasonable goal
given no prior task information. Secondly, we investigate the effects of both
data quantity and data quality on the training of a downstream goal-achievement
policy, and show that both large quantities and high-quality of data are
essential to train a general controller: a high-precision pose-achievement
policy capable of attaining a large number of poses over numerous continuous
control embodiments including humanoid.
| [
{
"version": "v1",
"created": "Mon, 7 Nov 2022 13:02:40 GMT"
},
{
"version": "v2",
"created": "Mon, 20 Feb 2023 14:28:14 GMT"
}
] | 1,676,937,600,000 | [
[
"Jacq",
"Alexis",
""
],
[
"Orsini",
"Manu",
""
],
[
"Dulac-Arnold",
"Gabriel",
""
],
[
"Pietquin",
"Olivier",
""
],
[
"Geist",
"Matthieu",
""
],
[
"Bachem",
"Olivier",
""
]
] |
2211.03612 | Ming Liu | Ming Liu, Yaojia LV, Jingrun Zhang, Ruiji Fu, Bing Qin | BigCilin: An Automatic Chinese Open-domain Knowledge Graph with
Fine-grained Hypernym-Hyponym Relations | 5 pages, 3 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents BigCilin, the first Chinese open-domain knowledge graph
with fine-grained hypernym-hyponym re-lations which are extracted automatically
from multiple sources for Chinese named entities. With the fine-grained
hypernym-hyponym relations, BigCilin owns flexible semantic hierarchical
structure. Since the hypernym-hyponym paths are automati-cally generated and
one entity may have several senses, we provide a path disambi-guation solution
to map a hypernym-hyponym path of one entity to its one sense on the condition
that the path and the sense express the same meaning. In order to conveniently
access our BigCilin Knowle-dge graph, we provide web interface in two ways. One
is that it supports querying any Chinese named entity and browsing the
extracted hypernym-hyponym paths surro-unding the query entity. The other is
that it gives a top-down browsing view to illust-rate the overall hierarchical
structure of our BigCilin knowledge graph over some sam-pled entities.
| [
{
"version": "v1",
"created": "Mon, 7 Nov 2022 15:05:01 GMT"
}
] | 1,667,865,600,000 | [
[
"Liu",
"Ming",
""
],
[
"LV",
"Yaojia",
""
],
[
"Zhang",
"Jingrun",
""
],
[
"Fu",
"Ruiji",
""
],
[
"Qin",
"Bing",
""
]
] |
2211.03831 | Lucas Caccia | Lucas Caccia, Edoardo Ponti, Zhan Su, Matheus Pereira, Nicolas Le
Roux, Alessandro Sordoni | Multi-Head Adapter Routing for Cross-Task Generalization | Accepted at NeurIPS 2023. Code is available at
https://github.com/microsoft/mttl | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Parameter-efficient fine-tuning (PEFT) for cross-task generalization consists
in pre-training adapters on a multi-task training set before few-shot
adaptation to test tasks. Polytropon [Ponti et al., 2023] ($\texttt{Poly}$)
jointly learns an inventory of adapters and a routing function that selects a
(variable-size) subset of adapters for each task during both pre-training and
few-shot adaptation. In this paper, we investigate the role that adapter
routing plays in its success and design new variants based on our findings.
First, we build on the intuition that finer-grained routing provides more
expressivity. Hence, we propose $\texttt{MHR}$ (Multi-Head Routing) which
combines subsets of adapter parameters and outperforms $\texttt{Poly}$ under a
comparable parameter budget; by only fine-tuning the routing function and not
the adapters ($\texttt{MHR}$-$z$) we achieve competitive performance with
extreme parameter efficiency. Second, we find that
$\texttt{Poly}$/$\texttt{MHR}$ performance is a result of better multi-task
optimization, rather than modular inductive biases that facilitate adapter
recombination and local adaptation, as previously hypothesized. In fact, we
find that $\texttt{MHR}$ exhibits high gradient alignment between training
tasks. We find that routing is most beneficial during multi-task pre-training
rather than during few-shot adaptation and propose $\texttt{MHR}$-$\mu$, which
discards routing and fine-tunes the average of the pre-trained adapters on each
downstream tasks. This establishes $\texttt{MHR}$-$\mu$ as an effective method
for single-adapter fine-tuning. We also show that $\texttt{MHR}$-$\mu$ can be
used as an effective zero-shot transfer method by training the average of the
pre-trained adapters for a few additional steps on the multi-task training set:
this yields gains up to 3% on absolute accuracy w.r.t. the baselines.
| [
{
"version": "v1",
"created": "Mon, 7 Nov 2022 19:35:55 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Jun 2023 19:08:25 GMT"
},
{
"version": "v3",
"created": "Mon, 13 Nov 2023 15:09:59 GMT"
}
] | 1,699,920,000,000 | [
[
"Caccia",
"Lucas",
""
],
[
"Ponti",
"Edoardo",
""
],
[
"Su",
"Zhan",
""
],
[
"Pereira",
"Matheus",
""
],
[
"Roux",
"Nicolas Le",
""
],
[
"Sordoni",
"Alessandro",
""
]
] |
2211.03888 | Shiqing Wu | Qing Liu, Wenli Yang, Shiqing Wu | Proceedings of Principle and practice of data and Knowledge Acquisition
Workshop 2022 (PKAW 2022) | Proceedings of Principle and practice of data and Knowledge
Acquisition Workshop 2022 (PKAW 2022) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Over the past two decades, PKAW has provided a forum for researchers and
practitioners to discuss the state-of-the-arts in the area of knowledge
acquisition and machine intelligence (MI, also Artificial Intelligence, AI).
PKAW2022 will continue the above focus and welcome the contributions on the
multi-disciplinary approach of human and big data-driven knowledge acquisition,
as well as AI techniques and applications.
| [
{
"version": "v1",
"created": "Mon, 7 Nov 2022 22:34:12 GMT"
},
{
"version": "v2",
"created": "Wed, 7 Dec 2022 05:05:07 GMT"
}
] | 1,670,457,600,000 | [
[
"Liu",
"Qing",
""
],
[
"Yang",
"Wenli",
""
],
[
"Wu",
"Shiqing",
""
]
] |
2211.03890 | Carlos Correa | Carlos G. Correa, Mark K. Ho, Frederick Callaway, Nathaniel D. Daw,
Thomas L. Griffiths | Humans decompose tasks by trading off utility and computational cost | null | null | 10.1371/journal.pcbi.1011087 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Human behavior emerges from planning over elaborate decompositions of tasks
into goals, subgoals, and low-level actions. How are these decompositions
created and used? Here, we propose and evaluate a normative framework for task
decomposition based on the simple idea that people decompose tasks to reduce
the overall cost of planning while maintaining task performance. Analyzing
11,117 distinct graph-structured planning tasks, we find that our framework
justifies several existing heuristics for task decomposition and makes
predictions that can be distinguished from two alternative normative accounts.
We report a behavioral study of task decomposition ($N=806$) that uses 30
randomly sampled graphs, a larger and more diverse set than that of any
previous behavioral study on this topic. We find that human responses are more
consistent with our framework for task decomposition than alternative normative
accounts and are most consistent with a heuristic -- betweenness centrality --
that is justified by our approach. Taken together, our results provide new
theoretical insight into the computational principles underlying the
intelligent structuring of goal-directed behavior.
| [
{
"version": "v1",
"created": "Mon, 7 Nov 2022 22:45:46 GMT"
}
] | 1,685,923,200,000 | [
[
"Correa",
"Carlos G.",
""
],
[
"Ho",
"Mark K.",
""
],
[
"Callaway",
"Frederick",
""
],
[
"Daw",
"Nathaniel D.",
""
],
[
"Griffiths",
"Thomas L.",
""
]
] |
2211.03943 | Lynette Hirschman | Matthew Peterson, Tonia Korves, Christopher Garay, Robyn Kozierok and
Lynette Hirschman | Final Report on MITRE Evaluations for the DARPA Big Mechanism Program | 46 pages, 8 figures | null | null | MTR180593 | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This report presents the evaluation approach developed for the DARPA Big
Mechanism program, which aimed at developing computer systems that will read
research papers, integrate the information into a computer model of cancer
mechanisms, and frame new hypotheses. We employed an iterative, incremental
approach to the evaluation of the three phases of the program. In Phase I, we
evaluated the ability of system and human teams ability to read-with-a-model to
capture mechanistic information from the biomedical literature, integrated with
information from expert curated biological databases. In Phase II we evaluated
the ability of systems to assemble fragments of information into a mechanistic
model. The Phase III evaluation focused on the ability of systems to provide
explanations of experimental observations based on models assembled (largely
automatically) by the Big Mechanism process. The evaluation for each phase
built on earlier evaluations and guided developers towards creating
capabilities for the new phase. The report describes our approach, including
innovations such as a reference set (a curated data set limited to major
findings of each paper) to assess the accuracy of systems in extracting
mechanistic findings in the absence of a gold standard, and a method to
evaluate model-based explanations of experimental data. Results of the
evaluation and supporting materials are included in the appendices.
| [
{
"version": "v1",
"created": "Tue, 8 Nov 2022 01:37:07 GMT"
}
] | 1,667,952,000,000 | [
[
"Peterson",
"Matthew",
""
],
[
"Korves",
"Tonia",
""
],
[
"Garay",
"Christopher",
""
],
[
"Kozierok",
"Robyn",
""
],
[
"Hirschman",
"Lynette",
""
]
] |
2211.03950 | Qian Li | Qian Li, Shafiq Joty, Daling Wang, Shi Feng and Yifei Zhang | Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive
Learning | null | EMNLP Findings 2022 | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Sparsity of formal knowledge and roughness of non-ontological construction
make sparsity problem particularly prominent in Open Knowledge Graphs
(OpenKGs). Due to sparse links, learning effective representation for few-shot
entities becomes difficult. We hypothesize that by introducing negative
samples, a contrastive learning (CL) formulation could be beneficial in such
scenarios. However, existing CL methods model KG triplets as binary objects of
entities ignoring the relation-guided ternary propagation patterns and they are
too generic, i.e., they ignore zero-shot, few-shot and synonymity problems that
appear in OpenKGs. To address this, we propose TernaryCL, a CL framework based
on ternary propagation patterns among head, relation and tail. TernaryCL
designs Contrastive Entity and Contrastive Relation to mine ternary
discriminative features with both negative entities and relations, introduces
Contrastive Self to help zero- and few-shot entities learn discriminative
features, Contrastive Synonym to model synonymous entities, and Contrastive
Fusion to aggregate graph features from multiple paths. Extensive experiments
on benchmarks demonstrate the superiority of TernaryCL over state-of-the-art
models.
| [
{
"version": "v1",
"created": "Tue, 8 Nov 2022 01:52:05 GMT"
}
] | 1,667,952,000,000 | [
[
"Li",
"Qian",
""
],
[
"Joty",
"Shafiq",
""
],
[
"Wang",
"Daling",
""
],
[
"Feng",
"Shi",
""
],
[
"Zhang",
"Yifei",
""
]
] |
2211.04009 | Liang Peng | Liang Peng, Boqi Li, Wenhao Yu, Kai Yang, Wenbo Shao, and Hong Wang | SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for
Autonomous Driving | 16 pages, 10 figures, 2 tables, submitted to IEEE TITS | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Autonomous driving confronts great challenges in complex traffic scenarios,
where the risk of Safety of the Intended Functionality (SOTIF) can be triggered
by the dynamic operational environment and system insufficiencies. The SOTIF
risk is reflected not only intuitively in the collision risk with objects
outside the autonomous vehicles (AVs), but also inherently in the performance
limitation risk of the implemented algorithms themselves. How to minimize the
SOTIF risk for autonomous driving is currently a critical, difficult, and
unresolved issue. Therefore, this paper proposes the "Self-Surveillance and
Self-Adaption System" as a systematic approach to online minimize the SOTIF
risk, which aims to provide a systematic solution for monitoring,
quantification, and mitigation of inherent and external risks. The core of this
system is the risk monitoring of the implemented artificial intelligence
algorithms within the AV. As a demonstration of the Self-Surveillance and
Self-Adaption System, the risk monitoring of the perception algorithm, i.e.,
YOLOv5 is highlighted. Moreover, the inherent perception algorithm risk and
external collision risk are jointly quantified via SOTIF entropy, which is then
propagated downstream to the decision-making module and mitigated. Finally,
several challenging scenarios are demonstrated, and the Hardware-in-the-Loop
experiments are conducted to verify the efficiency and effectiveness of the
system. The results demonstrate that the Self-Surveillance and Self-Adaption
System enables dependable online monitoring, quantification, and mitigation of
SOTIF risk in real-time critical traffic environments.
| [
{
"version": "v1",
"created": "Tue, 8 Nov 2022 05:02:12 GMT"
}
] | 1,667,952,000,000 | [
[
"Peng",
"Liang",
""
],
[
"Li",
"Boqi",
""
],
[
"Yu",
"Wenhao",
""
],
[
"Yang",
"Kai",
""
],
[
"Shao",
"Wenbo",
""
],
[
"Wang",
"Hong",
""
]
] |
2211.04313 | Subarna Roy | Shubham, Avinash Arya, Subarna Roy, Sridhar Jonnala | An Ensemble-based approach for assigning text to correct Harmonized
system code | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Industries must follow government rules and regulations around the world to
classify products when assessing duties and taxes for international shipment.
Harmonized System (HS) is the most standardized numerical method of classifying
traded products among industry classification systems. A hierarchical ensemble
model comprising of Bert-transformer, NER, distance-based approaches, and
knowledge-graphs have been developed to address scalability, coverage, ability
to capture nuances, automation and auditing requirements when classifying
unknown text-descriptions as per HS method.
| [
{
"version": "v1",
"created": "Tue, 8 Nov 2022 15:32:36 GMT"
}
] | 1,667,952,000,000 | [
[
"Shubham",
"",
""
],
[
"Arya",
"Avinash",
""
],
[
"Roy",
"Subarna",
""
],
[
"Jonnala",
"Sridhar",
""
]
] |
2211.04976 | Emin Nakilcioglu | Emin Nakilcioglu, Anisa Rizvanolli und Olaf Rendel | Workload Forecasting of a Logistic Node Using Bayesian Neural Networks | null | Proceedings of the Hamburg International Conference of Logistics
33 (2022) 237-264 | 10.15480/882.4694 | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Purpose: Traffic volume in empty container depots has been highly volatile
due to external factors. Forecasting the expected container truck traffic along
with having a dynamic module to foresee the future workload plays a critical
role in improving the work efficiency. This paper studies the relevant
literature and designs a forecasting model addressing the aforementioned
issues. Methodology: The paper develops a forecasting model to predict hourly
work and traffic volume of container trucks in an empty container depot using a
Bayesian Neural Network based model. Furthermore, the paper experiments with
datasets with different characteristics to assess the model's forecasting range
for various data sources. Findings: The real data of an empty container depot
is utilized to develop a forecasting model and to later verify the capabilities
of the model. The findings show the performance validity of the model and
provide the groundwork to build an effective traffic and workload planning
system for the empty container depot in question. Originality: This paper
proposes a Bayesian deep learning-based forecasting model for traffic and
workload of an empty container depot using real-world data. This designed and
implemented forecasting model offers a solution with which every actor in the
container truck transportation benefits from the optimized workload.
| [
{
"version": "v1",
"created": "Wed, 9 Nov 2022 15:42:33 GMT"
}
] | 1,668,038,400,000 | [
[
"Nakilcioglu",
"Emin",
""
],
[
"Rendel",
"Anisa Rizvanolli und Olaf",
""
]
] |
2211.05180 | Sanda-Maria Avram Dr. | Sanda Maria Avram and Mihai Oltean | A comparison of several AI techniques for authorship attribution on
Romanian texts | We initially used the Accuracy evaluation tool to compute the
macro-accuracy, obtaining a value of 88.84%. We, thereafter discovered that
this value was erroneous and used other methods which gave us the value of
80.94% for the macro-accuracy. In this version of the paper we present the
python module solution by using sklearn.metrics's classification_report and
balanced_accuracy_score | Mathematics 2022, 10(23), 4589 | 10.3390/math10234589 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Determining the author of a text is a difficult task. Here we compare
multiple AI techniques for classifying literary texts written by multiple
authors by taking into account a limited number of speech parts (prepositions,
adverbs, and conjunctions). We also introduce a new dataset composed of texts
written in the Romanian language on which we have run the algorithms. The
compared methods are Artificial Neural Networks, Support Vector Machines, Multi
Expression Programming, Decision Trees with C5.0, and k-Nearest Neighbour.
Numerical experiments show, first of all, that the problem is difficult, but
some algorithms are able to generate decent errors on the test set.
| [
{
"version": "v1",
"created": "Wed, 9 Nov 2022 20:24:48 GMT"
},
{
"version": "v2",
"created": "Sat, 21 Jan 2023 10:53:55 GMT"
}
] | 1,674,604,800,000 | [
[
"Avram",
"Sanda Maria",
""
],
[
"Oltean",
"Mihai",
""
]
] |
2211.05423 | Motahare Namakin | Motahare Namakin, Modjtaba Rouhani, Mostafa Sabzekar | A metaheuristic multi-objective interaction-aware feature selection
method | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-objective feature selection is one of the most significant issues in
the field of pattern recognition. It is challenging because it maximizes the
classification performance and, at the same time, minimizes the number of
selected features, and the mentioned two objectives are usually conflicting. To
achieve a better Pareto optimal solution, metaheuristic optimization methods
are widely used in many studies. However, the main drawback is the exploration
of a large search space. Another problem with multi-objective feature selection
approaches is the interaction between features. Selecting correlated features
has negative effect on classification performance. To tackle these problems, we
present a novel multi-objective feature selection method that has several
advantages. Firstly, it considers the interaction between features using an
advanced probability scheme. Secondly, it is based on the Pareto Archived
Evolution Strategy (PAES) method that has several advantages such as simplicity
and its speed in exploring the solution space. However, we improve the
structure of PAES in such a way that generates the offsprings, intelligently.
Thus, the proposed method utilizes the introduced probability scheme to produce
more promising offsprings. Finally, it is equipped with a novel strategy that
guides it to find the optimum number of features through the process of
evolution. The experimental results show a significant improvement in finding
the optimal Pareto front compared to state-of-the-art methods on different
real-world datasets.
| [
{
"version": "v1",
"created": "Thu, 10 Nov 2022 08:56:48 GMT"
}
] | 1,668,124,800,000 | [
[
"Namakin",
"Motahare",
""
],
[
"Rouhani",
"Modjtaba",
""
],
[
"Sabzekar",
"Mostafa",
""
]
] |
2211.05457 | Anguo Dong | Anguo Dong, Cuiyun Gao, Yan Jia, Qing Liao, Xuan Wang, Lei Wang, and
Jing Xiao | Syntax-Guided Domain Adaptation for Aspect-based Sentiment Analysis | I want to withdraw this article due to personal reason | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Aspect-based sentiment analysis (ABSA) aims at extracting opinionated aspect
terms in review texts and determining their sentiment polarities, which is
widely studied in both academia and industry. As a fine-grained classification
task, the annotation cost is extremely high. Domain adaptation is a popular
solution to alleviate the data deficiency issue in new domains by transferring
common knowledge across domains. Most cross-domain ABSA studies are based on
structure correspondence learning (SCL), and use pivot features to construct
auxiliary tasks for narrowing down the gap between domains. However, their
pivot-based auxiliary tasks can only transfer knowledge of aspect terms but not
sentiment, limiting the performance of existing models. In this work, we
propose a novel Syntax-guided Domain Adaptation Model, named SDAM, for more
effective cross-domain ABSA. SDAM exploits syntactic structure similarities for
building pseudo training instances, during which aspect terms of target domain
are explicitly related to sentiment polarities. Besides, we propose a
syntax-based BERT mask language model for further capturing domain-invariant
features. Finally, to alleviate the sentiment inconsistency issue in multi-gram
aspect terms, we introduce a span-based joint aspect term and sentiment
analysis module into the cross-domain End2End ABSA. Experiments on five
benchmark datasets show that our model consistently outperforms the
state-of-the-art baselines with respect to Micro-F1 metric for the cross-domain
End2End ABSA task.
| [
{
"version": "v1",
"created": "Thu, 10 Nov 2022 10:09:33 GMT"
},
{
"version": "v2",
"created": "Wed, 16 Aug 2023 02:58:59 GMT"
}
] | 1,692,230,400,000 | [
[
"Dong",
"Anguo",
""
],
[
"Gao",
"Cuiyun",
""
],
[
"Jia",
"Yan",
""
],
[
"Liao",
"Qing",
""
],
[
"Wang",
"Xuan",
""
],
[
"Wang",
"Lei",
""
],
[
"Xiao",
"Jing",
""
]
] |
2211.05939 | Ayal Taitler | Ayal Taitler, Michael Gimelfarb, Jihwan Jeong, Sriram Gopalakrishnan,
Martin Mladenov, Xiaotian Liu, Scott Sanner | pyRDDLGym: From RDDL to Gym Environments | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gym
environments from RDDL declerative description. The discrete time step
evolution of variables in RDDL is described by conditional probability
functions, which fits naturally into the Gym step scheme. Furthermore, since
RDDL is a lifted description, the modification and scaling up of environments
to support multiple entities and different configurations becomes trivial
rather than a tedious process prone to errors. We hope that pyRDDLGym will
serve as a new wind in the reinforcement learning community by enabling easy
and rapid development of benchmarks due to the unique expressive power of RDDL.
By providing explicit access to the model in the RDDL description, pyRDDLGym
can also facilitate research on hybrid approaches for learning from interaction
while leveraging model knowledge. We present the design and built-in examples
of pyRDDLGym, and the additions made to the RDDL language that were
incorporated into the framework.
| [
{
"version": "v1",
"created": "Fri, 11 Nov 2022 00:58:16 GMT"
},
{
"version": "v2",
"created": "Mon, 14 Nov 2022 19:55:56 GMT"
},
{
"version": "v3",
"created": "Fri, 16 Dec 2022 23:43:52 GMT"
},
{
"version": "v4",
"created": "Wed, 19 Jul 2023 14:40:45 GMT"
},
{
"version": "v5",
"created": "Tue, 6 Feb 2024 00:25:23 GMT"
}
] | 1,707,264,000,000 | [
[
"Taitler",
"Ayal",
""
],
[
"Gimelfarb",
"Michael",
""
],
[
"Jeong",
"Jihwan",
""
],
[
"Gopalakrishnan",
"Sriram",
""
],
[
"Mladenov",
"Martin",
""
],
[
"Liu",
"Xiaotian",
""
],
[
"Sanner",
"Scott",
""
]
] |
2211.06011 | Yuanyuan Tian | Yuanyuan Tian, Wenwen Li | GeoAI for Knowledge Graph Construction: Identifying Causality Between
Cascading Events to Support Environmental Resilience Research | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Knowledge graph technology is considered a powerful and semantically enabled
solution to link entities, allowing users to derive new knowledge by reasoning
data according to various types of reasoning rules. However, in building such a
knowledge graph, events modeling, such as that of disasters, is often limited
to single, isolated events. The linkages among cascading events are often
missing in existing knowledge graphs. This paper introduces our GeoAI
(Geospatial Artificial Intelligence) solutions to identify causality among
events, in particular, disaster events, based on a set of spatially and
temporally-enabled semantic rules. Through a use case of causal disaster events
modeling, we demonstrated how these defined rules, including theme-based
identification of correlated events, spatiotemporal co-occurrence constraint,
and text mining of event metadata, enable the automatic extraction of causal
relationships between different events. Our solution enriches the event
knowledge base and allows for the exploration of linked cascading events in
large knowledge graphs, therefore empowering knowledge query and discovery.
| [
{
"version": "v1",
"created": "Fri, 11 Nov 2022 05:31:03 GMT"
}
] | 1,668,384,000,000 | [
[
"Tian",
"Yuanyuan",
""
],
[
"Li",
"Wenwen",
""
]
] |
2211.06154 | Ivan Sevillano-Garc\'ia | Iv\'an Sevillano-Garc\'ia, Juli\'an Luengo-Mart\'in and Francisco
Herrera | REVEL Framework to measure Local Linear Explanations for black-box
models: Deep Learning Image Classification case of study | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Explainable artificial intelligence is proposed to provide explanations for
reasoning performed by an Artificial Intelligence. There is no consensus on how
to evaluate the quality of these explanations, since even the definition of
explanation itself is not clear in the literature. In particular, for the
widely known Local Linear Explanations, there are qualitative proposals for the
evaluation of explanations, although they suffer from theoretical
inconsistencies. The case of image is even more problematic, where a visual
explanation seems to explain a decision while detecting edges is what it really
does. There are a large number of metrics in the literature specialized in
quantitatively measuring different qualitative aspects so we should be able to
develop metrics capable of measuring in a robust and correct way the desirable
aspects of the explanations. In this paper, we propose a procedure called REVEL
to evaluate different aspects concerning the quality of explanations with a
theoretically coherent development. This procedure has several advances in the
state of the art: it standardizes the concepts of explanation and develops a
series of metrics not only to be able to compare between them but also to
obtain absolute information regarding the explanation itself. The experiments
have been carried out on image four datasets as benchmark where we show REVEL's
descriptive and analytical power.
| [
{
"version": "v1",
"created": "Fri, 11 Nov 2022 12:15:36 GMT"
}
] | 1,668,384,000,000 | [
[
"Sevillano-García",
"Iván",
""
],
[
"Luengo-Martín",
"Julián",
""
],
[
"Herrera",
"Francisco",
""
]
] |
2211.06402 | Anjana Wijekoon | Anjana Wijekoon and David Corsar and Nirmalie Wiratunga | Behaviour Trees for Creating Conversational Explanation Experiences | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper presented an XAI system specification and an interactive dialogue
model to facilitate the creation of Explanation Experiences (EE). Such
specifications combine the knowledge of XAI, domain and system experts of a use
case to formalise target user groups and their explanation needs and to
implement explanation strategies to address those needs. Formalising the XAI
system promotes the reuse of existing explainers and known explanation needs
that can be refined and evolved over time using user evaluation feedback. The
abstract EE dialogue model formalised the interactions between a user and an
XAI system. The resulting EE conversational chatbot is personalised to an XAI
system at run-time using the knowledge captured in its XAI system
specification. This seamless integration is enabled by using Behaviour Trees
(BT) to conceptualise both the EE dialogue model and the explanation
strategies. In the evaluation, we discussed several desirable properties of
using BTs over traditionally used STMs or FSMs. BTs promote the reusability of
dialogue components through the hierarchical nature of the design. Sub-trees
are modular, i.e. a sub-tree is responsible for a specific behaviour, which can
be designed in different levels of granularity to improve human
interpretability. The EE dialogue model consists of abstract behaviours needed
to capture EE, accordingly, it can be implemented as a conversational,
graphical or text-based interface which caters to different domains and users.
There is a significant computational cost when using BTs for modelling
dialogue, which we mitigate by using memory. Overall, we find that the ability
to create robust conversational pathways dynamically makes BTs a good candidate
for designing and implementing conversation for creating explanation
experiences.
| [
{
"version": "v1",
"created": "Fri, 11 Nov 2022 18:39:38 GMT"
},
{
"version": "v2",
"created": "Fri, 6 Jan 2023 10:01:49 GMT"
}
] | 1,673,222,400,000 | [
[
"Wijekoon",
"Anjana",
""
],
[
"Corsar",
"David",
""
],
[
"Wiratunga",
"Nirmalie",
""
]
] |
2211.06561 | Peter Love E.D | Peter ED Love, Jane Matthews, Weili Fang, Stuart Porter, Hanbin Luo
and Lieyun Ding | Explainable Artificial Intelligence in Construction: The Content,
Context, Process, Outcome Evaluation Framework | 43 pages, 5 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Explainable artificial intelligence is an emerging and evolving concept. Its
impact on construction, though yet to be realised, will be profound in the
foreseeable future. Still, XAI has received limited attention in construction.
As a result, no evaluation frameworks have been propagated to enable
construction organisations to understand the what, why, how, and when of XAI.
Our paper aims to fill this void by developing a content, context, process, and
outcome evaluation framework that can be used to justify the adoption and
effective management of XAI. After introducing and describing this novel
framework, we discuss its implications for future research. While our novel
framework is conceptual, it provides a frame of reference for construction
organisations to make headway toward realising XAI business value and benefits.
| [
{
"version": "v1",
"created": "Sat, 12 Nov 2022 03:50:14 GMT"
}
] | 1,668,470,400,000 | [
[
"Love",
"Peter ED",
""
],
[
"Matthews",
"Jane",
""
],
[
"Fang",
"Weili",
""
],
[
"Porter",
"Stuart",
""
],
[
"Luo",
"Hanbin",
""
],
[
"Ding",
"Lieyun",
""
]
] |
2211.06579 | Peter Love E.D | Peter ED Love, Weili Fang, Jane Matthews, Stuart Porter, Hanbin Luo,
and Lieyun Ding | Explainable Artificial Intelligence: Precepts, Methods, and
Opportunities for Research in Construction | 56 pages, 3 figures. arXiv admin note: text overlap with
arXiv:1910.10045 by other authors | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Explainable artificial intelligence has received limited attention in
construction despite its growing importance in various other industrial
sectors. In this paper, we provide a narrative review of XAI to raise awareness
about its potential in construction. Our review develops a taxonomy of the XAI
literature comprising its precepts and approaches. Opportunities for future XAI
research focusing on stakeholder desiderata and data and information fusion are
identified and discussed. We hope the opportunities we suggest stimulate new
lines of inquiry to help alleviate the scepticism and hesitancy toward AI
adoption and integration in construction.
| [
{
"version": "v1",
"created": "Sat, 12 Nov 2022 05:47:42 GMT"
},
{
"version": "v2",
"created": "Sat, 11 Feb 2023 02:38:17 GMT"
}
] | 1,676,332,800,000 | [
[
"Love",
"Peter ED",
""
],
[
"Fang",
"Weili",
""
],
[
"Matthews",
"Jane",
""
],
[
"Porter",
"Stuart",
""
],
[
"Luo",
"Hanbin",
""
],
[
"Ding",
"Lieyun",
""
]
] |
2211.08467 | Matthias Hutsebaut-Buysse | Matthias Hutsebaut-Buysse, Kevin Mets, Tom De Schepper, Steven Latr\'e | Structured Exploration Through Instruction Enhancement for Object
Navigation | Paper accepted to the BNAIC/BeNeLearn 2022 conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Finding an object of a specific class in an unseen environment remains an
unsolved navigation problem. Hence, we propose a hierarchical learning-based
method for object navigation. The top-level is capable of high-level planning,
and building a memory on a floorplan-level (e.g., which room makes the most
sense for the agent to visit next, where has the agent already been?). While
the lower-level is tasked with efficiently navigating between rooms and looking
for objects in them. Instructions can be provided to the agent using a simple
synthetic language. The top-level intelligently enhances the instructions in
order to make the overall task more tractable. Language grounding, mapping
instructions to visual observations, is performed by utilizing an additional
separate supervised trained goal assessment module. We demonstrate the
effectiveness of our method on a dynamic configurable domestic environment.
| [
{
"version": "v1",
"created": "Tue, 15 Nov 2022 19:39:22 GMT"
}
] | 1,668,643,200,000 | [
[
"Hutsebaut-Buysse",
"Matthias",
""
],
[
"Mets",
"Kevin",
""
],
[
"De Schepper",
"Tom",
""
],
[
"Latré",
"Steven",
""
]
] |
2211.08671 | Zhening Li | Zhening Li, Gabriel Poesia, Omar Costilla-Reyes, Noah Goodman, Armando
Solar-Lezama | LEMMA: Bootstrapping High-Level Mathematical Reasoning with Learned
Symbolic Abstractions | 10 pages, 2 figures; to appear in 2nd MATH-AI Workshop at NeurIPS'22 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Humans tame the complexity of mathematical reasoning by developing
hierarchies of abstractions. With proper abstractions, solutions to hard
problems can be expressed concisely, thus making them more likely to be found.
In this paper, we propose Learning Mathematical Abstractions (LEMMA): an
algorithm that implements this idea for reinforcement learning agents in
mathematical domains. LEMMA augments Expert Iteration with an abstraction step,
where solutions found so far are revisited and rewritten in terms of new
higher-level actions, which then become available to solve new problems. We
evaluate LEMMA on two mathematical reasoning tasks--equation solving and
fraction simplification--in a step-by-step fashion. In these two domains, LEMMA
improves the ability of an existing agent, both solving more problems and
generalizing more effectively to harder problems than those seen during
training.
| [
{
"version": "v1",
"created": "Wed, 16 Nov 2022 04:59:08 GMT"
}
] | 1,668,643,200,000 | [
[
"Li",
"Zhening",
""
],
[
"Poesia",
"Gabriel",
""
],
[
"Costilla-Reyes",
"Omar",
""
],
[
"Goodman",
"Noah",
""
],
[
"Solar-Lezama",
"Armando",
""
]
] |
2211.09622 | Kevin Du | Kevin Du, Ian Gemp, Yi Wu, Yingying Wu | AlphaSnake: Policy Iteration on a Nondeterministic NP-hard Markov
Decision Process | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Reinforcement learning has recently been used to approach well-known NP-hard
combinatorial problems in graph theory. Among these problems, Hamiltonian cycle
problems are exceptionally difficult to analyze, even when restricted to
individual instances of structurally complex graphs. In this paper, we use
Monte Carlo Tree Search (MCTS), the search algorithm behind many
state-of-the-art reinforcement learning algorithms such as AlphaZero, to create
autonomous agents that learn to play the game of Snake, a game centered on
properties of Hamiltonian cycles on grid graphs. The game of Snake can be
formulated as a single-player discounted Markov Decision Process (MDP) where
the agent must behave optimally in a stochastic environment. Determining the
optimal policy for Snake, defined as the policy that maximizes the probability
of winning - or win rate - with higher priority and minimizes the expected
number of time steps to win with lower priority, is conjectured to be NP-hard.
Performance-wise, compared to prior work in the Snake game, our algorithm is
the first to achieve a win rate over $0.5$ (a uniform random policy achieves a
win rate $< 2.57 \times 10^{-15}$), demonstrating the versatility of AlphaZero
in approaching NP-hard environments.
| [
{
"version": "v1",
"created": "Thu, 17 Nov 2022 16:15:26 GMT"
}
] | 1,668,729,600,000 | [
[
"Du",
"Kevin",
""
],
[
"Gemp",
"Ian",
""
],
[
"Wu",
"Yi",
""
],
[
"Wu",
"Yingying",
""
]
] |
2211.09752 | Yuanshun Yao | Yuanshun Yao, Chong Wang, Hang Li | Learning to Counterfactually Explain Recommendations | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Recommender system practitioners are facing increasing pressure to explain
recommendations. We explore how to explain recommendations using counterfactual
logic, i.e. "Had you not interacted with the following items, we would not
recommend it." Compared to the traditional explanation logic, counterfactual
explanations are easier to understand, more technically verifiable, and more
informative in terms of giving users control over recommendations. The major
challenge of generating such explanations is the computational cost because it
requires repeatedly retraining the models to obtain the effect on a
recommendation caused by the absence of user history. We propose a
learning-based framework to generate counterfactual explanations. The key idea
is to train a surrogate model to learn the effect of removing a subset of user
history on the recommendation. To this end, we first artificially simulate the
counterfactual outcomes on the recommendation after deleting subsets of
history. Then we train a surrogate model to learn the mapping between a history
deletion and the corresponding change of the recommendation caused by the
deletion. Finally, to generate an explanation, we find the history subset
predicted by the surrogate model that is most likely to remove the
recommendation. Through offline experiments and online user studies, we show
our method, compared to baselines, can generate explanations that are more
counterfactually valid and more satisfactory considered by users.
| [
{
"version": "v1",
"created": "Thu, 17 Nov 2022 18:21:21 GMT"
},
{
"version": "v2",
"created": "Wed, 8 Feb 2023 19:03:41 GMT"
}
] | 1,675,987,200,000 | [
[
"Yao",
"Yuanshun",
""
],
[
"Wang",
"Chong",
""
],
[
"Li",
"Hang",
""
]
] |
2211.10011 | Sumin Seo | Sumin Seo, Heeseon Cheon, Hyunho Kim, Dongseok Hyun | Structural Quality Metrics to Evaluate Knowledge Graphs | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This work presents six structural quality metrics that can measure the
quality of knowledge graphs and analyzes five cross-domain knowledge graphs on
the web (Wikidata, DBpedia, YAGO, Google Knowledge Graph, Freebase) as well as
'Raftel', Naver's integrated knowledge graph. The 'Good Knowledge Graph' should
define detailed classes and properties in its ontology so that knowledge in the
real world can be expressed abundantly. Also, instances and RDF triples should
use the classes and properties actively. Therefore, we tried to examine the
internal quality of knowledge graphs numerically by focusing on the structure
of the ontology, which is the schema of knowledge graphs, and the degree of use
thereof. As a result of the analysis, it was possible to find the
characteristics of a knowledge graph that could not be known only by
scale-related indicators such as the number of classes and properties.
| [
{
"version": "v1",
"created": "Fri, 18 Nov 2022 03:26:09 GMT"
},
{
"version": "v2",
"created": "Fri, 9 Dec 2022 09:50:53 GMT"
}
] | 1,670,803,200,000 | [
[
"Seo",
"Sumin",
""
],
[
"Cheon",
"Heeseon",
""
],
[
"Kim",
"Hyunho",
""
],
[
"Hyun",
"Dongseok",
""
]
] |
2211.10085 | Mingyu Kang | Mingyu Kang and Duxin Chen and Ning Meng and Gang Yan and Wenwu Yu | Identifying Unique Causal Network from Nonstationary Time Series | This manuscript are submitted so that other researchers can follow.
The code of this work is available at: https://github.com/KMY-SEU/HCE. Many
thanks for supports! | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Identifying causality is a challenging task in many data-intensive scenarios.
Many algorithms have been proposed for this critical task. However, most of
them consider the learning algorithms for directed acyclic graph (DAG) of
Bayesian network (BN). These BN-based models only have limited causal
explainability because of the issue of Markov equivalence class. Moreover, they
are dependent on the assumption of stationarity, whereas many sampling time
series from complex system are nonstationary. The nonstationary time series
bring dataset shift problem, which leads to the unsatisfactory performances of
these algorithms. To fill these gaps, a novel causation model named Unique
Causal Network (UCN) is proposed in this paper. Different from the previous
BN-based models, UCN considers the influence of time delay, and proves the
uniqueness of obtained network structure, which addresses the issue of Markov
equivalence class. Furthermore, based on the decomposability property of UCN, a
higher-order causal entropy (HCE) algorithm is designed to identify the
structure of UCN in a distributed way. HCE algorithm measures the strength of
causality by using nearest-neighbors entropy estimator, which works well on
nonstationary time series. Finally, lots of experiments validate that HCE
algorithm achieves state-of-the-art accuracy when time series are
nonstationary, compared to the other baseline algorithms.
| [
{
"version": "v1",
"created": "Fri, 18 Nov 2022 08:28:54 GMT"
},
{
"version": "v2",
"created": "Mon, 21 Nov 2022 15:02:10 GMT"
},
{
"version": "v3",
"created": "Wed, 30 Aug 2023 00:46:21 GMT"
}
] | 1,693,440,000,000 | [
[
"Kang",
"Mingyu",
""
],
[
"Chen",
"Duxin",
""
],
[
"Meng",
"Ning",
""
],
[
"Yan",
"Gang",
""
],
[
"Yu",
"Wenwu",
""
]
] |
2211.10298 | Siddhant Bhambri | Siddhant Bhambri, Amrita Bhattacharjee, Dimitri Bertsekas | Reinforcement Learning Methods for Wordle: A POMDP/Adaptive Control
Approach | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper we address the solution of the popular Wordle puzzle, using new
reinforcement learning methods, which apply more generally to adaptive control
of dynamic systems and to classes of Partially Observable Markov Decision
Process (POMDP) problems. These methods are based on approximation in value
space and the rollout approach, admit a straightforward implementation, and
provide improved performance over various heuristic approaches. For the Wordle
puzzle, they yield on-line solution strategies that are very close to optimal
at relatively modest computational cost. Our methods are viable for more
complex versions of Wordle and related search problems, for which an optimal
strategy would be impossible to compute. They are also applicable to a wide
range of adaptive sequential decision problems that involve an unknown or
frequently changing environment whose parameters are estimated on-line.
| [
{
"version": "v1",
"created": "Tue, 15 Nov 2022 03:46:41 GMT"
},
{
"version": "v2",
"created": "Mon, 21 Nov 2022 17:52:32 GMT"
},
{
"version": "v3",
"created": "Tue, 22 Nov 2022 02:05:23 GMT"
},
{
"version": "v4",
"created": "Tue, 29 Nov 2022 19:26:11 GMT"
}
] | 1,669,852,800,000 | [
[
"Bhambri",
"Siddhant",
""
],
[
"Bhattacharjee",
"Amrita",
""
],
[
"Bertsekas",
"Dimitri",
""
]
] |
2211.11281 | Ting Yu | Shiqiang Zhu, Ting Yu, Tao Xu, Hongyang Chen, Schahram Dustdar,
Sylvain Gigan, Deniz Gunduz, Ekram Hossain, Yaochu Jin, Feng Lin, Bo Liu,
Zhiguo Wan, Ji Zhang, Zhifeng Zhao, Wentao Zhu, Zuoning Chen, Tariq Durrani,
Huaimin Wang, Jiangxing Wu, Tongyi Zhang, Yunhe Pan | Intelligent Computing: The Latest Advances, Challenges and Future | null | Intell. Comput. 2023;2:0006 | 10.34133/icomputing.0006 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computing is a critical driving force in the development of human
civilization. In recent years, we have witnessed the emergence of intelligent
computing, a new computing paradigm that is reshaping traditional computing and
promoting digital revolution in the era of big data, artificial intelligence
and internet-of-things with new computing theories, architectures, methods,
systems, and applications. Intelligent computing has greatly broadened the
scope of computing, extending it from traditional computing on data to
increasingly diverse computing paradigms such as perceptual intelligence,
cognitive intelligence, autonomous intelligence, and human-computer fusion
intelligence. Intelligence and computing have undergone paths of different
evolution and development for a long time but have become increasingly
intertwined in recent years: intelligent computing is not only
intelligence-oriented but also intelligence-driven. Such cross-fertilization
has prompted the emergence and rapid advancement of intelligent computing.
Intelligent computing is still in its infancy and an abundance of innovations
in the theories, systems, and applications of intelligent computing are
expected to occur soon. We present the first comprehensive survey of literature
on intelligent computing, covering its theory fundamentals, the technological
fusion of intelligence and computing, important applications, challenges, and
future perspectives. We believe that this survey is highly timely and will
provide a comprehensive reference and cast valuable insights into intelligent
computing for academic and industrial researchers and practitioners.
| [
{
"version": "v1",
"created": "Mon, 21 Nov 2022 09:15:13 GMT"
}
] | 1,713,398,400,000 | [
[
"Zhu",
"Shiqiang",
""
],
[
"Yu",
"Ting",
""
],
[
"Xu",
"Tao",
""
],
[
"Chen",
"Hongyang",
""
],
[
"Dustdar",
"Schahram",
""
],
[
"Gigan",
"Sylvain",
""
],
[
"Gunduz",
"Deniz",
""
],
[
"Hossain",
"Ekram",
""
],
[
"Jin",
"Yaochu",
""
],
[
"Lin",
"Feng",
""
],
[
"Liu",
"Bo",
""
],
[
"Wan",
"Zhiguo",
""
],
[
"Zhang",
"Ji",
""
],
[
"Zhao",
"Zhifeng",
""
],
[
"Zhu",
"Wentao",
""
],
[
"Chen",
"Zuoning",
""
],
[
"Durrani",
"Tariq",
""
],
[
"Wang",
"Huaimin",
""
],
[
"Wu",
"Jiangxing",
""
],
[
"Zhang",
"Tongyi",
""
],
[
"Pan",
"Yunhe",
""
]
] |
2211.11650 | Zihan Ye | Zihan Ye, Hikaru Shindo, Devendra Singh Dhami, Kristian Kersting | Neural Meta-Symbolic Reasoning and Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep neural learning uses an increasing amount of computation and data to
solve very specific problems. By stark contrast, human minds solve a wide range
of problems using a fixed amount of computation and limited experience. One
ability that seems crucial to this kind of general intelligence is
meta-reasoning, i.e., our ability to reason about reasoning. To make deep
learning do more from less, we propose the first neural meta-symbolic system
(NEMESYS) for reasoning and learning: meta programming using differentiable
forward-chaining reasoning in first-order logic. Differentiable meta
programming naturally allows NEMESYS to reason and learn several tasks
efficiently. This is different from performing object-level deep reasoning and
learning, which refers in some way to entities external to the system. In
contrast, NEMESYS enables self-introspection, lifting from object- to
meta-level reasoning and vice versa. In our extensive experiments, we
demonstrate that NEMESYS can solve different kinds of tasks by adapting the
meta-level programs without modifying the internal reasoning system. Moreover,
we show that NEMESYS can learn meta-level programs given examples. This is
difficult, if not impossible, for standard differentiable logic programming
| [
{
"version": "v1",
"created": "Mon, 21 Nov 2022 17:12:06 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Dec 2023 21:19:11 GMT"
}
] | 1,702,944,000,000 | [
[
"Ye",
"Zihan",
""
],
[
"Shindo",
"Hikaru",
""
],
[
"Dhami",
"Devendra Singh",
""
],
[
"Kersting",
"Kristian",
""
]
] |
2211.12006 | Xuan Wu | Xuan Wu, Xinhao Zhu, Yizheng Zhao, Xinyu Dai | Differentiable Fuzzy $\mathcal{ALC}$: A Neural-Symbolic Representation
Language for Symbol Grounding | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural-symbolic computing aims at integrating robust neural learning and
sound symbolic reasoning into a single framework, so as to leverage the
complementary strengths of both of these, seemingly unrelated (maybe even
contradictory) AI paradigms. The central challenge in neural-symbolic computing
is to unify the formulation of neural learning and symbolic reasoning into a
single framework with common semantics, that is, to seek a joint representation
between a neural model and a logical theory that can support the basic
grounding learned by the neural model and also stick to the semantics of the
logical theory. In this paper, we propose differentiable fuzzy $\mathcal{ALC}$
(DF-$\mathcal{ALC}$) for this role, as a neural-symbolic representation
language with the desired semantics. DF-$\mathcal{ALC}$ unifies the description
logic $\mathcal{ALC}$ and neural models for symbol grounding; in particular, it
infuses an $\mathcal{ALC}$ knowledge base into neural models through
differentiable concept and role embeddings. We define a hierarchical loss to
the constraint that the grounding learned by neural models must be semantically
consistent with $\mathcal{ALC}$ knowledge bases. And we find that capturing the
semantics in grounding solely by maximizing satisfiability cannot revise
grounding rationally. We further define a rule-based loss for DF adapting to
symbol grounding problems. The experiment results show that DF-$\mathcal{ALC}$
with rule-based loss can improve the performance of image object detectors in
an unsupervised learning way, even in low-resource situations.
| [
{
"version": "v1",
"created": "Tue, 22 Nov 2022 04:54:20 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Dec 2022 05:57:44 GMT"
}
] | 1,669,939,200,000 | [
[
"Wu",
"Xuan",
""
],
[
"Zhu",
"Xinhao",
""
],
[
"Zhao",
"Yizheng",
""
],
[
"Dai",
"Xinyu",
""
]
] |
2211.12270 | Riccardo Massidda | Riccardo Massidda, Atticus Geiger, Thomas Icard, Davide Bacciu | Causal Abstraction with Soft Interventions | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Causal abstraction provides a theory describing how several causal models can
represent the same system at different levels of detail. Existing theoretical
proposals limit the analysis of abstract models to "hard" interventions fixing
causal variables to be constant values. In this work, we extend causal
abstraction to "soft" interventions, which assign possibly non-constant
functions to variables without adding new causal connections. Specifically, (i)
we generalize $\tau$-abstraction from Beckers and Halpern (2019) to soft
interventions, (ii) we propose a further definition of soft abstraction to
ensure a unique map $\omega$ between soft interventions, and (iii) we prove
that our constructive definition of soft abstraction guarantees the
intervention map $\omega$ has a specific and necessary explicit form.
| [
{
"version": "v1",
"created": "Tue, 22 Nov 2022 13:42:43 GMT"
}
] | 1,669,161,600,000 | [
[
"Massidda",
"Riccardo",
""
],
[
"Geiger",
"Atticus",
""
],
[
"Icard",
"Thomas",
""
],
[
"Bacciu",
"Davide",
""
]
] |
2211.12560 | Adam Hepworth | Adam Hepworth, Aya Hussein, Darryn Reid, Hussein Abbass | Contextually Aware Intelligent Control Agents for Heterogeneous Swarms | 37 pages, 3 figures, 11 tables | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | An emerging challenge in swarm shepherding research is to design effective
and efficient artificial intelligence algorithms that maintain a
low-computational ceiling while increasing the swarm's abilities to operate in
diverse contexts. We propose a methodology to design a context-aware
swarm-control intelligent agent. The intelligent control agent (shepherd) first
uses swarm metrics to recognise the type of swarm it interacts with to then
select a suitable parameterisation from its behavioural library for that
particular swarm type. The design principle of our methodology is to increase
the situation awareness (i.e. information contents) of the control agent
without sacrificing the low-computational cost necessary for efficient swarm
control. We demonstrate successful shepherding in both homogeneous and
heterogeneous swarms.
| [
{
"version": "v1",
"created": "Tue, 22 Nov 2022 20:25:59 GMT"
}
] | 1,669,248,000,000 | [
[
"Hepworth",
"Adam",
""
],
[
"Hussein",
"Aya",
""
],
[
"Reid",
"Darryn",
""
],
[
"Abbass",
"Hussein",
""
]
] |
2211.13315 | Kumar Sankar Ray | Kumar Sankar Ray | Bayesian Brain: Computation with Perception to Recognize 3D Objects | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We mimic the cognitive ability of Human perception, based on Bayesian
hypothesis, to recognize view-based 3D objects. We consider approximate
Bayesian (Empirical Bayesian) for perceptual inference for recognition. We
essentially handle computation with perception.
| [
{
"version": "v1",
"created": "Wed, 23 Nov 2022 21:33:57 GMT"
}
] | 1,669,593,600,000 | [
[
"Ray",
"Kumar Sankar",
""
]
] |
2211.13469 | Haoran Luo | Haoran Luo, Haihong E, Yuhao Yang, Gengxian Zhou, Yikai Guo, Tianyu
Yao, Zichen Tang, Xueyuan Lin, Kaiyang Wan | NQE: N-ary Query Embedding for Complex Query Answering over
Hyper-Relational Knowledge Graphs | Accepted by AAAI 2023 | AAAI 2023 | 10.1609/aaai.v37i4.25576 | 7903 | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Complex query answering (CQA) is an essential task for multi-hop and logical
reasoning on knowledge graphs (KGs). Currently, most approaches are limited to
queries among binary relational facts and pay less attention to n-ary facts
(n>=2) containing more than two entities, which are more prevalent in the real
world. Moreover, previous CQA methods can only make predictions for a few given
types of queries and cannot be flexibly extended to more complex logical
queries, which significantly limits their applications. To overcome these
challenges, in this work, we propose a novel N-ary Query Embedding (NQE) model
for CQA over hyper-relational knowledge graphs (HKGs), which include massive
n-ary facts. The NQE utilizes a dual-heterogeneous Transformer encoder and
fuzzy logic theory to satisfy all n-ary FOL queries, including existential
quantifiers, conjunction, disjunction, and negation. We also propose a parallel
processing algorithm that can train or predict arbitrary n-ary FOL queries in a
single batch, regardless of the kind of each query, with good flexibility and
extensibility. In addition, we generate a new CQA dataset WD50K-NFOL, including
diverse n-ary FOL queries over WD50K. Experimental results on WD50K-NFOL and
other standard CQA datasets show that NQE is the state-of-the-art CQA method
over HKGs with good generalization capability. Our code and dataset are
publicly available.
| [
{
"version": "v1",
"created": "Thu, 24 Nov 2022 08:26:18 GMT"
},
{
"version": "v2",
"created": "Fri, 24 Feb 2023 17:12:38 GMT"
},
{
"version": "v3",
"created": "Fri, 31 Mar 2023 21:54:52 GMT"
}
] | 1,697,500,800,000 | [
[
"Luo",
"Haoran",
""
],
[
"E",
"Haihong",
""
],
[
"Yang",
"Yuhao",
""
],
[
"Zhou",
"Gengxian",
""
],
[
"Guo",
"Yikai",
""
],
[
"Yao",
"Tianyu",
""
],
[
"Tang",
"Zichen",
""
],
[
"Lin",
"Xueyuan",
""
],
[
"Wan",
"Kaiyang",
""
]
] |
2211.14405 | Yong Gao | Congsong Zhang and Yong Gao and James Nastos | Learning Branching Heuristics from Graph Neural Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Backtracking has been widely used for solving problems in artificial
intelligence (AI), including constraint satisfaction problems and combinatorial
optimization problems. Good branching heuristics can efficiently improve the
performance of backtracking by helping prune the search space and leading the
search to the most promising direction. In this paper, we first propose a new
graph neural network (GNN) model designed using the probabilistic method. From
the GNN model, we introduce an approach to learn a branching heuristic for
combinatorial optimization problems. In particular, our GNN model learns
appropriate probability distributions on vertices in given graphs from which
the branching heuristic is extracted and used in a backtracking search. Our
experimental results for the (minimum) dominating-clique problem show that this
learned branching heuristic performs better than the minimum-remaining-values
heuristic in terms of the number of branches of the whole search tree. Our
approach introduces a new way of applying GNNs towards enhancing the classical
backtracking algorithm used in AI.
| [
{
"version": "v1",
"created": "Sat, 26 Nov 2022 00:01:01 GMT"
}
] | 1,669,680,000,000 | [
[
"Zhang",
"Congsong",
""
],
[
"Gao",
"Yong",
""
],
[
"Nastos",
"James",
""
]
] |
2211.14409 | Ryo Kuroiwa | Ryo Kuroiwa and J. Christopher Beck | Domain-Independent Dynamic Programming: Generic State Space Search for
Combinatorial Optimization | This paper was accepted at the 33rd International Conference on
Automated Planning and Scheduling (ICAPS) 2023 | Proceedings of the International Conference on Automated Planning
and Scheduling, 33(1), 2023, 236-244 | 10.1609/icaps.v33i1.27200 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For combinatorial optimization problems, model-based approaches such as
mixed-integer programming (MIP) and constraint programming (CP) aim to decouple
modeling and solving a problem: the 'holy grail' of declarative problem
solving. We propose domain-independent dynamic programming (DIDP), a new
model-based paradigm based on dynamic programming (DP). While DP is not new, it
has typically been implemented as a problem-specific method. We propose Dynamic
Programming Description Language (DyPDL), a formalism to define DP models, and
develop Cost-Algebraic A* Solver for DyPDL (CAASDy), a generic solver for DyPDL
using state space search. We formalize existing problem-specific DP and state
space search methods for combinatorial optimization problems as DP models in
DyPDL. Using CAASDy and commercial MIP and CP solvers, we experimentally
compare the DP models with existing MIP and CP models, showing that, despite
its nascent nature, CAASDy outperforms MIP and CP on a number of common problem
classes.
| [
{
"version": "v1",
"created": "Sat, 26 Nov 2022 00:15:45 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Mar 2023 19:35:47 GMT"
}
] | 1,706,227,200,000 | [
[
"Kuroiwa",
"Ryo",
""
],
[
"Beck",
"J. Christopher",
""
]
] |
2211.14422 | Lei Kou | Quande Yuan, Yuzhen Pi, Lei Kou, Fangfang Zhang, Bo Ye | Quantitative Method for Security Situation of the Power Information
Network Based on the Evolutionary Neural Network | Frontiers in Energy Research | null | 10.3389/fenrg.2022.88535 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cybersecurity is the security cornerstone of digital transformation of the
power grid and construction of new power systems. The traditional network
security situation quantification method only analyzes from the perspective of
network performance, ignoring the impact of various power application services
on the security situation, so the quantification results cannot fully reflect
the power information network risk state. This study proposes a method for
quantifying security situation of the power information network based on the
evolutionary neural network. First, the security posture system architecture is
designed by analyzing the business characteristics of power information network
applications. Second, combining the importance of power application business,
the spatial element index system of coupled interconnection is established from
three dimensions of network reliability, threat, and vulnerability. Then, the
BP neural network optimized by the genetic evolutionary algorithm is
incorporated into the element index calculation process, and the quantitative
model of security posture of the power information network based on the
evolutionary neural network is constructed. Finally, a simulation experiment
environment is built according to a power sector network topology, and the
effectiveness and robustness of the method proposed in the study are verified.
| [
{
"version": "v1",
"created": "Sat, 26 Nov 2022 01:05:35 GMT"
}
] | 1,669,680,000,000 | [
[
"Yuan",
"Quande",
""
],
[
"Pi",
"Yuzhen",
""
],
[
"Kou",
"Lei",
""
],
[
"Zhang",
"Fangfang",
""
],
[
"Ye",
"Bo",
""
]
] |
2211.14492 | Yuan Sun Dr | Yuan Sun, Su Nguyen, Dhananjay Thiruvady, Xiaodong Li, Andreas T.
Ernst and Uwe Aickelin | Enhancing Constraint Programming via Supervised Learning for Job Shop
Scheduling | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Constraint programming (CP) is a powerful technique for solving constraint
satisfaction and optimization problems. In CP solvers, the variable ordering
strategy used to select which variable to explore first in the solving process
has a significant impact on solver effectiveness. To address this issue, we
propose a novel variable ordering strategy based on supervised learning, which
we evaluate in the context of job shop scheduling problems. Our learning-based
methods predict the optimal solution of a problem instance and use the
predicted solution to order variables for CP solvers. \added[]{Unlike
traditional variable ordering methods, our methods can learn from the
characteristics of each problem instance and customize the variable ordering
strategy accordingly, leading to improved solver performance.} Our experiments
demonstrate that training machine learning models is highly efficient and can
achieve high accuracy. Furthermore, our learned variable ordering methods
perform competitively when compared to four existing methods. Finally, we
demonstrate that hybridising the machine learning-based variable ordering
methods with traditional domain-based methods is beneficial.
| [
{
"version": "v1",
"created": "Sat, 26 Nov 2022 06:30:28 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Apr 2023 07:20:31 GMT"
}
] | 1,681,344,000,000 | [
[
"Sun",
"Yuan",
""
],
[
"Nguyen",
"Su",
""
],
[
"Thiruvady",
"Dhananjay",
""
],
[
"Li",
"Xiaodong",
""
],
[
"Ernst",
"Andreas T.",
""
],
[
"Aickelin",
"Uwe",
""
]
] |
2211.14541 | Vladimir Poliakov | Vladimir Poliakov and Kenan Niu and Emmanuel Vander Poorten and
Dzmitry Tsetserukou | RL-Based Guidance in Outpatient Hysteroscopy Training: A Feasibility
Study | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This work presents an RL-based agent for outpatient hysteroscopy training.
Hysteroscopy is a gynecological procedure for examination of the uterine
cavity. Recent advancements enabled performing this type of intervention in the
outpatient setup without anaesthesia. While being beneficial to the patient,
this approach introduces new challenges for clinicians, who should take
additional measures to maintain the level of patient comfort and prevent tissue
damage. Our prior work has presented a platform for hysteroscopic training with
the focus on the passage of the cervical canal. With this work, we aim to
extend the functionality of the platform by designing a subsystem that
autonomously performs the task of the passage of the cervical canal. This
feature can later be used as a virtual instructor to provide educational cues
for trainees and assess their performance. The developed algorithm is based on
the soft actor critic approach to smooth the learning curve of the agent and
ensure uniform exploration of the workspace. The designed algorithm was tested
against the performance of five clinicians. Overall, the algorithm demonstrated
high efficiency and reliability, succeeding in 98% of trials and outperforming
the expert group in three out of four measured metrics.
| [
{
"version": "v1",
"created": "Sat, 26 Nov 2022 11:16:17 GMT"
}
] | 1,669,680,000,000 | [
[
"Poliakov",
"Vladimir",
""
],
[
"Niu",
"Kenan",
""
],
[
"Poorten",
"Emmanuel Vander",
""
],
[
"Tsetserukou",
"Dzmitry",
""
]
] |
2211.14673 | Charles Lovering J | Charles Lovering, Jessica Zosa Forde, George Konidaris, Ellie Pavlick,
Michael L. Littman | Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in
Hex | 10 pages, Neural Information Processing Systems 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | AlphaZero, an approach to reinforcement learning that couples neural networks
and Monte Carlo tree search (MCTS), has produced state-of-the-art strategies
for traditional board games like chess, Go, shogi, and Hex. While researchers
and game commentators have suggested that AlphaZero uses concepts that humans
consider important, it is unclear how these concepts are captured in the
network. We investigate AlphaZero's internal representations in the game of Hex
using two evaluation techniques from natural language processing (NLP): model
probing and behavioral tests. In doing so, we introduce new evaluation tools to
the RL community and illustrate how evaluations other than task performance can
be used to provide a more complete picture of a model's strengths and
weaknesses. Our analyses in the game of Hex reveal interesting patterns and
generate some testable hypotheses about how such models learn in general. For
example, we find that MCTS discovers concepts before the neural network learns
to encode them. We also find that concepts related to short-term end-game
planning are best encoded in the final layers of the model, whereas concepts
related to long-term planning are encoded in the middle layers of the model.
| [
{
"version": "v1",
"created": "Sat, 26 Nov 2022 21:59:11 GMT"
}
] | 1,669,680,000,000 | [
[
"Lovering",
"Charles",
""
],
[
"Forde",
"Jessica Zosa",
""
],
[
"Konidaris",
"George",
""
],
[
"Pavlick",
"Ellie",
""
],
[
"Littman",
"Michael L.",
""
]
] |
2211.14987 | Jia-Qi Lin | Jia-Qi Lin, Man-Sheng Chen, Xi-Ran Zhu, Chang-Dong Wang, Haizhang
Zhang | Dual Information Enhanced Multi-view Attributed Graph Clustering | 11 pages, 4 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-view attributed graph clustering is an important approach to partition
multi-view data based on the attribute feature and adjacent matrices from
different views. Some attempts have been made in utilizing Graph Neural Network
(GNN), which have achieved promising clustering performance. Despite this, few
of them pay attention to the inherent specific information embedded in multiple
views. Meanwhile, they are incapable of recovering the latent high-level
representation from the low-level ones, greatly limiting the downstream
clustering performance. To fill these gaps, a novel Dual Information enhanced
multi-view Attributed Graph Clustering (DIAGC) method is proposed in this
paper. Specifically, the proposed method introduces the Specific Information
Reconstruction (SIR) module to disentangle the explorations of the consensus
and specific information from multiple views, which enables GCN to capture the
more essential low-level representations. Besides, the Mutual Information
Maximization (MIM) module maximizes the agreement between the latent high-level
representation and low-level ones, and enables the high-level representation to
satisfy the desired clustering structure with the help of the Self-supervised
Clustering (SC) module. Extensive experiments on several real-world benchmarks
demonstrate the effectiveness of the proposed DIAGC method compared with the
state-of-the-art baselines.
| [
{
"version": "v1",
"created": "Mon, 28 Nov 2022 01:18:04 GMT"
}
] | 1,669,680,000,000 | [
[
"Lin",
"Jia-Qi",
""
],
[
"Chen",
"Man-Sheng",
""
],
[
"Zhu",
"Xi-Ran",
""
],
[
"Wang",
"Chang-Dong",
""
],
[
"Zhang",
"Haizhang",
""
]
] |
2211.15234 | Ruitian Wu | Jingwei Li, Ruitian Wu, Tzu-liang Huang, Zian Pan, Ming-chun Huang | Shoupa: An AI System for Early Diagnosis of Parkinson's Disease | 2 pages, 1 figure, accepted by IEEE/ACM CHASE 2022 (Poster
Presentation) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Parkinson's Disease (PD) is a progressive nervous system disorder that has
affected more than 5.8 million people, especially the elderly. Due to the
complexity of its symptoms and its similarity to other neurological disorders,
early detection requires neurologists or PD specialists to be involved, which
is not accessible to most old people. Therefore, we integrate smart mobile
devices with AI technologies. In this paper, we introduce the framework of our
developed PD early detection system which combines different tasks evaluating
both motor and non-motor symptoms. With the developed model, we help users
detect PD punctually in non-clinical settings and figure out their most severe
symptoms. The results are expected to be further used for PD rehabilitation
guidance and detection of other neurological disorders.
| [
{
"version": "v1",
"created": "Mon, 28 Nov 2022 11:32:17 GMT"
}
] | 1,669,680,000,000 | [
[
"Li",
"Jingwei",
""
],
[
"Wu",
"Ruitian",
""
],
[
"Huang",
"Tzu-liang",
""
],
[
"Pan",
"Zian",
""
],
[
"Huang",
"Ming-chun",
""
]
] |
2211.15271 | Eva Cetinic | Eva Cetinic | The Myth of Culturally Agnostic AI Models | Accepted for "Cultures in AI/AI in Culture" NeurIPS 2022 Workshop | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The paper discusses the potential of large vision-language models as objects
of interest for empirical cultural studies. Focusing on the comparative
analysis of outputs from two popular text-to-image synthesis models, DALL-E 2
and Stable Diffusion, the paper tries to tackle the pros and cons of striving
towards culturally agnostic vs. culturally specific AI models. The paper
discusses several examples of memorization and bias in generated outputs which
showcase the trade-off between risk mitigation and cultural specificity, as
well as the overall impossibility of developing culturally agnostic models.
| [
{
"version": "v1",
"created": "Mon, 28 Nov 2022 12:54:34 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Nov 2022 11:22:38 GMT"
}
] | 1,669,766,400,000 | [
[
"Cetinic",
"Eva",
""
]
] |
2211.15324 | Yinan Liu | Yinan Liu and Hu Chen and Wei Shen and Jiaoyan Chen | Low-resource Personal Attribute Prediction from Conversation | Accepted by AAAI'23 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Personal knowledge bases (PKBs) are crucial for a broad range of applications
such as personalized recommendation and Web-based chatbots. A critical
challenge to build PKBs is extracting personal attribute knowledge from users'
conversation data. Given some users of a conversational system, a personal
attribute and these users' utterances, our goal is to predict the ranking of
the given personal attribute values for each user. Previous studies often rely
on a relative number of resources such as labeled utterances and external data,
yet the attribute knowledge embedded in unlabeled utterances is underutilized
and their performance of predicting some difficult personal attributes is still
unsatisfactory. In addition, it is found that some text classification methods
could be employed to resolve this task directly. However, they also perform not
well over those difficult personal attributes. In this paper, we propose a
novel framework PEARL to predict personal attributes from conversations by
leveraging the abundant personal attribute knowledge from utterances under a
low-resource setting in which no labeled utterances or external data are
utilized. PEARL combines the biterm semantic information with the word
co-occurrence information seamlessly via employing the updated prior attribute
knowledge to refine the biterm topic model's Gibbs sampling process in an
iterative manner. The extensive experimental results show that PEARL
outperforms all the baseline methods not only on the task of personal attribute
prediction from conversations over two data sets, but also on the more general
weakly supervised text classification task over one data set.
| [
{
"version": "v1",
"created": "Mon, 28 Nov 2022 14:04:51 GMT"
}
] | 1,669,680,000,000 | [
[
"Liu",
"Yinan",
""
],
[
"Chen",
"Hu",
""
],
[
"Shen",
"Wei",
""
],
[
"Chen",
"Jiaoyan",
""
]
] |
2211.15349 | Michal Ajdar\'ow | Michal Ajdar\'ow, \v{S}imon Brlej, Petr Novotn\'y | Shielding in Resource-Constrained Goal POMDPs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider partially observable Markov decision processes (POMDPs) modeling
an agent that needs a supply of a certain resource (e.g., electricity stored in
batteries) to operate correctly. The resource is consumed by agent's actions
and can be replenished only in certain states. The agent aims to minimize the
expected cost of reaching some goal while preventing resource exhaustion, a
problem we call \emph{resource-constrained goal optimization} (RSGO). We take a
two-step approach to the RSGO problem. First, using formal methods techniques,
we design an algorithm computing a \emph{shield} for a given scenario: a
procedure that observes the agent and prevents it from using actions that might
eventually lead to resource exhaustion. Second, we augment the POMCP heuristic
search algorithm for POMDP planning with our shields to obtain an algorithm
solving the RSGO problem. We implement our algorithm and present experiments
showing its applicability to benchmarks from the literature.
| [
{
"version": "v1",
"created": "Mon, 28 Nov 2022 14:30:05 GMT"
}
] | 1,669,680,000,000 | [
[
"Ajdarów",
"Michal",
""
],
[
"Brlej",
"Šimon",
""
],
[
"Novotný",
"Petr",
""
]
] |
2211.15384 | Yangtianze Tao | Yangtianze Tao and John Doe | Double Deep Q-Learning in Opponent Modeling | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Multi-agent systems in which secondary agents with conflicting agendas also
alter their methods need opponent modeling. In this study, we simulate the main
agent's and secondary agents' tactics using Double Deep Q-Networks (DDQN) with
a prioritized experience replay mechanism. Then, under the opponent modeling
setup, a Mixture-of-Experts architecture is used to identify various opponent
strategy patterns. Finally, we analyze our models in two environments with
several agents. The findings indicate that the Mixture-of-Experts model, which
is based on opponent modeling, performs better than DDQN.
| [
{
"version": "v1",
"created": "Thu, 24 Nov 2022 06:07:47 GMT"
}
] | 1,669,680,000,000 | [
[
"Tao",
"Yangtianze",
""
],
[
"Doe",
"John",
""
]
] |
2211.15408 | Michael Gr. Voskoglou Prof. Dr. | Michael Gr. Voskoglou | Fuzziness, Indeterminacy and Soft Sets: Frontiers and Perspectives | 15 pages, 2 figures, 3 Tables, 30n references | Mathematics, 10, 3909, 2022 | 10.3390/math10203909 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The present paper comes across the main steps that laid from Zadeh's
fuzziness ana Atanassov's intuitionistic fuzzy sets to Smarandache's
indeterminacy and to Molodstov's soft sets. Two hybrid methods for assessment
and decision making respectively under fuzzy conditions are also presented
through suitable examples that use soft sets and real intervals as tools. The
decision making method improves an earlier method of Maji et al. Further, it is
described how the concept of topological space, the most general category of
mathematical spaces, can be extended to fuzzy structures and how to generalize
the fundamental mathematical concepts of limit, continuity compactness and
Hausdorff space within such kind of structures. In particular, fuzzy and soft
topological spaces are defined and examples are given to illustrate these
generalizations.
| [
{
"version": "v1",
"created": "Thu, 10 Nov 2022 07:09:07 GMT"
}
] | 1,669,680,000,000 | [
[
"Voskoglou",
"Michael Gr.",
""
]
] |
2211.15552 | Kaira Samuel | Kaira Samuel, Matthew LaRosa, Kyle McAlpin, Morgan Schaefer, Brandon
Swenson, Devin Wasilefsky, Yan Wu, Dan Zhao, Jeremy Kepner | AI Enabled Maneuver Identification via the Maneuver Identification
Challenge | 10 pages, 7 figures, 4 tables, accepted to and presented at I/ITSEC | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Artificial intelligence (AI) has enormous potential to improve Air Force
pilot training by providing actionable feedback to pilot trainees on the
quality of their maneuvers and enabling instructor-less flying familiarization
for early-stage trainees in low-cost simulators. Historically, AI challenges
consisting of data, problem descriptions, and example code have been critical
to fueling AI breakthroughs. The Department of the Air Force-Massachusetts
Institute of Technology AI Accelerator (DAF-MIT AI Accelerator) developed such
an AI challenge using real-world Air Force flight simulator data. The Maneuver
ID challenge assembled thousands of virtual reality simulator flight recordings
collected by actual Air Force student pilots at Pilot Training Next (PTN). This
dataset has been publicly released at Maneuver-ID.mit.edu and represents the
first of its kind public release of USAF flight training data. Using this
dataset, we have applied a variety of AI methods to separate "good" vs "bad"
simulator data and categorize and characterize maneuvers. These data,
algorithms, and software are being released as baselines of model performance
for others to build upon to enable the AI ecosystem for flight simulator
training.
| [
{
"version": "v1",
"created": "Mon, 28 Nov 2022 16:55:32 GMT"
}
] | 1,669,680,000,000 | [
[
"Samuel",
"Kaira",
""
],
[
"LaRosa",
"Matthew",
""
],
[
"McAlpin",
"Kyle",
""
],
[
"Schaefer",
"Morgan",
""
],
[
"Swenson",
"Brandon",
""
],
[
"Wasilefsky",
"Devin",
""
],
[
"Wu",
"Yan",
""
],
[
"Zhao",
"Dan",
""
],
[
"Kepner",
"Jeremy",
""
]
] |
2211.15566 | Jae Hee Lee | Jae Hee Lee, Michael Sioutis, Kyra Ahrens, Marjan Alirezaie, Matthias
Kerzel, Stefan Wermter | Neuro-Symbolic Spatio-Temporal Reasoning | Contribution to the book "A Compendium of Neuro-Symbolic Artificial
Intelligence", which is to appear in the first half of 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge about space and time is necessary to solve problems in the physical
world: An AI agent situated in the physical world and interacting with objects
often needs to reason about positions of and relations between objects; and as
soon as the agent plans its actions to solve a task, it needs to consider the
temporal aspect (e.g., what actions to perform over time). Spatio-temporal
knowledge, however, is required beyond interacting with the physical world, and
is also often transferred to the abstract world of concepts through analogies
and metaphors (e.g., "a threat that is hanging over our heads"). As spatial and
temporal reasoning is ubiquitous, different attempts have been made to
integrate this into AI systems. In the area of knowledge representation,
spatial and temporal reasoning has been largely limited to modeling objects and
relations and developing reasoning methods to verify statements about objects
and relations. On the other hand, neural network researchers have tried to
teach models to learn spatial relations from data with limited reasoning
capabilities. Bridging the gap between these two approaches in a mutually
beneficial way could allow us to tackle many complex real-world problems, such
as natural language processing, visual question answering, and semantic image
segmentation. In this chapter, we view this integration problem from the
perspective of Neuro-Symbolic AI. Specifically, we propose a synergy between
logical reasoning and machine learning that will be grounded on spatial and
temporal knowledge. Describing some successful applications, remaining
challenges, and evaluation datasets pertaining to this direction is the main
topic of this contribution.
| [
{
"version": "v1",
"created": "Mon, 28 Nov 2022 17:21:41 GMT"
},
{
"version": "v2",
"created": "Fri, 13 Jan 2023 16:53:14 GMT"
}
] | 1,673,827,200,000 | [
[
"Lee",
"Jae Hee",
""
],
[
"Sioutis",
"Michael",
""
],
[
"Ahrens",
"Kyra",
""
],
[
"Alirezaie",
"Marjan",
""
],
[
"Kerzel",
"Matthias",
""
],
[
"Wermter",
"Stefan",
""
]
] |
2211.15782 | Minal Suresh Patil | Minal Suresh Patil | Towards Preserving Semantic Structure in Argumentative Multi-Agent via
Abstract Interpretation | 5 pages, 2 figures, The Online Handbook of Argumentation for AI
(OHAAI) 2022, Vol. 3 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Over the recent twenty years, argumentation has received considerable
attention in the fields of knowledge representation, reasoning, and multi-agent
systems. However, argumentation in dynamic multi-agent systems encounters the
problem of significant arguments generated by agents, which comes at the
expense of representational complexity and computational cost. In this work, we
aim to investigate the notion of abstraction from the model-checking
perspective, where several arguments are trying to defend the same position
from various points of view, thereby reducing the size of the argumentation
framework whilst preserving the semantic flow structure in the system.
| [
{
"version": "v1",
"created": "Mon, 28 Nov 2022 21:32:52 GMT"
}
] | 1,669,766,400,000 | [
[
"Patil",
"Minal Suresh",
""
]
] |
2211.15864 | Gabriel Poesia | Gabriel Poesia and Noah D. Goodman | Peano: Learning Formal Mathematical Reasoning | null | null | 10.1098/rsta.2022.0044 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | General mathematical reasoning is computationally undecidable, but humans
routinely solve new problems. Moreover, discoveries developed over centuries
are taught to subsequent generations quickly. What structure enables this, and
how might that inform automated mathematical reasoning? We posit that central
to both puzzles is the structure of procedural abstractions underlying
mathematics. We explore this idea in a case study on 5 sections of beginning
algebra on the Khan Academy platform. To define a computational foundation, we
introduce Peano, a theorem-proving environment where the set of valid actions
at any point is finite. We use Peano to formalize introductory algebra problems
and axioms, obtaining well-defined search problems. We observe existing
reinforcement learning methods for symbolic reasoning to be insufficient to
solve harder problems. Adding the ability to induce reusable abstractions
("tactics") from its own solutions allows an agent to make steady progress,
solving all problems. Furthermore, these abstractions induce an order to the
problems, seen at random during training. The recovered order has significant
agreement with the expert-designed Khan Academy curriculum, and
second-generation agents trained on the recovered curriculum learn
significantly faster. These results illustrate the synergistic role of
abstractions and curricula in the cultural transmission of mathematics.
| [
{
"version": "v1",
"created": "Tue, 29 Nov 2022 01:42:26 GMT"
}
] | 1,687,305,600,000 | [
[
"Poesia",
"Gabriel",
""
],
[
"Goodman",
"Noah D.",
""
]
] |
2211.15941 | Minrui Xu | Minrui Xu, Xiaoxu Ren, Dusit Niyato, Jiawen Kang, Chao Qiu, Zehui
Xiong, Xiaofei Wang, and Victor C. M. Leung | When Quantum Information Technologies Meet Blockchain in Web 3.0 | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | With the drive to create a decentralized digital economy, Web 3.0 has become
a cornerstone of digital transformation, developed on the basis of
computing-force networking, distributed data storage, and blockchain. With the
rapid realization of quantum devices, Web 3.0 is being developed in parallel
with the deployment of quantum cloud computing and quantum Internet. In this
regard, quantum computing first disrupts the original cryptographic systems
that protect data security while reshaping modern cryptography with the
advantages of quantum computing and communication. Therefore, in this paper, we
introduce a quantum blockchain-driven Web 3.0 framework that provides
information-theoretic security for decentralized data transferring and payment
transactions. First, we present the framework of quantum blockchain-driven Web
3.0 with future-proof security during the transmission of data and transaction
information. Next, we discuss the potential applications and challenges of
implementing quantum blockchain in Web 3.0. Finally, we describe a use case for
quantum non-fungible tokens (NFTs) and propose a quantum deep learning-based
optimal auction for NFT trading to maximize the achievable revenue for
sufficient liquidity in Web 3.0. In this way, the proposed framework can
achieve proven security and sustainability for the next-generation
decentralized digital society.
| [
{
"version": "v1",
"created": "Tue, 29 Nov 2022 05:38:42 GMT"
}
] | 1,669,766,400,000 | [
[
"Xu",
"Minrui",
""
],
[
"Ren",
"Xiaoxu",
""
],
[
"Niyato",
"Dusit",
""
],
[
"Kang",
"Jiawen",
""
],
[
"Qiu",
"Chao",
""
],
[
"Xiong",
"Zehui",
""
],
[
"Wang",
"Xiaofei",
""
],
[
"Leung",
"Victor C. M.",
""
]
] |
2211.16011 | Jiongzhi Zheng | Jiongzhi Zheng and Kun He and Jianrong Zhou and Yan Jin and Chu-Min Li
and Felip Many\`a | Incorporating Multi-armed Bandit with Local Search for MaxSAT | arXiv admin note: substantial text overlap with arXiv:2201.05544 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Partial MaxSAT (PMS) and Weighted PMS (WPMS) are two practical
generalizations of the MaxSAT problem. In this paper, we propose a local search
algorithm for these problems, called BandHS, which applies two multi-armed
bandits to guide the search directions when escaping local optima. One bandit
is combined with all the soft clauses to help the algorithm select to satisfy
appropriate soft clauses, and the other bandit with all the literals in hard
clauses to help the algorithm select appropriate literals to satisfy the hard
clauses. These two bandits can improve the algorithm's search ability in both
feasible and infeasible solution spaces. We further propose an initialization
method for (W)PMS that prioritizes both unit and binary clauses when producing
the initial solutions. Extensive experiments demonstrate the excellent
performance and generalization capability of our proposed methods, that greatly
boost the state-of-the-art local search algorithm, SATLike3.0, and the
state-of-the-art SAT-based incomplete solver, NuWLS-c.
| [
{
"version": "v1",
"created": "Tue, 29 Nov 2022 08:19:26 GMT"
}
] | 1,669,766,400,000 | [
[
"Zheng",
"Jiongzhi",
""
],
[
"He",
"Kun",
""
],
[
"Zhou",
"Jianrong",
""
],
[
"Jin",
"Yan",
""
],
[
"Li",
"Chu-Min",
""
],
[
"Manyà",
"Felip",
""
]
] |
2211.16118 | Nir Oren | Nir Oren and Bruno Yun | Inferring Attack Relations for Gradual Semantics | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | A gradual semantics takes a weighted argumentation framework as input and
outputs a final acceptability degree for each argument, with different
semantics performing the computation in different manners. In this work, we
consider the problem of attack inference. That is, given a gradual semantics, a
set of arguments with associated initial weights, and the final desirable
acceptability degrees associated with each argument, we seek to determine
whether there is a set of attacks on those arguments such that we can obtain
these acceptability degrees. The main contribution of our work is to
demonstrate that the associated decision problem, i.e., whether a set of
attacks can exist which allows the final acceptability degrees to occur for
given initial weights, is NP-complete for the weighted h-categoriser and
cardinality-based semantics, and is polynomial for the weighted max-based
semantics, even for the complete version of the problem (where all initial
weights and final acceptability degrees are known). We then briefly discuss how
this decision problem can be modified to find the attacks themselves and
conclude by examining the partial problem where not all initial weights or
final acceptability degrees may be known.
| [
{
"version": "v1",
"created": "Tue, 29 Nov 2022 11:45:27 GMT"
},
{
"version": "v2",
"created": "Wed, 8 Feb 2023 08:47:46 GMT"
}
] | 1,675,900,800,000 | [
[
"Oren",
"Nir",
""
],
[
"Yun",
"Bruno",
""
]
] |
2211.16242 | Zhou Wen | Wen Zhou | Based on particle swarm optimization support vector machine model of the
electric car sales strategy research | Some experiments need to be done better, and some theories need to be
improved,thank you | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | From the perspective of constructing the classification model, this paper
uses the weight coefficient (influencing factors) in the model to analyze the
sales impact on different brands of electric vehicles, and optimizes the
existing sales strategy.
| [
{
"version": "v1",
"created": "Tue, 29 Nov 2022 14:26:11 GMT"
},
{
"version": "v2",
"created": "Sat, 3 Dec 2022 09:24:32 GMT"
}
] | 1,670,284,800,000 | [
[
"Zhou",
"Wen",
""
]
] |
2211.17199 | Yohai Trabelsi | Yohai Trabelsi, Abhijin Adiga, Sarit Kraus, S.S. Ravi, Daniel J.
Rosenkrantz | Resource Sharing Through Multi-Round Matchings | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Applications such as employees sharing office spaces over a workweek can be
modeled as problems where agents are matched to resources over multiple rounds.
Agents' requirements limit the set of compatible resources and the rounds in
which they want to be matched. Viewing such an application as a multi-round
matching problem on a bipartite compatibility graph between agents and
resources, we show that a solution (i.e., a set of matchings, with one matching
per round) can be found efficiently if one exists. To cope with situations
where a solution does not exist, we consider two extensions. In the first
extension, a benefit function is defined for each agent and the objective is to
find a multi-round matching to maximize the total benefit. For a general class
of benefit functions satisfying certain properties (including diminishing
returns), we show that this multi-round matching problem is efficiently
solvable. This class includes utilitarian and Rawlsian welfare functions. For
another benefit function, we show that the maximization problem is NP-hard. In
the second extension, the objective is to generate advice to each agent (i.e.,
a subset of requirements to be relaxed) subject to a budget constraint so that
the agent can be matched. We show that this budget-constrained advice
generation problem is NP-hard. For this problem, we develop an integer linear
programming formulation as well as a heuristic based on local search. We
experimentally evaluate our algorithms on synthetic networks and apply them to
two real-world situations: shared office spaces and matching courses to
classrooms.
| [
{
"version": "v1",
"created": "Wed, 30 Nov 2022 17:46:43 GMT"
}
] | 1,669,852,800,000 | [
[
"Trabelsi",
"Yohai",
""
],
[
"Adiga",
"Abhijin",
""
],
[
"Kraus",
"Sarit",
""
],
[
"Ravi",
"S. S.",
""
],
[
"Rosenkrantz",
"Daniel J.",
""
]
] |
2211.17262 | Jesse Heyninck | Jesse Heyninck and Ofer Arieli and Bart Bogaerts | Non-Deterministic Approximation Fixpoint Theory and Its Application in
Disjunctive Logic Programming | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Approximation fixpoint theory (AFT) is an abstract and general algebraic
framework for studying the semantics of nonmonotonic logics. It provides a
unifying study of the semantics of different formalisms for nonmonotonic
reasoning, such as logic programming, default logic and autoepistemic logic. In
this paper, we extend AFT to dealing with non-deterministic constructs that
allow to handle indefinite information, represented e.g. by disjunctive
formulas. This is done by generalizing the main constructions and corresponding
results of AFT to non-deterministic operators, whose ranges are sets of
elements rather than single elements. The applicability and usefulness of this
generalization is illustrated in the context of disjunctive logic programming.
| [
{
"version": "v1",
"created": "Wed, 30 Nov 2022 18:58:32 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Dec 2022 08:58:45 GMT"
}
] | 1,669,939,200,000 | [
[
"Heyninck",
"Jesse",
""
],
[
"Arieli",
"Ofer",
""
],
[
"Bogaerts",
"Bart",
""
]
] |
2212.00061 | Christeen T Jose | Christeen T. Jose | Auxiliary Learning as a step towards Artificial General Intelligence | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Auxiliary Learning is a machine learning approach in which the model
acknowledges the existence of objects that do not come under any of its learned
categories.The name Auxiliary learning was chosen due to the introduction of an
auxiliary class. The paper focuses on increasing the generality of existing
narrow purpose neural networks and also highlights the need to handle unknown
objects. The Cat & Dog binary classifier is taken as an example throughout the
paper.
| [
{
"version": "v1",
"created": "Wed, 30 Nov 2022 19:04:50 GMT"
}
] | 1,669,939,200,000 | [
[
"Jose",
"Christeen T.",
""
]
] |
2212.00258 | Manjie Xu | Yu-Zhe Shi, Manjie Xu, Wenjuan Han, Yixin Zhu | To think inside the box, or to think out of the box? Scientific
discovery via the reciprocation of insights and concepts | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | If scientific discovery is one of the main driving forces of human progress,
insight is the fuel for the engine, which has long attracted behavior-level
research to understand and model its underlying cognitive process. However,
current tasks that abstract scientific discovery mostly focus on the emergence
of insight, ignoring the special role played by domain knowledge. In this
concept paper, we view scientific discovery as an interplay between $thinking \
out \ of \ the \ box$ that actively seeks insightful solutions and $thinking \
inside \ the \ box$ that generalizes on conceptual domain knowledge to keep
correct. Accordingly, we propose Mindle, a semantic searching game that
triggers scientific-discovery-like thinking spontaneously, as infrastructure
for exploring scientific discovery on a large scale. On this basis, the
meta-strategies for insights and the usage of concepts can be investigated
reciprocally. In the pilot studies, several interesting observations inspire
elaborated hypotheses on meta-strategies, context, and individual diversity for
further investigations.
| [
{
"version": "v1",
"created": "Thu, 1 Dec 2022 03:52:12 GMT"
},
{
"version": "v2",
"created": "Sun, 4 Dec 2022 09:04:30 GMT"
}
] | 1,670,284,800,000 | [
[
"Shi",
"Yu-Zhe",
""
],
[
"Xu",
"Manjie",
""
],
[
"Han",
"Wenjuan",
""
],
[
"Zhu",
"Yixin",
""
]
] |
2212.00331 | Chengbo Qiu | Chengbo Qiu, Kai Yang, Ji Wang, and Shenjie Zhao | AI Empowered Net-RCA for 6G | 1 pages, wrong page footer | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | 6G is envisioned to offer higher data rate, improved reliability, ubiquitous
AI services, and support massive scale of connected devices. As a consequence,
6G will be much more complex than its predecessors. The growth of the system
scale and complexity as well as the coexistence with the legacy networks and
the diversified service requirements will inevitably incur huge maintenance
cost and efforts for future 6G networks. Network Root Cause Analysis (Net-RCA)
plays a critical role in identifying root causes of network faults. In this
article, we first give an introduction about the envisioned 6G networks. Next,
we discuss the challenges and potential solutions of 6G network operation and
management, and comprehensively survey existing RCA methods. Then we propose an
artificial intelligence (AI)-empowered Net-RCA framework for 6G. Performance
comparisons on both synthetic and real-world network data are carried out to
demonstrate that the proposed method outperforms the existing method
considerably.
| [
{
"version": "v1",
"created": "Thu, 1 Dec 2022 07:38:32 GMT"
},
{
"version": "v2",
"created": "Mon, 5 Dec 2022 00:19:15 GMT"
}
] | 1,670,284,800,000 | [
[
"Qiu",
"Chengbo",
""
],
[
"Yang",
"Kai",
""
],
[
"Wang",
"Ji",
""
],
[
"Zhao",
"Shenjie",
""
]
] |
2212.00342 | Balaji Ganesan | Sukriti Jaitly, Deepa Mariam George, Balaji Ganesan, Muhammad Ameen,
Srinivas Pusapati | xEM: Explainable Entity Matching in Customer 360 | 4 pages, 5 figures. CODS-COMAD 2023 Demo | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Entity matching in Customer 360 is the task of determining if multiple
records represent the same real world entity. Entities are typically people,
organizations, locations, and events represented as attributed nodes in a
graph, though they can also be represented as records in relational data. While
probabilistic matching engines and artificial neural network models exist for
this task, explaining entity matching has received less attention. In this
demo, we present our Explainable Entity Matching (xEM) system and discuss the
different AI/ML considerations that went into its implementation.
| [
{
"version": "v1",
"created": "Thu, 1 Dec 2022 08:01:01 GMT"
}
] | 1,669,939,200,000 | [
[
"Jaitly",
"Sukriti",
""
],
[
"George",
"Deepa Mariam",
""
],
[
"Ganesan",
"Balaji",
""
],
[
"Ameen",
"Muhammad",
""
],
[
"Pusapati",
"Srinivas",
""
]
] |
2212.00368 | Konstantin Nikolaev | O. A. Nevzorova, K. S. Nikolaev | Ontomathedu Ontology Enrichment Method | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Nowadays, distance learning technologies have become very popular. The recent
pandemic has had a particularly strong impact on the development of distance
education technologies. Kazan Federal University has a distance learning system
based on LMS Moodle. This article describes the structure of the OntoMathEdu
ecosystem aimed at improving the process of teaching school mathematics
courses, and also provides a method for improving the OntoMathEdu ontology
structure based on identifying new connections between contextually related
concepts.
| [
{
"version": "v1",
"created": "Thu, 1 Dec 2022 08:57:18 GMT"
}
] | 1,669,939,200,000 | [
[
"Nevzorova",
"O. A.",
""
],
[
"Nikolaev",
"K. S.",
""
]
] |
2212.00373 | Rongzhen Ye | Rongzhen Ye, Tianqu Zhuang, Hai Wan, Jianfeng Du, Weilin Luo, Pingjia
Liang | A Noise-tolerant Differentiable Learning Approach for Single Occurrence
Regular Expression with Interleaving | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We study the problem of learning a single occurrence regular expression with
interleaving (SOIRE) from a set of text strings possibly with noise. SOIRE
fully supports interleaving and covers a large portion of regular expressions
used in practice. Learning SOIREs is challenging because it requires heavy
computation and text strings usually contain noise in practice. Most of the
previous studies only learn restricted SOIREs and are not robust on noisy data.
To tackle these issues, we propose a noise-tolerant differentiable learning
approach SOIREDL for SOIRE. We design a neural network to simulate SOIRE
matching and theoretically prove that certain assignments of the set of
parameters learnt by the neural network, called faithful encodings, are
one-to-one corresponding to SOIREs for a bounded size. Based on this
correspondence, we interpret the target SOIRE from an assignment of the set of
parameters of the neural network by exploring the nearest faithful encodings.
Experimental results show that SOIREDL outperforms the state-of-the-art
approaches, especially on noisy data.
| [
{
"version": "v1",
"created": "Thu, 1 Dec 2022 09:05:43 GMT"
},
{
"version": "v2",
"created": "Fri, 2 Dec 2022 10:34:23 GMT"
},
{
"version": "v3",
"created": "Wed, 11 Jan 2023 07:57:17 GMT"
}
] | 1,673,481,600,000 | [
[
"Ye",
"Rongzhen",
""
],
[
"Zhuang",
"Tianqu",
""
],
[
"Wan",
"Hai",
""
],
[
"Du",
"Jianfeng",
""
],
[
"Luo",
"Weilin",
""
],
[
"Liang",
"Pingjia",
""
]
] |
2212.00443 | Kanghoon Yoon | Kanghoon Yoon, Kibum Kim, Jinyoung Moon, Chanyoung Park | Unbiased Heterogeneous Scene Graph Generation with Relation-aware
Message Passing Neural Network | 9 pages; AAAI 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent scene graph generation (SGG) frameworks have focused on learning
complex relationships among multiple objects in an image. Thanks to the nature
of the message passing neural network (MPNN) that models high-order
interactions between objects and their neighboring objects, they are dominant
representation learning modules for SGG. However, existing MPNN-based
frameworks assume the scene graph as a homogeneous graph, which restricts the
context-awareness of visual relations between objects. That is, they overlook
the fact that the relations tend to be highly dependent on the objects with
which the relations are associated. In this paper, we propose an unbiased
heterogeneous scene graph generation (HetSGG) framework that captures
relation-aware context using message passing neural networks. We devise a novel
message passing layer, called relation-aware message passing neural network
(RMP), that aggregates the contextual information of an image considering the
predicate type between objects. Our extensive evaluations demonstrate that
HetSGG outperforms state-of-the-art methods, especially outperforming on tail
predicate classes.
| [
{
"version": "v1",
"created": "Thu, 1 Dec 2022 11:25:36 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Feb 2023 09:48:09 GMT"
},
{
"version": "v3",
"created": "Tue, 13 Jun 2023 11:16:50 GMT"
},
{
"version": "v4",
"created": "Thu, 6 Jul 2023 06:18:01 GMT"
}
] | 1,688,688,000,000 | [
[
"Yoon",
"Kanghoon",
""
],
[
"Kim",
"Kibum",
""
],
[
"Moon",
"Jinyoung",
""
],
[
"Park",
"Chanyoung",
""
]
] |
2212.00506 | Alberto Pozanco | Alberto Pozanco, Daniel Borrajo | Fairness in Multi-Agent Planning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In cooperative Multi-Agent Planning (MAP), a set of goals has to be achieved
by a set of agents. Independently of whether they perform a pre-assignment of
goals to agents or they directly search for a solution without any goal
assignment, most previous works did not focus on a fair
distribution/achievement of goals by agents. This paper adapts well-known
fairness schemes to MAP, and introduces two novel approaches to generate
cost-aware fair plans. The first one solves an optimization problem to
pre-assign goals to agents, and then solves a centralized MAP task using that
assignment. The second one consists of a planning-based compilation that allows
solving the joint problem of goal assignment and planning while taking into
account the given fairness scheme. Empirical results in several standard MAP
benchmarks show that these approaches outperform different baselines. They also
show that there is no need to sacrifice much plan cost to generate fair plans.
| [
{
"version": "v1",
"created": "Thu, 1 Dec 2022 13:58:46 GMT"
},
{
"version": "v2",
"created": "Mon, 22 May 2023 10:55:25 GMT"
}
] | 1,684,800,000,000 | [
[
"Pozanco",
"Alberto",
""
],
[
"Borrajo",
"Daniel",
""
]
] |
2212.00543 | Bin Liu | Bin Liu, Jin Wang, Kaiwei Sun, Grigorios Tsoumakas | Fine-Grained Selective Similarity Integration for Drug-Target
Interaction Prediction | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The discovery of drug-target interactions (DTIs) is a pivotal process in
pharmaceutical development. Computational approaches are a promising and
efficient alternative to tedious and costly wet-lab experiments for predicting
novel DTIs from numerous candidates. Recently, with the availability of
abundant heterogeneous biological information from diverse data sources,
computational methods have been able to leverage multiple drug and target
similarities to boost the performance of DTI prediction. Similarity integration
is an effective and flexible strategy to extract crucial information across
complementary similarity views, providing a compressed input for any
similarity-based DTI prediction model. However, existing similarity integration
methods filter and fuse similarities from a global perspective, neglecting the
utility of similarity views for each drug and target. In this study, we propose
a Fine-Grained Selective similarity integration approach, called FGS, which
employs a local interaction consistency-based weight matrix to capture and
exploit the importance of similarities at a finer granularity in both
similarity selection and combination steps. We evaluate FGS on five DTI
prediction datasets under various prediction settings. Experimental results
show that our method not only outperforms similarity integration competitors
with comparable computational costs, but also achieves better prediction
performance than state-of-the-art DTI prediction approaches by collaborating
with conventional base models. Furthermore, case studies on the analysis of
similarity weights and on the verification of novel predictions confirm the
practical ability of FGS.
| [
{
"version": "v1",
"created": "Thu, 1 Dec 2022 14:50:42 GMT"
},
{
"version": "v2",
"created": "Tue, 21 Mar 2023 12:52:31 GMT"
}
] | 1,679,443,200,000 | [
[
"Liu",
"Bin",
""
],
[
"Wang",
"Jin",
""
],
[
"Sun",
"Kaiwei",
""
],
[
"Tsoumakas",
"Grigorios",
""
]
] |
2212.00800 | Martin Korth | Martin Korth | The purpose of qualia: What if human thinking is not (only) information
processing? | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite recent breakthroughs in the field of artificial intelligence (AI) -
or more specifically machine learning (ML) algorithms for object recognition
and natural language processing - it seems to be the majority view that current
AI approaches are still no real match for natural intelligence (NI). More
importantly, philosophers have collected a long catalogue of features which
imply that NI works differently from current AI not only in a gradual sense,
but in a more substantial way: NI is closely related to consciousness,
intentionality and experiential features like qualia (the subjective contents
of mental states) and allows for understanding (e.g., taking insight into
causal relationships instead of 'blindly' relying on correlations), as well as
aesthetical and ethical judgement beyond what we can put into (explicit or
data-induced implicit) rules to program machines with. Additionally,
Psychologists find NI to range from unconscious psychological processes to
focused information processing, and from embodied and implicit cognition to
'true' agency and creativity. NI thus seems to transcend any neurobiological
functionalism by operating on 'bits of meaning' instead of information in the
sense of data, quite unlike both the 'good old fashioned', symbolic AI of the
past, as well as the current wave of deep neural network based, 'sub-symbolic'
AI, which both share the idea of thinking as (only) information processing. In
the following I propose an alternative view of NI as information processing
plus 'bundle pushing', discuss an example which illustrates how bundle pushing
can cut information processing short, and suggest first ideas for scientific
experiments in neuro-biology and information theory as further investigations.
| [
{
"version": "v1",
"created": "Tue, 22 Nov 2022 09:45:26 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Dec 2022 12:35:56 GMT"
}
] | 1,670,371,200,000 | [
[
"Korth",
"Martin",
""
]
] |
2212.00951 | Matthew Brown | Youngwon Choi, M. Wasil Wahi-Anwar, Matthew S. Brown | SimpleMind adds thinking to deep neural networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep neural networks (DNNs) detect patterns in data and have shown
versatility and strong performance in many computer vision applications.
However, DNNs alone are susceptible to obvious mistakes that violate simple,
common sense concepts and are limited in their ability to use explicit
knowledge to guide their search and decision making. While overall DNN
performance metrics may be good, these obvious errors, coupled with a lack of
explainability, have prevented widespread adoption for crucial tasks such as
medical image analysis. The purpose of this paper is to introduce SimpleMind,
an open-source software framework for Cognitive AI focused on medical image
understanding. It allows creation of a knowledge base that describes expected
characteristics and relationships between image objects in an intuitive
human-readable form. The SimpleMind framework brings thinking to DNNs by: (1)
providing methods for reasoning with the knowledge base about image content,
such as spatial inferencing and conditional reasoning to check DNN outputs; (2)
applying process knowledge, in the form of general-purpose software agents,
that are chained together to accomplish image preprocessing, DNN prediction,
and result post-processing, and (3) performing automatic co-optimization of all
knowledge base parameters to adapt agents to specific problems. SimpleMind
enables reasoning on multiple detected objects to ensure consistency, providing
cross checking between DNN outputs. This machine reasoning improves the
reliability and trustworthiness of DNNs through an interpretable model and
explainable decisions. Example applications are provided that demonstrate how
SimpleMind supports and improves deep neural networks by embedding them within
a Cognitive AI framework.
| [
{
"version": "v1",
"created": "Fri, 2 Dec 2022 03:38:20 GMT"
}
] | 1,670,198,400,000 | [
[
"Choi",
"Youngwon",
""
],
[
"Wahi-Anwar",
"M. Wasil",
""
],
[
"Brown",
"Matthew S.",
""
]
] |
2212.00994 | Chenxin Zou | Xiaodong Li, Chenxin Zou, Yi Cai, Yuelong Zhu | Knowledge Graph Quality Evaluation under Incomplete Information | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge graphs (KGs) have attracted more and more attentions because of
their fundamental roles in many tasks. Quality evaluation for KGs is thus
crucial and indispensable. Existing methods in this field evaluate KGs by
either proposing new quality metrics from different dimensions or measuring
performances at KG construction stages. However, there are two major issues
with those methods. First, they highly rely on raw data in KGs, which makes
KGs' internal information exposed during quality evaluation. Second, they
consider more about the quality at data level instead of ability level, where
the latter one is more important for downstream applications. To address these
issues, we propose a knowledge graph quality evaluation framework under
incomplete information (QEII). The quality evaluation task is transformed into
an adversarial Q&A game between two KGs. Winner of the game is thus considered
to have better qualities. During the evaluation process, no raw data is
exposed, which ensures information protection. Experimental results on four
pairs of KGs demonstrate that, compared with baselines, the QEII implements a
reasonable quality evaluation at ability level under incomplete information.
| [
{
"version": "v1",
"created": "Fri, 2 Dec 2022 06:12:10 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Apr 2023 19:46:34 GMT"
},
{
"version": "v3",
"created": "Wed, 12 Apr 2023 07:53:54 GMT"
}
] | 1,681,344,000,000 | [
[
"Li",
"Xiaodong",
""
],
[
"Zou",
"Chenxin",
""
],
[
"Cai",
"Yi",
""
],
[
"Zhu",
"Yuelong",
""
]
] |
2212.01022 | Indranil Saha | Nikhil Kumar Singh and Indranil Saha | STL-Based Synthesis of Feedback Controllers Using Reinforcement Learning | Full version of the paper to be published in AAAI 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep Reinforcement Learning (DRL) has the potential to be used for
synthesizing feedback controllers (agents) for various complex systems with
unknown dynamics. These systems are expected to satisfy diverse safety and
liveness properties best captured using temporal logic. In RL, the reward
function plays a crucial role in specifying the desired behaviour of these
agents. However, the problem of designing the reward function for an RL agent
to satisfy complex temporal logic specifications has received limited attention
in the literature. To address this, we provide a systematic way of generating
rewards in real-time by using the quantitative semantics of Signal Temporal
Logic (STL), a widely used temporal logic to specify the behaviour of
cyber-physical systems. We propose a new quantitative semantics for STL having
several desirable properties, making it suitable for reward generation. We
evaluate our STL-based reinforcement learning mechanism on several complex
continuous control benchmarks and compare our STL semantics with those
available in the literature in terms of their efficacy in synthesizing the
controller agent. Experimental results establish our new semantics to be the
most suitable for synthesizing feedback controllers for complex continuous
dynamical systems through reinforcement learning.
| [
{
"version": "v1",
"created": "Fri, 2 Dec 2022 08:31:46 GMT"
}
] | 1,670,198,400,000 | [
[
"Singh",
"Nikhil Kumar",
""
],
[
"Saha",
"Indranil",
""
]
] |
2212.02064 | Can Chang | Can Chang, Ni Mu, Jiajun Wu, Ling Pan, Huazhe Xu | E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel
Program Guidance | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | A critical challenge in multi-agent reinforcement learning(MARL) is for
multiple agents to efficiently accomplish complex, long-horizon tasks. The
agents often have difficulties in cooperating on common goals, dividing complex
tasks, and planning through several stages to make progress. We propose to
address these challenges by guiding agents with programs designed for
parallelization, since programs as a representation contain rich structural and
semantic information, and are widely used as abstractions for long-horizon
tasks. Specifically, we introduce Efficient Multi-Agent Reinforcement Learning
with Parallel Program Guidance(E-MAPP), a novel framework that leverages
parallel programs to guide multiple agents to efficiently accomplish goals that
require planning over $10+$ stages. E-MAPP integrates the structural
information from a parallel program, promotes the cooperative behaviors
grounded in program semantics, and improves the time efficiency via a task
allocator. We conduct extensive experiments on a series of challenging,
long-horizon cooperative tasks in the Overcooked environment. Results show that
E-MAPP outperforms strong baselines in terms of the completion rate, time
efficiency, and zero-shot generalization ability by a large margin.
| [
{
"version": "v1",
"created": "Mon, 5 Dec 2022 07:02:05 GMT"
}
] | 1,670,284,800,000 | [
[
"Chang",
"Can",
""
],
[
"Mu",
"Ni",
""
],
[
"Wu",
"Jiajun",
""
],
[
"Pan",
"Ling",
""
],
[
"Xu",
"Huazhe",
""
]
] |
2212.02098 | Taewoon Kim | Taewoon Kim, Michael Cochez, Vincent Fran\c{c}ois-Lavet, Mark
Neerincx, Piek Vossen | A Machine with Short-Term, Episodic, and Semantic Memory Systems | null | Proceedings of the AAAI Conference on Artificial Intelligence
(2023), 37(1), 48-56 | 10.1609/aaai.v37i1.25075 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Inspired by the cognitive science theory of the explicit human memory
systems, we have modeled an agent with short-term, episodic, and semantic
memory systems, each of which is modeled with a knowledge graph. To evaluate
this system and analyze the behavior of this agent, we designed and released
our own reinforcement learning agent environment, "the Room", where an agent
has to learn how to encode, store, and retrieve memories to maximize its return
by answering questions. We show that our deep Q-learning based agent
successfully learns whether a short-term memory should be forgotten, or rather
be stored in the episodic or semantic memory systems. Our experiments indicate
that an agent with human-like memory systems can outperform an agent without
this memory structure in the environment.
| [
{
"version": "v1",
"created": "Mon, 5 Dec 2022 08:34:23 GMT"
},
{
"version": "v2",
"created": "Sat, 8 Jul 2023 10:50:19 GMT"
}
] | 1,689,033,600,000 | [
[
"Kim",
"Taewoon",
""
],
[
"Cochez",
"Michael",
""
],
[
"François-Lavet",
"Vincent",
""
],
[
"Neerincx",
"Mark",
""
],
[
"Vossen",
"Piek",
""
]
] |
2212.02823 | Siddharth Srivastava | Siddharth Srivastava | Hierarchical Decomposition and Analysis for Generalized Planning | Accepted for publication at JAIR | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents new methods for analyzing and evaluating generalized
plans that can solve broad classes of related planning problems. Although
synthesis and learning of generalized plans has been a longstanding goal in AI,
it remains challenging due to fundamental gaps in methods for analyzing the
scope and utility of a given generalized plan. This paper addresses these gaps
by developing a new conceptual framework along with proof techniques and
algorithmic processes for assessing termination and goal-reachability related
properties of generalized plans. We build upon classic results from graph
theory to decompose generalized plans into smaller components that are then
used to derive hierarchical termination arguments. These methods can be used to
determine the utility of a given generalized plan, as well as to guide the
synthesis and learning processes for generalized plans. We present theoretical
as well as empirical results illustrating the scope of this new approach. Our
analysis shows that this approach significantly extends the class of
generalized plans that can be assessed automatically, thereby reducing barriers
in the synthesis and learning of reliable generalized plans.
| [
{
"version": "v1",
"created": "Tue, 6 Dec 2022 08:37:21 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Jun 2023 18:43:18 GMT"
}
] | 1,687,910,400,000 | [
[
"Srivastava",
"Siddharth",
""
]
] |
2212.02893 | Guillaume Escamocher | Guillaume Escamocher, Barry O'Sullivan | Generation and Prediction of Difficult Model Counting Instances | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We present a way to create small yet difficult model counting instances. Our
generator is highly parameterizable: the number of variables of the instances
it produces, as well as their number of clauses and the number of literals in
each clause, can all be set to any value. Our instances have been tested on
state of the art model counters, against other difficult model counting
instances, in the Model Counting Competition. The smallest unsolved instances
of the competition, both in terms of number of variables and number of clauses,
were ours. We also observe a peak of difficulty when fixing the number of
variables and varying the number of clauses, in both random instances and
instances built by our generator. Using these results, we predict the parameter
values for which the hardest to count instances will occur.
| [
{
"version": "v1",
"created": "Tue, 6 Dec 2022 11:36:02 GMT"
}
] | 1,670,371,200,000 | [
[
"Escamocher",
"Guillaume",
""
],
[
"O'Sullivan",
"Barry",
""
]
] |
2212.02951 | Ziqi Wang | Ziqi Wang, Tianye Shu, Jialin Liu | State Space Closure: Revisiting Endless Online Level Generation via
Reinforcement Learning | Accepted by the IEEE Transactions on Games | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we revisit endless online level generation with the recently
proposed experience-driven procedural content generation via reinforcement
learning (EDRL) framework. Inspired by an observation that EDRL tends to
generate recurrent patterns, we formulate a notion of state space closure which
makes any stochastic state appeared possibly in an infinite-horizon online
generation process can be found within a finite-horizon. Through theoretical
analysis, we find that even though state space closure arises a concern about
diversity, it generalises EDRL trained with a finite-horizon to the
infinite-horizon scenario without deterioration of content quality. Moreover,
we verify the quality and the diversity of contents generated by EDRL via
empirical studies, on the widely used Super Mario Bros. benchmark. Experimental
results reveal that the diversity of levels generated by EDRL is limited due to
the state space closure, whereas their quality does not deteriorate in a
horizon which is longer than the one specified in the training. Concluding our
outcomes and analysis, future work on endless online level generation via
reinforcement learning should address the issue of diversity while assuring the
occurrence of state space closure and quality.
| [
{
"version": "v1",
"created": "Tue, 6 Dec 2022 13:12:16 GMT"
},
{
"version": "v2",
"created": "Fri, 24 Mar 2023 10:23:22 GMT"
}
] | 1,679,875,200,000 | [
[
"Wang",
"Ziqi",
""
],
[
"Shu",
"Tianye",
""
],
[
"Liu",
"Jialin",
""
]
] |
2212.03178 | Mohsen Hooshmand | Alireza Abdi, Masih Hajsaeedi, Mohsen Hooshmand | Longest Common Substring in Longest Common Subsequence's Solution
Service: A Novel Hyper-Heuristic | null | null | 10.1016/j.compbiolchem.2023.107882 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Longest Common Subsequence (LCS) is the problem of finding a subsequence
among a set of strings that has two properties of being common to all and is
the longest. The LCS has applications in computational biology and text
editing, among many others. Due to the NP-hardness of the general longest
common subsequence, numerous heuristic algorithms and solvers have been
proposed to give the best possible solution for different sets of strings. None
of them has the best performance for all types of sets. In addition, there is
no method to specify the type of a given set of strings. Besides that, the
available hyper-heuristic is not efficient and fast enough to solve this
problem in real-world applications. This paper proposes a novel hyper-heuristic
to solve the longest common subsequence problem using a novel criterion to
classify a set of strings based on their similarity. To do this, we offer a
general stochastic framework to identify the type of a given set of strings.
Following that, we introduce the set similarity dichotomizer ($S^2D$) algorithm
based on the framework that divides the type of sets into two. This algorithm
is introduced for the first time in this paper and opens a new way to go beyond
the current LCS solvers. Then, we present a novel hyper-heuristic that exploits
the $S^2D$ and one of the internal properties of the set to choose the best
matching heuristic among a set of heuristics. We compare the results on
benchmark datasets with the best heuristics and hyper-heuristics. The results
show a higher performance of our proposed hyper-heuristic in both quality of
solutions and run time factors.
| [
{
"version": "v1",
"created": "Sat, 3 Dec 2022 07:52:57 GMT"
}
] | 1,686,096,000,000 | [
[
"Abdi",
"Alireza",
""
],
[
"Hajsaeedi",
"Masih",
""
],
[
"Hooshmand",
"Mohsen",
""
]
] |
2212.03387 | Matthew Guzdial | Kynan Sorochan, Matthew Guzdial | Generating Real-Time Strategy Game Units Using Search-Based Procedural
Content Generation and Monte Carlo Tree Search | 7 pages, 3 figures, Experimental AI in Games Workshop | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Real-Time Strategy (RTS) game unit generation is an unexplored area of
Procedural Content Generation (PCG) research, which leaves the question of how
to automatically generate interesting and balanced units unanswered. Creating
unique and balanced units can be a difficult task when designing an RTS game,
even for humans. Having an automated method of designing units could help
developers speed up the creation process as well as find new ideas. In this
work we propose a method of generating balanced and useful RTS units. We draw
on Search-Based PCG and a fitness function based on Monte Carlo Tree Search
(MCTS). We present ten units generated by our system designed to be used in the
game microRTS, as well as results demonstrating that these units are unique,
useful, and balanced.
| [
{
"version": "v1",
"created": "Wed, 7 Dec 2022 00:43:30 GMT"
}
] | 1,670,457,600,000 | [
[
"Sorochan",
"Kynan",
""
],
[
"Guzdial",
"Matthew",
""
]
] |
2212.03467 | Christopher Jerrett | Yue Han, Christopher Jerrett, Elliot Anshelevich | Optimizing Multiple Simultaneous Objectives for Voting and Facility
Location | To be published in the Proceedings of 37th Conference on Artificial
Intelligence (AAAI 2023) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the classic facility location setting, where we are given $n$
clients and $m$ possible facility locations in some arbitrary metric space, and
want to choose a location to build a facility. The exact same setting also
arises in spatial social choice, where voters are the clients and the goal is
to choose a candidate or outcome, with the distance from a voter to an outcome
representing the cost of this outcome for the voter (e.g., based on their
ideological differences). Unlike most previous work, we do not focus on a
single objective to optimize (e.g., the total distance from clients to the
facility, or the maximum distance, etc.), but instead attempt to optimize
several different objectives simultaneously. More specifically, we consider the
$l$-centrum family of objectives, which includes the total distance, max
distance, and many others. We present tight bounds on how well any pair of such
objectives (e.g., max and sum) can be simultaneously approximated compared to
their optimum outcomes. In particular, we show that for any such pair of
objectives, it is always possible to choose an outcome which simultaneously
approximates both objectives within a factor of $1+\sqrt{2}$, and give a
precise characterization of how this factor improves as the two objectives
being optimized become more similar. For $q>2$ different centrum objectives, we
show that it is always possible to approximate all $q$ of these objectives
within a small constant, and that this constant approaches 3 as $q\rightarrow
\infty$. Our results show that when optimizing only a few simultaneous
objectives, it is always possible to form an outcome which is a significantly
better than 3 approximation for all of these objectives.
| [
{
"version": "v1",
"created": "Wed, 7 Dec 2022 05:12:40 GMT"
},
{
"version": "v2",
"created": "Sat, 10 Dec 2022 18:01:15 GMT"
}
] | 1,670,889,600,000 | [
[
"Han",
"Yue",
""
],
[
"Jerrett",
"Christopher",
""
],
[
"Anshelevich",
"Elliot",
""
]
] |
2212.04401 | Ida Momennejad | Ida Momennejad | A Rubric for Human-like Agents and NeuroAI | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Researchers across cognitive, neuro-, and computer sciences increasingly
reference human-like artificial intelligence and neuroAI. However, the scope
and use of the terms are often inconsistent. Contributed research ranges widely
from mimicking behaviour, to testing machine learning methods as neurally
plausible hypotheses at the cellular or functional levels, or solving
engineering problems. However, it cannot be assumed nor expected that progress
on one of these three goals will automatically translate to progress in others.
Here a simple rubric is proposed to clarify the scope of individual
contributions, grounded in their commitments to human-like behaviour, neural
plausibility, or benchmark/engineering goals. This is clarified using examples
of weak and strong neuroAI and human-like agents, and discussing the
generative, corroborate, and corrective ways in which the three dimensions
interact with one another. The author maintains that future progress in
artificial intelligence will need strong interactions across the disciplines,
with iterative feedback loops and meticulous validity tests, leading to both
known and yet-unknown advances that may span decades to come.
| [
{
"version": "v1",
"created": "Thu, 8 Dec 2022 16:59:40 GMT"
}
] | 1,670,544,000,000 | [
[
"Momennejad",
"Ida",
""
]
] |
2212.04419 | Conor Muldoon | Conor Muldoon, Levent G\"org\"u, John J. O'Sullivan, Wim G. Meijer,
Gregory M. P. O'Hare | Mining Explainable Predictive Features for Water Quality Management | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | With water quality management processes, identifying and interpreting
relationships between features, such as location and weather variable tuples,
and water quality variables, such as levels of bacteria, is key to gaining
insights and identifying areas where interventions should be made. There is a
need for a search process to identify the locations and types of phenomena that
are influencing water quality and a need to explain how the quality is being
affected and which factors are most relevant. This paper addresses both of
these issues. A process is developed for collecting data for features that
represent a variety of variables over a spatial region and which are used for
training models and inference. An analysis of the performance of the features
is undertaken using the models and Shapley values. Shapley values originated in
cooperative game theory and can be used to aid in the interpretation of machine
learning results. Evaluations are performed using several machine learning
algorithms and water quality data from the Dublin Grand Canal basin.
| [
{
"version": "v1",
"created": "Thu, 8 Dec 2022 17:18:50 GMT"
},
{
"version": "v2",
"created": "Fri, 9 Dec 2022 15:47:59 GMT"
}
] | 1,670,803,200,000 | [
[
"Muldoon",
"Conor",
""
],
[
"Görgü",
"Levent",
""
],
[
"O'Sullivan",
"John J.",
""
],
[
"Meijer",
"Wim G.",
""
],
[
"O'Hare",
"Gregory M. P.",
""
]
] |
2212.04576 | Duo Xu | Duo Xu, Faramarz Fekri | Generalizing LTL Instructions via Future Dependent Options | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In many real-world applications of control system and robotics, linear
temporal logic (LTL) is a widely-used task specification language which has a
compositional grammar that naturally induces temporally extended behaviours
across tasks, including conditionals and alternative realizations. An important
problem in RL with LTL tasks is to learn task-conditioned policies which can
zero-shot generalize to new LTL instructions not observed in the training.
However, because symbolic observation is often lossy and LTL tasks can have
long time horizon, previous works can suffer from issues such as training
sampling inefficiency and infeasibility or sub-optimality of the found
solutions. In order to tackle these issues, this paper proposes a novel
multi-task RL algorithm with improved learning efficiency and optimality. To
achieve the global optimality of task completion, we propose to learn options
dependent on the future subgoals via a novel off-policy approach. In order to
propagate the rewards of satisfying future subgoals back more efficiently, we
propose to train a multi-step value function conditioned on the subgoal
sequence which is updated with Monte Carlo estimates of multi-step discounted
returns. In experiments on three different domains, we evaluate the LTL
generalization capability of the agent trained by the proposed method, showing
its advantage over previous representative methods.
| [
{
"version": "v1",
"created": "Thu, 8 Dec 2022 21:44:18 GMT"
},
{
"version": "v2",
"created": "Tue, 13 Dec 2022 04:29:02 GMT"
},
{
"version": "v3",
"created": "Thu, 15 Dec 2022 12:52:01 GMT"
}
] | 1,671,148,800,000 | [
[
"Xu",
"Duo",
""
],
[
"Fekri",
"Faramarz",
""
]
] |
2212.04589 | Eduardo C. Garrido-Merch\'an | Eduardo C. Garrido-Merch\'an, Javier S\'anchez-Ca\~nizares | Optimizing Integrated Information with a Prior Guided Random Search
Algorithm | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Integrated information theory (IIT) is a theoretical framework that provides
a quantitative measure to estimate when a physical system is conscious, its
degree of consciousness, and the complexity of the qualia space that the system
is experiencing. Formally, IIT rests on the assumption that if a surrogate
physical system can fully embed the phenomenological properties of
consciousness, then the system properties must be constrained by the properties
of the qualia being experienced. Following this assumption, IIT represents the
physical system as a network of interconnected elements that can be thought of
as a probabilistic causal graph, $\mathcal{G}$, where each node has an
input-output function and all the graph is encoded in a transition probability
matrix. Consequently, IIT's quantitative measure of consciousness, $\Phi$, is
computed with respect to the transition probability matrix and the present
state of the graph. In this paper, we provide a random search algorithm that is
able to optimize $\Phi$ in order to investigate, as the number of nodes
increases, the structure of the graphs that have higher $\Phi$. We also provide
arguments that show the difficulties of applying more complex black-box search
algorithms, such as Bayesian optimization or metaheuristics, in this particular
problem. Additionally, we suggest specific research lines for these techniques
to enhance the search algorithm that guarantees maximal $\Phi$.
| [
{
"version": "v1",
"created": "Thu, 8 Dec 2022 22:34:00 GMT"
}
] | 1,670,803,200,000 | [
[
"Garrido-Merchán",
"Eduardo C.",
""
],
[
"Sánchez-Cañizares",
"Javier",
""
]
] |
2212.04891 | Xunzhu Tang | Shi Wang and Daniel Tang and Luchen Zhang and Huilin Li and Ding Han | HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | International Classification of Diseases (ICD) is a set of classification
codes for medical records. Automated ICD coding, which assigns unique
International Classification of Diseases codes with each medical record, is
widely used recently for its efficiency and error-prone avoidance. However,
there are challenges that remain such as heterogeneity, label unbalance, and
complex relationships between ICD codes. In this work, we proposed a novel
Bidirectional Hierarchy Framework(HieNet) to address the challenges.
Specifically, a personalized PageRank routine is developed to capture the
co-relation of codes, a bidirectional hierarchy passage encoder to capture the
codes' hierarchical representations, and a progressive predicting method is
then proposed to narrow down the semantic searching space of prediction. We
validate our method on two widely used datasets. Experimental results on two
authoritative public datasets demonstrate that our proposed method boosts
state-of-the-art performance by a large margin.
| [
{
"version": "v1",
"created": "Fri, 9 Dec 2022 14:51:12 GMT"
}
] | 1,670,803,200,000 | [
[
"Wang",
"Shi",
""
],
[
"Tang",
"Daniel",
""
],
[
"Zhang",
"Luchen",
""
],
[
"Li",
"Huilin",
""
],
[
"Han",
"Ding",
""
]
] |
2212.05412 | Kebing Jin | Kebing Jin, Yingkai Xiao, Hankz Hankui Zhuo, Renyong Ma | A Hierarchical Temporal Planning-Based Approach for Dynamic Hoist
Scheduling Problems | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Hoist scheduling has become a bottleneck in electroplating industry
applications with the development of autonomous devices. Although there are a
few approaches proposed to target at the challenging problem, they generally
cannot scale to large-scale scheduling problems. In this paper, we formulate
the hoist scheduling problem as a new temporal planning problem in the form of
adapted PDDL, and propose a novel hierarchical temporal planning approach to
efficiently solve the scheduling problem. Additionally, we provide a collection
of real-life benchmark instances that can be used to evaluate solution methods
for the problem. We exhibit that the proposed approach is able to efficiently
find solutions of high quality for large-scale real-life benchmark instances,
with comparison to state-of-the-art baselines.
| [
{
"version": "v1",
"created": "Sun, 11 Dec 2022 05:30:44 GMT"
}
] | 1,670,889,600,000 | [
[
"Jin",
"Kebing",
""
],
[
"Xiao",
"Yingkai",
""
],
[
"Zhuo",
"Hankz Hankui",
""
],
[
"Ma",
"Renyong",
""
]
] |
2212.06064 | Leonardo Felizardo Kanashiro | Leonardo Kanashiro Felizardo, Francisco Caio Lima Paiva, Anna Helena
Reali Costa, Emilio Del-Moral-Hernandez | Reinforcement Learning Applied to Trading Systems: A Survey | 38 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Financial domain tasks, such as trading in market exchanges, are challenging
and have long attracted researchers. The recent achievements and the consequent
notoriety of Reinforcement Learning (RL) have also increased its adoption in
trading tasks. RL uses a framework with well-established formal concepts, which
raises its attractiveness in learning profitable trading strategies. However,
RL use without due attention in the financial area can prevent new researchers
from following standards or failing to adopt relevant conceptual guidelines. In
this work, we embrace the seminal RL technical fundamentals, concepts, and
recommendations to perform a unified, theoretically-grounded examination and
comparison of previous research that could serve as a structuring guide for the
field of study. A selection of twenty-nine articles was reviewed under our
classification that considers RL's most common formulations and design patterns
from a large volume of available studies. This classification allowed for
precise inspection of the most relevant aspects regarding data input,
preprocessing, state and action composition, adopted RL techniques, evaluation
setups, and overall results. Our analysis approach organized around fundamental
RL concepts allowed for a clear identification of current system design best
practices, gaps that require further investigation, and promising research
opportunities. Finally, this review attempts to promote the development of this
field of study by facilitating researchers' commitment to standards adherence
and helping them to avoid straying away from the RL constructs' firm ground.
| [
{
"version": "v1",
"created": "Tue, 1 Nov 2022 21:26:12 GMT"
}
] | 1,670,889,600,000 | [
[
"Felizardo",
"Leonardo Kanashiro",
""
],
[
"Paiva",
"Francisco Caio Lima",
""
],
[
"Costa",
"Anna Helena Reali",
""
],
[
"Del-Moral-Hernandez",
"Emilio",
""
]
] |
2212.06330 | Shuqiang Wang | Changwei Gong, Changhong Jing, Ye Li, Xinan Liu, Zuxin Chen, Shuqiang
Wang | Generative artificial intelligence-enabled dynamic detection of
nicotine-related circuits | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The identification of addiction-related circuits is critical for explaining
addiction processes and developing addiction treatments. And models of
functional addiction circuits developed from functional imaging are an
effective tool for discovering and verifying addiction circuits. However,
analyzing functional imaging data of addiction and detecting functional
addiction circuits still have challenges. We have developed a data-driven and
end-to-end generative artificial intelligence(AI) framework to address these
difficulties. The framework integrates dynamic brain network modeling and novel
network architecture networks architecture, including temporal graph
Transformer and contrastive learning modules. A complete workflow is formed by
our generative AI framework: the functional imaging data, from neurobiological
experiments, and computational modeling, to end-to-end neural networks, is
transformed into dynamic nicotine addiction-related circuits. It enables the
detection of addiction-related brain circuits with dynamic properties and
reveals the underlying mechanisms of addiction.
| [
{
"version": "v1",
"created": "Tue, 13 Dec 2022 02:21:22 GMT"
}
] | 1,670,976,000,000 | [
[
"Gong",
"Changwei",
""
],
[
"Jing",
"Changhong",
""
],
[
"Li",
"Ye",
""
],
[
"Liu",
"Xinan",
""
],
[
"Chen",
"Zuxin",
""
],
[
"Wang",
"Shuqiang",
""
]
] |
2212.06564 | Sergey Zeltyn Dr. | Sergey Zeltyn, Segev Shlomov, Avi Yaeli, Alon Oved | Prescriptive Process Monitoring in Intelligent Process Automation with
Chatbot Orchestration | IJCAI 2022 Workshop on Process Management in the AI era (PMAI) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Business processes that involve AI-powered automation have been gaining
importance and market share in recent years. These business processes combine
the characteristics of classical business process management, goal-driven
chatbots, conversational recommendation systems, and robotic process
automation. In the new context, prescriptive process monitoring demands
innovative approaches. Unfortunately, data logs from these new processes are
still not available in the public domain. We describe the main challenges in
this new domain and introduce a synthesized dataset that is based on an actual
use case of intelligent process automation with chatbot orchestration. Using
this dataset, we demonstrate crowd-wisdom and goal-driven approaches to
prescriptive process monitoring.
| [
{
"version": "v1",
"created": "Tue, 13 Dec 2022 13:34:08 GMT"
}
] | 1,670,976,000,000 | [
[
"Zeltyn",
"Sergey",
""
],
[
"Shlomov",
"Segev",
""
],
[
"Yaeli",
"Avi",
""
],
[
"Oved",
"Alon",
""
]
] |
2212.07226 | Andrea Micheli | Andrea Micheli | An Efficient Incremental Simple Temporal Network Data Structure for
Temporal Planning | V2: Fixed a typo in the algorithm pseudocode | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | One popular technique to solve temporal planning problems consists in
decoupling the causal decisions, demanding them to heuristic search, from
temporal decisions, demanding them to a simple temporal network (STN) solver.
In this architecture, one needs to check the consistency of a series of STNs
that are related one another, therefore having methods to incrementally re-use
previous computations and that avoid expensive memory duplication is of
paramount importance. In this paper, we describe in detail how STNs are used in
temporal planning, we identify a clear interface to support this use-case and
we present an efficient data-structure implementing this interface that is both
time- and memory-efficient. We show that our data structure, called \deltastn,
is superior to other state-of-the-art approaches on temporal planning sequences
of problems.
| [
{
"version": "v1",
"created": "Wed, 14 Dec 2022 13:57:37 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Aug 2023 13:59:47 GMT"
}
] | 1,691,971,200,000 | [
[
"Micheli",
"Andrea",
""
]
] |
2212.07523 | Laura Giordano | Mario Alviano, Laura Giordano, and Daniele Theseider Dupr\'e | Many-valued Argumentation, Conditionals and a Probabilistic Semantics
for Gradual Argumentation | 17 pages, 1 figure | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper we propose a general approach to define a many-valued
preferential interpretation of gradual argumentation semantics. The approach
allows for conditional reasoning over arguments and boolean combination of
arguments, with respect to a class of gradual semantics, through the
verification of graded (strict or defeasible) implications over a preferential
interpretation. As a proof of concept, in the finitely-valued case, an Answer
set Programming approach is proposed for conditional reasoning in a many-valued
argumentation semantics of weighted argumentation graphs. The paper also
develops and discusses a probabilistic semantics for gradual argumentation,
which builds on the many-valued conditional semantics.
| [
{
"version": "v1",
"created": "Wed, 14 Dec 2022 22:10:46 GMT"
}
] | 1,671,148,800,000 | [
[
"Alviano",
"Mario",
""
],
[
"Giordano",
"Laura",
""
],
[
"Dupré",
"Daniele Theseider",
""
]
] |
2212.07996 | Stefan Sarkadi | Lars Bengel, Elfia Bezou-Vrakatseli, Lydia Bl\"umel, Federico
Castagna, Giulia D'Agostino, Daphne Odekerken, Minal Suresh Patil, Jordan
Robinson, Hao Wu, Andreas Xydis | Online Handbook of Argumentation for AI: Volume 3 | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This volume contains revised versions of the papers selected for the third
volume of the Online Handbook of Argumentation for AI (OHAAI). Previously,
formal theories of argument and argument interaction have been proposed and
studied, and this has led to the more recent study of computational models of
argument. Argumentation, as a field within artificial intelligence (AI), is
highly relevant for researchers interested in symbolic representations of
knowledge and defeasible reasoning. The purpose of this handbook is to provide
an open access and curated anthology for the argumentation research community.
OHAAI is designed to serve as a research hub to keep track of the latest and
upcoming PhD-driven research on the theory and application of argumentation in
all areas related to AI.
| [
{
"version": "v1",
"created": "Thu, 15 Dec 2022 17:49:44 GMT"
}
] | 1,671,148,800,000 | [
[
"Bengel",
"Lars",
""
],
[
"Bezou-Vrakatseli",
"Elfia",
""
],
[
"Blümel",
"Lydia",
""
],
[
"Castagna",
"Federico",
""
],
[
"D'Agostino",
"Giulia",
""
],
[
"Odekerken",
"Daphne",
""
],
[
"Patil",
"Minal Suresh",
""
],
[
"Robinson",
"Jordan",
""
],
[
"Wu",
"Hao",
""
],
[
"Xydis",
"Andreas",
""
]
] |
2212.08183 | Taoan Huang | Taoan Huang, Aaron Ferber, Yuandong Tian, Bistra Dilkina, Benoit
Steiner | Local Branching Relaxation Heuristics for Integer Linear Programs | null | null | 10.1007/978-3-031-33271-5_7 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Large Neighborhood Search (LNS) is a popular heuristic algorithm for solving
combinatorial optimization problems (COP). It starts with an initial solution
to the problem and iteratively improves it by searching a large neighborhood
around the current best solution. LNS relies on heuristics to select
neighborhoods to search in. In this paper, we focus on designing effective and
efficient heuristics in LNS for integer linear programs (ILP) since a wide
range of COPs can be represented as ILPs. Local Branching (LB) is a heuristic
that selects the neighborhood that leads to the largest improvement over the
current solution in each iteration of LNS. LB is often slow since it needs to
solve an ILP of the same size as input. Our proposed heuristics, LB-RELAX and
its variants, use the linear programming relaxation of LB to select
neighborhoods. Empirically, LB-RELAX and its variants compute as effective
neighborhoods as LB but run faster. They achieve state-of-the-art anytime
performance on several ILP benchmarks.
| [
{
"version": "v1",
"created": "Thu, 15 Dec 2022 22:53:09 GMT"
},
{
"version": "v2",
"created": "Wed, 31 May 2023 18:16:01 GMT"
}
] | 1,685,664,000,000 | [
[
"Huang",
"Taoan",
""
],
[
"Ferber",
"Aaron",
""
],
[
"Tian",
"Yuandong",
""
],
[
"Dilkina",
"Bistra",
""
],
[
"Steiner",
"Benoit",
""
]
] |
2212.08626 | Deokgun Park | Deokgun Park, Md Ashaduzzaman Rubel Mondol, SM Mazharul Islam,
Aishwarya Pothula | Hippocampus-Inspired Cognitive Architecture (HICA) for Operant
Conditioning | arXiv admin note: text overlap with arXiv:2108.03793 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The neural implementation of operant conditioning with few trials is unclear.
We propose a Hippocampus-Inspired Cognitive Architecture (HICA) as a neural
mechanism for operant conditioning. HICA explains a learning mechanism in which
agents can learn a new behavior policy in a few trials, as mammals do in
operant conditioning experiments. HICA is composed of two different types of
modules. One is a universal learning module type that represents a cortical
column in the neocortex gray matter. The working principle is modeled as
Modulated Heterarchical Prediction Memory (mHPM). In mHPM, each module learns
to predict a succeeding input vector given the sequence of the input vectors
from lower layers and the context vectors from higher layers. The prediction is
fed into the lower layers as a context signal (top-down feedback signaling),
and into the higher layers as an input signal (bottom-up feedforward
signaling). Rewards modulate the learning rate in those modules to memorize
meaningful sequences effectively. In mHPM, each module updates in a local and
distributed way compared to conventional end-to-end learning with
backpropagation of the single objective loss. This local structure enables the
heterarchical network of modules. The second type is an innate, special-purpose
module representing various organs of the brain's subcortical system. Modules
modeling organs such as the amygdala, hippocampus, and reward center are
pre-programmed to enable instinctive behaviors. The hippocampus plays the role
of the simulator. It is an autoregressive prediction model of the top-most
level signal with a loop structure of memory, while cortical columns are lower
layers that provide detailed information to the simulation. The simulation
becomes the basis for learning with few trials and the deliberate planning
required for operant conditioning.
| [
{
"version": "v1",
"created": "Fri, 16 Dec 2022 18:00:21 GMT"
}
] | 1,671,408,000,000 | [
[
"Park",
"Deokgun",
""
],
[
"Mondol",
"Md Ashaduzzaman Rubel",
""
],
[
"Islam",
"SM Mazharul",
""
],
[
"Pothula",
"Aishwarya",
""
]
] |
2212.08681 | Vishal Pallagani | Vishal Pallagani, Bharath Muppasani, Keerthiram Murugesan, Francesca
Rossi, Lior Horesh, Biplav Srivastava, Francesco Fabiano, Andrea Loreggia | Plansformer: Generating Symbolic Plans using Transformers | 44 pages including supplementary material | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Large Language Models (LLMs) have been the subject of active research,
significantly advancing the field of Natural Language Processing (NLP). From
BERT to BLOOM, LLMs have surpassed state-of-the-art results in various natural
language tasks such as question answering, summarization, and text generation.
Many ongoing efforts focus on understanding LLMs' capabilities, including their
knowledge of the world, syntax, and semantics. However, extending the textual
prowess of LLMs to symbolic reasoning has been slow and predominantly focused
on tackling problems related to the mathematical field. In this paper, we
explore the use of LLMs for automated planning - a branch of AI concerned with
the realization of action sequences (plans) to achieve a goal, typically
executed by intelligent agents, autonomous robots, and unmanned vehicles. We
introduce Plansformer; an LLM fine-tuned on planning problems and capable of
generating plans with favorable behavior in terms of correctness and length
with reduced knowledge-engineering efforts. We also demonstrate the
adaptability of Plansformer in solving different planning domains with varying
complexities, owing to the transfer learning abilities of LLMs. For one
configuration of Plansformer, we achieve ~97% valid plans, out of which ~95%
are optimal for Towers of Hanoi - a puzzle-solving domain.
| [
{
"version": "v1",
"created": "Fri, 16 Dec 2022 19:06:49 GMT"
}
] | 1,671,494,400,000 | [
[
"Pallagani",
"Vishal",
""
],
[
"Muppasani",
"Bharath",
""
],
[
"Murugesan",
"Keerthiram",
""
],
[
"Rossi",
"Francesca",
""
],
[
"Horesh",
"Lior",
""
],
[
"Srivastava",
"Biplav",
""
],
[
"Fabiano",
"Francesco",
""
],
[
"Loreggia",
"Andrea",
""
]
] |
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