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2103.15975 | Dan Bohus | Dan Bohus, Sean Andrist, Ashley Feniello, Nick Saw, Mihai Jalobeanu,
Patrick Sweeney, Anne Loomis Thompson, Eric Horvitz | Platform for Situated Intelligence | 29 pages, 14 figures, Microsoft Research Technical Report | null | null | MSR-TR-2021-02 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce Platform for Situated Intelligence, an open-source framework
created to support the rapid development and study of multimodal,
integrative-AI systems. The framework provides infrastructure for sensing,
fusing, and making inferences from temporal streams of data across different
modalities, a set of tools that enable visualization and debugging, and an
ecosystem of components that encapsulate a variety of perception and processing
technologies. These assets jointly provide the means for rapidly constructing
and refining multimodal, integrative-AI systems, while retaining the efficiency
and performance characteristics required for deployment in open-world settings.
| [
{
"version": "v1",
"created": "Mon, 29 Mar 2021 22:30:15 GMT"
}
] | 1,617,148,800,000 | [
[
"Bohus",
"Dan",
""
],
[
"Andrist",
"Sean",
""
],
[
"Feniello",
"Ashley",
""
],
[
"Saw",
"Nick",
""
],
[
"Jalobeanu",
"Mihai",
""
],
[
"Sweeney",
"Patrick",
""
],
[
"Thompson",
"Anne Loomis",
""
],
[
"Horvitz",
"Eric",
""
]
] |
2103.16176 | Ildar Batyrshin Z. | Ildar Batyrshin | Contracting and Involutive Negations of Probability Distributions | 12 pages, 2 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | A dozen papers have considered the concept of negation of probability
distributions (pd) introduced by Yager. Usually, such negations are generated
point-by-point by functions defined on a set of probability values and called
here negators. Recently it was shown that Yager negator plays a crucial role in
the definition of pd-independent linear negators: any linear negator is a
function of Yager negator. Here, we prove that the sequence of multiple
negations of pd generated by a linear negator converges to the uniform
distribution with maximal entropy. We show that any pd-independent negator is
non-involutive, and any non-trivial linear negator is strictly contracting.
Finally, we introduce an involutive negator in the class of pd-dependent
negators that generates an involutive negation of probability distributions.
| [
{
"version": "v1",
"created": "Tue, 30 Mar 2021 08:58:08 GMT"
}
] | 1,617,148,800,000 | [
[
"Batyrshin",
"Ildar",
""
]
] |
2103.16177 | Jo\v{z}e Ro\v{z}anec | Patrik Zajec, Jo\v{z}e M. Ro\v{z}anec, Inna Novalija, Bla\v{z}
Fortuna, Dunja Mladeni\'c, Klemen Kenda | Towards Active Learning Based Smart Assistant for Manufacturing | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A general approach for building a smart assistant that guides a user from a
forecast generated by a machine learning model through a sequence of
decision-making steps is presented. We develop a methodology to build such a
system. The system is demonstrated on a demand forecasting use case in
manufacturing. The methodology can be extended to several use cases in
manufacturing. The system provides means for knowledge acquisition, gathering
data from users. We envision active learning can be used to get data labels
where labeled data is scarce.
| [
{
"version": "v1",
"created": "Tue, 30 Mar 2021 08:58:40 GMT"
}
] | 1,617,148,800,000 | [
[
"Zajec",
"Patrik",
""
],
[
"Rožanec",
"Jože M.",
""
],
[
"Novalija",
"Inna",
""
],
[
"Fortuna",
"Blaž",
""
],
[
"Mladenić",
"Dunja",
""
],
[
"Kenda",
"Klemen",
""
]
] |
2103.16692 | Chao Gao | Chao Gao | On AO*, Proof Number Search and Minimax Search | 6 pages, 1 page reference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We discuss the interconnections between AO*, adversarial game-searching
algorithms, e.g., proof number search and minimax search. The former was
developed in the context of a general AND/OR graph model, while the latter were
mostly presented in game-trees which are sometimes modeled using AND/OR trees.
It is thus worth investigating to what extent these algorithms are related and
how they are connected. In this paper, we explicate the interconnections
between these search paradigms. We argue that generalized proof number search
might be regarded as a more informed replacement of AO* for solving arbitrary
AND/OR graphs, and the minimax principle might also extended to use dual
heuristics.
| [
{
"version": "v1",
"created": "Tue, 30 Mar 2021 21:27:40 GMT"
}
] | 1,617,235,200,000 | [
[
"Gao",
"Chao",
""
]
] |
2103.16704 | Hongjing Lu | Hongjing Lu, Nicholas Ichien, Keith J. Holyoak | Probabilistic Analogical Mapping with Semantic Relation Networks | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The human ability to flexibly reason using analogies with domain-general
content depends on mechanisms for identifying relations between concepts, and
for mapping concepts and their relations across analogs. Building on a recent
model of how semantic relations can be learned from non-relational word
embeddings, we present a new computational model of mapping between two
analogs. The model adopts a Bayesian framework for probabilistic graph
matching, operating on semantic relation networks constructed from distributed
representations of individual concepts and of relations between concepts.
Through comparisons of model predictions with human performance in a novel
mapping task requiring integration of multiple relations, as well as in several
classic studies, we demonstrate that the model accounts for a broad range of
phenomena involving analogical mapping by both adults and children. We also
show the potential for extending the model to deal with analog retrieval. Our
approach demonstrates that human-like analogical mapping can emerge from
comparison mechanisms applied to rich semantic representations of individual
concepts and relations.
| [
{
"version": "v1",
"created": "Tue, 30 Mar 2021 22:14:13 GMT"
},
{
"version": "v2",
"created": "Sat, 29 May 2021 20:52:03 GMT"
},
{
"version": "v3",
"created": "Tue, 5 Oct 2021 03:43:18 GMT"
}
] | 1,633,478,400,000 | [
[
"Lu",
"Hongjing",
""
],
[
"Ichien",
"Nicholas",
""
],
[
"Holyoak",
"Keith J.",
""
]
] |
2103.17245 | Enis Karaarslan Dr. | \"Ozg\"ur Dogan, Oguzhan Sahin, Enis Karaarslan | Digital Twin Based Disaster Management System Proposal: DT-DMS | 5 pages, 6 figures | Journal of Emerging Computer Technologies (JECT), 2021, Vol:1 (2),
25-30 | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The damage and the impact of natural disasters are becoming more destructive
with the increase of urbanization. Today's metropolitan cities are not
sufficiently prepared for the pre and post-disaster situations. Digital Twin
technology can provide a solution. A virtual copy of the physical city could be
created by collecting data from sensors of the Internet of Things (IoT) devices
and stored on the cloud infrastructure. This virtual copy is kept current and
up to date with the continuous flow of the data coming from the sensors. We
propose a disaster management system utilizing machine learning called DT-DMS
is used to support decision-making mechanisms. This study aims to show how to
educate and prepare emergency center staff by simulating potential disaster
situations on the virtual copy. The event of a disaster will be simulated
allowing emergency center staff to make decisions and depicting the potential
outcomes of these decisions. A rescue operation after an earthquake is
simulated. Test results are promising and the simulation scope is planned to be
extended.
| [
{
"version": "v1",
"created": "Wed, 31 Mar 2021 17:47:15 GMT"
}
] | 1,617,235,200,000 | [
[
"Dogan",
"Özgür",
""
],
[
"Sahin",
"Oguzhan",
""
],
[
"Karaarslan",
"Enis",
""
]
] |
2104.00060 | Jingkai Chen | Jingkai Chen, Yuening Zhang, Cheng Fang, Brian C. Williams | Generalized Conflict-directed Search for Optimal Ordering Problems | Accepted at SOCS2021. 9 pages, 4 figures, 2 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Solving planning and scheduling problems for multiple tasks with highly
coupled state and temporal constraints is notoriously challenging. An appealing
approach to effectively decouple the problem is to judiciously order the events
such that decisions can be made over sequences of tasks. As many problems
encountered in practice are over-constrained, we must instead find relaxed
solutions in which certain requirements are dropped. This motivates a
formulation of optimality with respect to the costs of relaxing constraints and
the problem of finding an optimal ordering under which this relaxing cost is
minimum. In this paper, we present Generalized Conflict-directed Ordering
(GCDO), a branch-and-bound ordering method that generates an optimal total
order of events by leveraging the generalized conflicts of both inconsistency
and suboptimality from sub-solvers for cost estimation and solution space
pruning. Due to its ability to reason over generalized conflicts, GCDO is much
more efficient in finding high-quality total orders than the previous
conflict-directed approach CDITO. We demonstrate this by benchmarking on
temporal network configuration problems, which involves managing networks over
time and makes necessary tradeoffs between network flows against CDITO and
Mixed Integer-Linear Programing (MILP). Our algorithm is able to solve two
orders of magnitude more benchmark problems to optimality and twice the
problems compared to CDITO and MILP within a runtime limit, respectively.
| [
{
"version": "v1",
"created": "Wed, 31 Mar 2021 18:46:48 GMT"
}
] | 1,617,321,600,000 | [
[
"Chen",
"Jingkai",
""
],
[
"Zhang",
"Yuening",
""
],
[
"Fang",
"Cheng",
""
],
[
"Williams",
"Brian C.",
""
]
] |
2104.00362 | Martin K\"appel | Martin K\"appel, Stefan Jablonski, Stefan Sch\"onig | Evaluating Predictive Business Process Monitoring Approaches on Small
Event Logs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predictive business process monitoring is concerned with the prediction how a
running process instance will unfold up to its completion at runtime. Most of
the proposed approaches rely on a wide number of different machine learning
(ML) techniques. In the last years numerous comparative studies, reviews, and
benchmarks of such approaches where published and revealed that they can be
successfully applied for different prediction targets. ML techniques require a
qualitatively and quantitatively sufficient data set. However, there are many
situations in business process management (BPM) where only a quantitatively
insufficient data set is available. The problem of insufficient data in the
context of BPM is still neglected. Hence, none of the comparative studies or
benchmarks investigates the performance of predictive business process
monitoring techniques in environments with small data sets. In this paper an
evaluation framework for comparing existing approaches with regard to their
suitability for small data sets is developed and exemplarily applied to
state-of-the-art approaches in predictive business process monitoring.
| [
{
"version": "v1",
"created": "Thu, 1 Apr 2021 09:36:04 GMT"
},
{
"version": "v2",
"created": "Tue, 20 Apr 2021 06:40:02 GMT"
}
] | 1,618,963,200,000 | [
[
"Käppel",
"Martin",
""
],
[
"Jablonski",
"Stefan",
""
],
[
"Schönig",
"Stefan",
""
]
] |
2104.00698 | Anssi Kanervisto | Dylan Ashley, Anssi Kanervisto, Brendan Bennett | Back to Square One: Superhuman Performance in Chutes and Ladders Through
Deep Neural Networks and Tree Search | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We present AlphaChute: a state-of-the-art algorithm that achieves superhuman
performance in the ancient game of Chutes and Ladders. We prove that our
algorithm converges to the Nash equilibrium in constant time, and therefore is
-- to the best of our knowledge -- the first such formal solution to this game.
Surprisingly, despite all this, our implementation of AlphaChute remains
relatively straightforward due to domain-specific adaptations. We provide the
source code for AlphaChute here in our Appendix.
| [
{
"version": "v1",
"created": "Thu, 1 Apr 2021 18:08:55 GMT"
}
] | 1,617,580,800,000 | [
[
"Ashley",
"Dylan",
""
],
[
"Kanervisto",
"Anssi",
""
],
[
"Bennett",
"Brendan",
""
]
] |
2104.01190 | Fang Li | Fang Li, Huaduo Wang, Gopal Gupta | grASP: A Graph Based ASP-Solver and Justification System | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Answer set programming (ASP) is a popular nonmonotonic-logic based paradigm
for knowledge representation and solving combinatorial problems. Computing the
answer set of an ASP program is NP-hard in general, and researchers have been
investing significant effort to speed it up. The majority of current ASP
solvers employ SAT solver-like technology to find these answer sets. As a
result, justification for why a literal is in the answer set is hard to
produce. There are dependency graph based approaches to find answer sets, but
due to the representational limitations of dependency graphs, such approaches
are limited. We propose a novel dependency graph-based approach for finding
answer sets in which conjunction of goals is explicitly represented as a node
which allows arbitrary answer set programs to be uniformly represented. Our
representation preserves causal relationships allowing for justification for
each literal in the answer set to be elegantly found. Performance results from
an implementation are also reported. Our work paves the way for computing
answer sets without grounding a program.
| [
{
"version": "v1",
"created": "Fri, 2 Apr 2021 18:16:20 GMT"
}
] | 1,617,667,200,000 | [
[
"Li",
"Fang",
""
],
[
"Wang",
"Huaduo",
""
],
[
"Gupta",
"Gopal",
""
]
] |
2104.01910 | Yuanpeng He | Yuanpeng He | Combining conflicting ordinal quantum evidences utilizing individual
reliability | 44 pages, 20 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How to combine uncertain information from different sources has been a hot
topic for years. However, with respect to ordinal quantum evidences contained
in information, there is no any referable work which is able to provide a
solution to this kind of problem. Besides, the method to dispel uncertainty of
quantum information is still an open issue. Therefore, in this paper, a
specially designed method is designed to provide an excellent method which
improves the combination of ordinal quantum evidences reasonably and reduce the
effects brought by uncertainty contained in quantum information simultaneously.
Besides, some actual applications are provided to verify the correctness and
validity of the proposed method.
| [
{
"version": "v1",
"created": "Thu, 1 Apr 2021 13:18:38 GMT"
}
] | 1,617,667,200,000 | [
[
"He",
"Yuanpeng",
""
]
] |
2104.01966 | Martin Garriga | Damian Andrew Tamburri, Willem-Jan Van den Heuvel, Martin Garriga | DataOps for Societal Intelligence: a Data Pipeline for Labor Market
Skills Extraction and Matching | null | 2020 IEEE 21st International Conference on Information Reuse and
Integration for Data Science (IRI), Las Vegas, NV, USA, 2020, pp. 391-394 | 10.1109/IRI49571.2020.00063 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Big Data analytics supported by AI algorithms can support skills localization
and retrieval in the context of a labor market intelligence problem. We
formulate and solve this problem through specific DataOps models, blending data
sources from administrative and technical partners in several countries into
cooperation, creating shared knowledge to support policy and decision-making.
We then focus on the critical task of skills extraction from resumes and
vacancies featuring state-of-the-art machine learning models. We showcase
preliminary results with applied machine learning on real data from the
employment agencies of the Netherlands and the Flemish region in Belgium. The
final goal is to match these skills to standard ontologies of skills, jobs and
occupations.
| [
{
"version": "v1",
"created": "Mon, 5 Apr 2021 15:37:25 GMT"
}
] | 1,617,667,200,000 | [
[
"Tamburri",
"Damian Andrew",
""
],
[
"Heuvel",
"Willem-Jan Van den",
""
],
[
"Garriga",
"Martin",
""
]
] |
2104.02425 | Kashif Ahmad | Senthil Kumar Jagatheesaperumal, Mohamed Rahouti, Kashif Ahmad, Ala
Al-Fuqaha, Mohsen Guizani | The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review
of Applications, Techniques, Challenges, and Future Research Directions | 33 pages, 10 figures, 7 tables | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The increasing need for economic, safe, and sustainable smart manufacturing
combined with novel technological enablers, has paved the way for Artificial
Intelligence (AI) and Big Data in support of smart manufacturing. This implies
a substantial integration of AI, Industrial Internet of Things (IIoT),
Robotics, Big data, Blockchain, 5G communications, in support of smart
manufacturing and the dynamical processes in modern industries. In this paper,
we provide a comprehensive overview of different aspects of AI and Big Data in
Industry 4.0 with a particular focus on key applications, techniques, the
concepts involved, key enabling technologies, challenges, and research
perspective towards deployment of Industry 5.0. In detail, we highlight and
analyze how the duo of AI and Big Data is helping in different applications of
Industry 4.0. We also highlight key challenges in a successful deployment of AI
and Big Data methods in smart industries with a particular emphasis on
data-related issues, such as availability, bias, auditing, management,
interpretability, communication, and different adversarial attacks and security
issues. In a nutshell, we have explored the significance of AI and Big data
towards Industry 4.0 applications through panoramic reviews and discussions. We
believe, this work will provide a baseline for future research in the domain.
| [
{
"version": "v1",
"created": "Tue, 6 Apr 2021 11:08:02 GMT"
},
{
"version": "v2",
"created": "Wed, 7 Apr 2021 10:59:47 GMT"
}
] | 1,617,840,000,000 | [
[
"Jagatheesaperumal",
"Senthil Kumar",
""
],
[
"Rahouti",
"Mohamed",
""
],
[
"Ahmad",
"Kashif",
""
],
[
"Al-Fuqaha",
"Ala",
""
],
[
"Guizani",
"Mohsen",
""
]
] |
2104.02545 | Xugui Zhou | Xugui Zhou, Bulbul Ahmed, James H. Aylor, Philip Asare, Homa Alemzadeh | Data-driven Design of Context-aware Monitors for Hazard Prediction in
Artificial Pancreas Systems | 13 pages, 9 figures, to appear in the 51st IEEE/IFIP International
Conference on Dependable Systems and Networks (DSN 2021) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Medical Cyber-physical Systems (MCPS) are vulnerable to accidental or
malicious faults that can target their controllers and cause safety hazards and
harm to patients. This paper proposes a combined model and data-driven approach
for designing context-aware monitors that can detect early signs of hazards and
mitigate them in MCPS. We present a framework for formal specification of
unsafe system context using Signal Temporal Logic (STL) combined with an
optimization method for patient-specific refinement of STL formulas based on
real or simulated faulty data from the closed-loop system for the generation of
monitor logic. We evaluate our approach in simulation using two
state-of-the-art closed-loop Artificial Pancreas Systems (APS). The results
show the context-aware monitor achieves up to 1.4 times increase in average
hazard prediction accuracy (F1-score) over several baseline monitors, reduces
false-positive and false-negative rates, and enables hazard mitigation with a
54% success rate while decreasing the average risk for patients.
| [
{
"version": "v1",
"created": "Tue, 6 Apr 2021 14:36:33 GMT"
},
{
"version": "v2",
"created": "Tue, 13 Apr 2021 05:22:04 GMT"
}
] | 1,618,358,400,000 | [
[
"Zhou",
"Xugui",
""
],
[
"Ahmed",
"Bulbul",
""
],
[
"Aylor",
"James H.",
""
],
[
"Asare",
"Philip",
""
],
[
"Alemzadeh",
"Homa",
""
]
] |
2104.02621 | Zhenhua Chen | Zhenhua Chen, Xiwen Li, Qian Lou, David Crandall | How to Accelerate Capsule Convolutions in Capsule Networks | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | How to improve the efficiency of routing procedures in CapsNets has been
studied a lot. However, the efficiency of capsule convolutions has largely been
neglected. Capsule convolution, which uses capsules rather than neurons as the
basic computation unit, makes it incompatible with current deep learning
frameworks' optimization solution. As a result, capsule convolutions are
usually very slow with these frameworks. We observe that capsule convolutions
can be considered as the operations of `multiplication of multiple small
matrics' plus tensor-based combination. Based on this observation, we develop
two acceleration schemes with CUDA APIs and test them on a custom CapsNet. The
result shows that our solution achieves a 4X acceleration.
| [
{
"version": "v1",
"created": "Tue, 6 Apr 2021 15:57:49 GMT"
}
] | 1,617,753,600,000 | [
[
"Chen",
"Zhenhua",
""
],
[
"Li",
"Xiwen",
""
],
[
"Lou",
"Qian",
""
],
[
"Crandall",
"David",
""
]
] |
2104.02997 | Stefan Edelkamp | Stefan Edelkamp | On the Power of Refined Skat Selection | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Skat is a fascinating combinatorial card game, show-casing many of the
intrinsic challenges for modern AI systems such as cooperative and adversarial
behaviors (among the players), randomness (in the deal), and partial knowledge
(due to hidden cards). Given the larger number of tricks and higher degree of
uncertainty, reinforcement learning is less effective compared to classical
board games like Chess and Go. As within the game of Bridge, in Skat we have a
bidding and trick-taking stage. Prior to the trick-taking and as part of the
bidding process, one phase in the game is to select two skat cards, whose
quality may influence subsequent playing performance drastically. This paper
looks into different skat selection strategies. Besides predicting the
probability of winning and other hand strength functions we propose hard
expert-rules and a scoring functions based on refined skat evaluation features.
Experiments emphasize the impact of the refined skat putting algorithm on the
playing performance of the bots, especially for AI bidding and AI game
selection.
| [
{
"version": "v1",
"created": "Wed, 7 Apr 2021 08:54:58 GMT"
}
] | 1,617,840,000,000 | [
[
"Edelkamp",
"Stefan",
""
]
] |
2104.03252 | Maaike Van Roy | Maaike Van Roy, Pieter Robberechts, Wen-Chi Yang, Luc De Raedt, Jesse
Davis | Leaving Goals on the Pitch: Evaluating Decision Making in Soccer | Add missing funding | 2021 MIT Sloan Sports Analytics Conference | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Analysis of the popular expected goals (xG) metric in soccer has determined
that a (slightly) smaller number of high-quality attempts will likely yield
more goals than a slew of low-quality ones. This observation has driven a
change in shooting behavior. Teams are passing up on shots from outside the
penalty box, in the hopes of generating a better shot closer to goal later on.
This paper evaluates whether this decrease in long-distance shots is warranted.
Therefore, we propose a novel generic framework to reason about decision-making
in soccer by combining techniques from machine learning and artificial
intelligence (AI). First, we model how a team has behaved offensively over the
course of two seasons by learning a Markov Decision Process (MDP) from event
stream data. Second, we use reasoning techniques arising from the AI literature
on verification to each team's MDP. This allows us to reason about the efficacy
of certain potential decisions by posing counterfactual questions to the MDP.
Our key conclusion is that teams would score more goals if they shot more often
from outside the penalty box in a small number of team-specific locations. The
proposed framework can easily be extended and applied to analyze other aspects
of the game.
| [
{
"version": "v1",
"created": "Wed, 7 Apr 2021 16:56:31 GMT"
},
{
"version": "v2",
"created": "Thu, 16 Feb 2023 10:31:20 GMT"
}
] | 1,676,592,000,000 | [
[
"Van Roy",
"Maaike",
""
],
[
"Robberechts",
"Pieter",
""
],
[
"Yang",
"Wen-Chi",
""
],
[
"De Raedt",
"Luc",
""
],
[
"Davis",
"Jesse",
""
]
] |
2104.03571 | Paolo Liberatore | Paolo Liberatore | On Mixed Iterated Revisions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Several forms of iterable belief change exist, differing in the kind of
change and its strength: some operators introduce formulae, others remove them;
some add formulae unconditionally, others only as additions to the previous
beliefs; some only relative to the current situation, others in all possible
cases. A sequence of changes may involve several of them: for example, the
first step is a revision, the second a contraction and the third a refinement
of the previous beliefs. The ten operators considered in this article are shown
to be all reducible to three: lexicographic revision, refinement and severe
withdrawal. In turn, these three can be expressed in terms of lexicographic
revision at the cost of restructuring the sequence. This restructuring needs
not to be done explicitly: an algorithm that works on the original sequence is
shown. The complexity of mixed sequences of belief change operators is also
analyzed. Most of them require only a polynomial number of calls to a
satisfiability checker, some are even easier.
| [
{
"version": "v1",
"created": "Thu, 8 Apr 2021 07:34:56 GMT"
}
] | 1,617,926,400,000 | [
[
"Liberatore",
"Paolo",
""
]
] |
2104.04008 | Mark Keane | Mohammed Temraz and Eoin Kenny and Elodie Ruelle and Laurence Shalloo
and Barry Smyth and Mark T Keane | Handling Climate Change Using Counterfactuals: Using Counterfactuals in
Data Augmentation to Predict Crop Growth in an Uncertain Climate Future | 15 pages, 6 figures, 3 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Climate change poses a major challenge to humanity, especially in its impact
on agriculture, a challenge that a responsible AI should meet. In this paper,
we examine a CBR system (PBI-CBR) designed to aid sustainable dairy farming by
supporting grassland management, through accurate crop growth prediction. As
climate changes, PBI-CBRs historical cases become less useful in predicting
future grass growth. Hence, we extend PBI-CBR using data augmentation, to
specifically handle disruptive climate events, using a counterfactual method
(from XAI). Study 1 shows that historical, extreme climate-events (climate
outlier cases) tend to be used by PBI-CBR to predict grass growth during
climate disrupted periods. Study 2 shows that synthetic outliers, generated as
counterfactuals on a outlier-boundary, improve the predictive accuracy of
PBICBR, during the drought of 2018. This study also shows that an
instance-based counterfactual method does better than a benchmark,
constraint-guided method.
| [
{
"version": "v1",
"created": "Thu, 8 Apr 2021 18:54:21 GMT"
}
] | 1,619,740,800,000 | [
[
"Temraz",
"Mohammed",
""
],
[
"Kenny",
"Eoin",
""
],
[
"Ruelle",
"Elodie",
""
],
[
"Shalloo",
"Laurence",
""
],
[
"Smyth",
"Barry",
""
],
[
"Keane",
"Mark T",
""
]
] |
2104.04278 | Tristan Cazenave | Tristan Cazenave | Batch Monte Carlo Tree Search | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Making inferences with a deep neural network on a batch of states is much
faster with a GPU than making inferences on one state after another. We build
on this property to propose Monte Carlo Tree Search algorithms using batched
inferences. Instead of using either a search tree or a transposition table we
propose to use both in the same algorithm. The transposition table contains the
results of the inferences while the search tree contains the statistics of
Monte Carlo Tree Search. We also propose to analyze multiple heuristics that
improve the search: the $\mu$ FPU, the Virtual Mean, the Last Iteration and the
Second Move heuristics. They are evaluated for the game of Go using a MobileNet
neural network.
| [
{
"version": "v1",
"created": "Fri, 9 Apr 2021 09:54:21 GMT"
}
] | 1,618,185,600,000 | [
[
"Cazenave",
"Tristan",
""
]
] |
2104.05003 | Chengjin Xu | Chengjin Xu, Mojtaba Nayyeri, Sahar Vahdati, and Jens Lehmann | Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph
Embeddings | Accepted by the 2021 International Joint Conference on Neural
Networks (IJCNN 2021) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Among the top approaches of recent years, link prediction using knowledge
graph embedding (KGE) models has gained significant attention for knowledge
graph completion. Various embedding models have been proposed so far, among
which, some recent KGE models obtain state-of-the-art performance on link
prediction tasks by using embeddings with a high dimension (e.g. 1000) which
accelerate the costs of training and evaluation considering the large scale of
KGs. In this paper, we propose a simple but effective performance boosting
strategy for KGE models by using multiple low dimensions in different
repetition rounds of the same model. For example, instead of training a model
one time with a large embedding size of 1200, we repeat the training of the
model 6 times in parallel with an embedding size of 200 and then combine the 6
separate models for testing while the overall numbers of adjustable parameters
are same (6*200=1200) and the total memory footprint remains the same. We show
that our approach enables different models to better cope with their
expressiveness issues on modeling various graph patterns such as symmetric,
1-n, n-1 and n-n. In order to justify our findings, we conduct experiments on
various KGE models. Experimental results on standard benchmark datasets, namely
FB15K, FB15K-237 and WN18RR, show that multiple low-dimensional models of the
same kind outperform the corresponding single high-dimensional models on link
prediction in a certain range and have advantages in training efficiency by
using parallel training while the overall numbers of adjustable parameters are
same.
| [
{
"version": "v1",
"created": "Sun, 11 Apr 2021 12:26:50 GMT"
},
{
"version": "v2",
"created": "Sun, 30 May 2021 08:51:14 GMT"
}
] | 1,622,505,600,000 | [
[
"Xu",
"Chengjin",
""
],
[
"Nayyeri",
"Mojtaba",
""
],
[
"Vahdati",
"Sahar",
""
],
[
"Lehmann",
"Jens",
""
]
] |
2104.05046 | Suyash Shandilya | Suyash Shandilya | Print Error Detection using Convolutional Neural Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper discusses the need of an automated system for detecting print
errors and the efficacy of Convolutional Neural Networks in such an
application. We recognise the need of a dataset containing print error samples
and propose a way to generate one artificially. We discuss the algorithms to
generate such data along with the limitaions and advantages of such an
apporach. Our final trained network gives a remarkable accuracy of 99.83\% in
testing. We further evaluate how such efficiency was achieved and what
modifications can be tested to further the results.
| [
{
"version": "v1",
"created": "Sun, 11 Apr 2021 16:30:17 GMT"
}
] | 1,618,272,000,000 | [
[
"Shandilya",
"Suyash",
""
]
] |
2104.05163 | Yan Haoyang | Haoyang Yan, Xiaolei Ma | Learning dynamic and hierarchical traffic spatiotemporal features with
Transformer | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traffic forecasting is an indispensable part of Intelligent transportation
systems (ITS), and long-term network-wide accurate traffic speed forecasting is
one of the most challenging tasks. Recently, deep learning methods have become
popular in this domain. As traffic data are physically associated with road
networks, most proposed models treat it as a spatiotemporal graph modeling
problem and use Graph Convolution Network (GCN) based methods. These GCN-based
models highly depend on a predefined and fixed adjacent matrix to reflect the
spatial dependency. However, the predefined fixed adjacent matrix is limited in
reflecting the actual dependence of traffic flow. This paper proposes a novel
model, Traffic Transformer, for spatial-temporal graph modeling and long-term
traffic forecasting to overcome these limitations. Transformer is the most
popular framework in Natural Language Processing (NLP). And by adapting it to
the spatiotemporal problem, Traffic Transformer hierarchically extracts
spatiotemporal features through data dynamically by multi-head attention and
masked multi-head attention mechanism, and fuse these features for traffic
forecasting. Furthermore, analyzing the attention weight matrixes can find the
influential part of road networks, allowing us to learn the traffic networks
better. Experimental results on the public traffic network datasets and
real-world traffic network datasets generated by ourselves demonstrate our
proposed model achieves better performance than the state-of-the-art ones.
| [
{
"version": "v1",
"created": "Mon, 12 Apr 2021 02:29:58 GMT"
}
] | 1,618,272,000,000 | [
[
"Yan",
"Haoyang",
""
],
[
"Ma",
"Xiaolei",
""
]
] |
2104.05234 | Shi Min | Cong Li, Min Shi, Bo Qu, Xiang Li | Deep Attributed Network Representation Learning via Attribute Enhanced
Neighborhood | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Attributed network representation learning aims at learning node embeddings
by integrating network structure and attribute information. It is a challenge
to fully capture the microscopic structure and the attribute semantics
simultaneously, where the microscopic structure includes the one-step, two-step
and multi-step relations, indicating the first-order, second-order and
high-order proximity of nodes, respectively. In this paper, we propose a deep
attributed network representation learning via attribute enhanced neighborhood
(DANRL-ANE) model to improve the robustness and effectiveness of node
representations. The DANRL-ANE model adopts the idea of the autoencoder, and
expands the decoder component to three branches to capture different order
proximity. We linearly combine the adjacency matrix with the attribute
similarity matrix as the input of our model, where the attribute similarity
matrix is calculated by the cosine similarity between the attributes based on
the social homophily. In this way, we preserve the second-order proximity to
enhance the robustness of DANRL-ANE model on sparse networks, and deal with the
topological and attribute information simultaneously. Moreover, the sigmoid
cross-entropy loss function is extended to capture the neighborhood character,
so that the first-order proximity is better preserved. We compare our model
with the state-of-the-art models on five real-world datasets and two network
analysis tasks, i.e., link prediction and node classification. The DANRL-ANE
model performs well on various networks, even on sparse networks or networks
with isolated nodes given the attribute information is sufficient.
| [
{
"version": "v1",
"created": "Mon, 12 Apr 2021 07:03:16 GMT"
}
] | 1,618,272,000,000 | [
[
"Li",
"Cong",
""
],
[
"Shi",
"Min",
""
],
[
"Qu",
"Bo",
""
],
[
"Li",
"Xiang",
""
]
] |
2104.05235 | Km Poonam | Km Poonam, Rajlakshmi Guha, Partha P Chakrabarti | Artificial Intelligence Methods Based Hierarchical Classification of
Frontotemporal Dementia to Improve Diagnostic Predictability | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Patients with Frontotemporal Dementia (FTD) have impaired cognitive
abilities, executive and behavioral traits, loss of language ability, and
decreased memory capabilities. Based on the distinct patterns of cortical
atrophy and symptoms, the FTD spectrum primarily includes three variants:
behavioral variant FTD (bvFTD), non-fluent variant primary progressive aphasia
(nfvPPA), and semantic variant primary progressive aphasia (svPPA). The purpose
of this study is to classify MRI images of every single subject into one of the
spectrums of the FTD in a hierarchical order by applying data-driven techniques
of Artificial Intelligence (AI) on cortical thickness data. This data is
computed by FreeSurfer software. We used the Smallest Univalue Segment
Assimilating Nucleus (SUSAN) technique to minimize the noise in cortical
thickness data. Specifically, we took 204 subjects from the frontotemporal
lobar degeneration neuroimaging initiative (NIFTD) database to validate this
approach, and each subject was diagnosed in one of the diagnostic categories
(bvFTD, svPPA, nfvPPA and cognitively normal). Our proposed automated
classification model yielded classification accuracy of 86.5, 76, and 72.7 with
support vector machine (SVM), linear discriminant analysis (LDA), and Naive
Bayes methods, respectively, in 10-fold cross-validation analysis, which is a
significant improvement on a traditional single multi-class model with an
accuracy of 82.7, 73.4, and 69.2.
| [
{
"version": "v1",
"created": "Mon, 12 Apr 2021 07:04:11 GMT"
}
] | 1,618,272,000,000 | [
[
"Poonam",
"Km",
""
],
[
"Guha",
"Rajlakshmi",
""
],
[
"Chakrabarti",
"Partha P",
""
]
] |
2104.05314 | Christian Janiesch | Christian Janiesch, Patrick Zschech, Kai Heinrich | Machine learning and deep learning | Published online first in Electronic Markets | null | 10.1007/s12525-021-00475-2 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Today, intelligent systems that offer artificial intelligence capabilities
often rely on machine learning. Machine learning describes the capacity of
systems to learn from problem-specific training data to automate the process of
analytical model building and solve associated tasks. Deep learning is a
machine learning concept based on artificial neural networks. For many
applications, deep learning models outperform shallow machine learning models
and traditional data analysis approaches. In this article, we summarize the
fundamentals of machine learning and deep learning to generate a broader
understanding of the methodical underpinning of current intelligent systems. In
particular, we provide a conceptual distinction between relevant terms and
concepts, explain the process of automated analytical model building through
machine learning and deep learning, and discuss the challenges that arise when
implementing such intelligent systems in the field of electronic markets and
networked business. These naturally go beyond technological aspects and
highlight issues in human-machine interaction and artificial intelligence
servitization.
| [
{
"version": "v1",
"created": "Mon, 12 Apr 2021 09:54:12 GMT"
},
{
"version": "v2",
"created": "Wed, 14 Apr 2021 10:31:01 GMT"
}
] | 1,618,444,800,000 | [
[
"Janiesch",
"Christian",
""
],
[
"Zschech",
"Patrick",
""
],
[
"Heinrich",
"Kai",
""
]
] |
2104.05331 | Rushil Thareja | Rushil Thareja | MeToo Tweets Sentiment Analysis Using Multi Modal frameworks | the paper underwent peer review after submission to arXiv and is
found to be unsuitable for publication, the authors therefore choose to
withdraw it to prevent its dissemination in the scientific community and work
to update the work | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, We present our approach for IEEEBigMM 2020, Grand Challenge
(BMGC), Identifying senti-ments from tweets related to the MeToo movement. The
modelis based on an ensemble of Convolutional Neural Network,Bidirectional LSTM
and a DNN for final classification. Thispaper is aimed at providing a detailed
analysis of the modeland the results obtained. We have ranked 5th out of 10
teamswith a score of 0.51491
| [
{
"version": "v1",
"created": "Mon, 12 Apr 2021 10:18:33 GMT"
},
{
"version": "v2",
"created": "Fri, 21 Apr 2023 09:56:06 GMT"
}
] | 1,682,294,400,000 | [
[
"Thareja",
"Rushil",
""
]
] |
2104.05407 | Vladimir Ivanov | V. K. Ivanov, I. V. Obraztsov, B. V. Palyukh | Implementing an expert system to evaluate technical solutions
innovativeness | 12 pages, in Russian | Software & Systems. 2019. T. 4 (32) | 10.15827/0236-235X.128.696-707 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The paper presents a possible solution to the problem of algorithmization for
quantifying inno-vativeness indicators of technical products, inventions and
technologies. The concepts of technological nov-elty, relevance and
implementability as components of product innovation criterion are introduced.
Authors propose a model and algorithm to calculate every of these indicators of
innovativeness under conditions of incompleteness and inaccuracy, and sometimes
inconsistency of the initial information. The paper describes the developed
specialized software that is a promising methodological tool for using interval
estimations in accordance with the theory of evidence. These estimations are
used in the analysis of complex multicomponent systems, aggregations of large
volumes of fuzzy and incomplete data of various structures. Composition and
structure of a multi-agent expert system are presented. The purpose of such
system is to process groups of measurement results and to estimate indicators
values of objects innovativeness. The paper defines active elements of the
system, their functionality, roles, interaction order, input and output
inter-faces, as well as the general software functioning algorithm. It
describes implementation of software modules and gives an example of solving a
specific problem to determine the level of technical products innovation.
| [
{
"version": "v1",
"created": "Fri, 26 Mar 2021 10:11:44 GMT"
}
] | 1,618,272,000,000 | [
[
"Ivanov",
"V. K.",
""
],
[
"Obraztsov",
"I. V.",
""
],
[
"Palyukh",
"B. V.",
""
]
] |
2104.05416 | Yuanpeng He | Yuanpeng He | An approach utilizing negation of extended-dimensional vector of
disposing mass for ordinal evidences combination in a fuzzy environment | 28 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How to measure the degree of uncertainty of a given frame of discernment has
been a hot topic for years. A lot of meaningful works have provided some
effective methods to measure the degree properly. However, a crucial factor,
sequence of propositions, is missing in the definition of traditional frame of
discernment. In this paper, a detailed definition of ordinal frame of
discernment has been provided. Besides, an innovative method utilizing a
concept of computer vision to combine the order of propositions and the mass of
them is proposed to better manifest relationships between the two important
element of the frame of discernment. More than that, a specially designed
method covering some powerful tools in indicating the degree of uncertainty of
a traditional frame of discernment is also offered to give an indicator of
level of uncertainty of an ordinal frame of discernment on the level of vector.
| [
{
"version": "v1",
"created": "Tue, 6 Apr 2021 09:35:29 GMT"
}
] | 1,618,272,000,000 | [
[
"He",
"Yuanpeng",
""
]
] |
2104.05423 | Stefan Edelkamp | Stefan Edelkamp | Knowledge-Based Paranoia Search in Trick-Taking | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes \emph{knowledge-based paraonoia search} (KBPS) to find
forced wins during trick-taking in the card game Skat; for some one of the most
interesting card games for three players. It combines efficient partial
information game-tree search with knowledge representation and reasoning. This
worst-case analysis, initiated after a small number of tricks, leads to a
prioritized choice of cards. We provide variants of KBPS for the declarer and
the opponents, and an approximation to find a forced win against most worlds in
the belief space. Replaying thousands of expert games, our evaluation indicates
that the AIs with the new algorithms perform better than humans in their play,
achieving an average score of over 1,000 points in the agreed standard for
evaluating Skat tournaments, the extended Seeger system.
| [
{
"version": "v1",
"created": "Wed, 7 Apr 2021 09:12:45 GMT"
}
] | 1,618,272,000,000 | [
[
"Edelkamp",
"Stefan",
""
]
] |
2104.05755 | Evangelos Georganas | Evangelos Georganas, Dhiraj Kalamkar, Sasikanth Avancha, Menachem
Adelman, Deepti Aggarwal, Cristina Anderson, Alexander Breuer, Jeremy
Bruestle, Narendra Chaudhary, Abhisek Kundu, Denise Kutnick, Frank Laub,
Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Brian Retford,
Barukh Ziv, Alexander Heinecke | Tensor Processing Primitives: A Programming Abstraction for Efficiency
and Portability in Deep Learning & HPC Workloads | null | null | 10.1145/3458817.3476206 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | During the past decade, novel Deep Learning (DL) algorithms, workloads and
hardware have been developed to tackle a wide range of problems. Despite the
advances in workload and hardware ecosystems, the programming methodology of DL
systems is stagnant. DL workloads leverage either highly-optimized, yet
platform-specific and inflexible kernels from DL libraries, or in the case of
novel operators, reference implementations are built via DL framework
primitives with underwhelming performance. This work introduces the Tensor
Processing Primitives (TPP), a programming abstraction striving for efficient,
portable implementation of DL workloads with high-productivity. TPPs define a
compact, yet versatile set of 2D-tensor operators (or a virtual Tensor ISA),
which subsequently can be utilized as building-blocks to construct complex
operators on high-dimensional tensors. The TPP specification is
platform-agnostic, thus code expressed via TPPs is portable, whereas the TPP
implementation is highly-optimized and platform-specific. We demonstrate the
efficacy and viability of our approach using standalone kernels and end-to-end
DL & HPC workloads expressed entirely via TPPs that outperform state-of-the-art
implementations on multiple platforms.
| [
{
"version": "v1",
"created": "Mon, 12 Apr 2021 18:35:49 GMT"
},
{
"version": "v2",
"created": "Wed, 14 Apr 2021 15:38:38 GMT"
},
{
"version": "v3",
"created": "Thu, 26 Aug 2021 17:27:06 GMT"
},
{
"version": "v4",
"created": "Tue, 30 Nov 2021 23:40:39 GMT"
}
] | 1,638,403,200,000 | [
[
"Georganas",
"Evangelos",
""
],
[
"Kalamkar",
"Dhiraj",
""
],
[
"Avancha",
"Sasikanth",
""
],
[
"Adelman",
"Menachem",
""
],
[
"Aggarwal",
"Deepti",
""
],
[
"Anderson",
"Cristina",
""
],
[
"Breuer",
"Alexander",
""
],
[
"Bruestle",
"Jeremy",
""
],
[
"Chaudhary",
"Narendra",
""
],
[
"Kundu",
"Abhisek",
""
],
[
"Kutnick",
"Denise",
""
],
[
"Laub",
"Frank",
""
],
[
"Md",
"Vasimuddin",
""
],
[
"Misra",
"Sanchit",
""
],
[
"Mohanty",
"Ramanarayan",
""
],
[
"Pabst",
"Hans",
""
],
[
"Retford",
"Brian",
""
],
[
"Ziv",
"Barukh",
""
],
[
"Heinecke",
"Alexander",
""
]
] |
2104.05874 | Matt Calder | Matt Calder | Gradient Kernel Regression | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article a surprising result is demonstrated using the neural tangent
kernel. This kernel is defined as the inner product of the vector of the
gradient of an underlying model evaluated at training points. This kernel is
used to perform kernel regression. The surprising thing is that the accuracy of
that regression is independent of the accuracy of the underlying network.
| [
{
"version": "v1",
"created": "Tue, 13 Apr 2021 00:32:34 GMT"
}
] | 1,618,358,400,000 | [
[
"Calder",
"Matt",
""
]
] |
2104.05931 | Taeyoung Kim | Taeyoung Kim, Luiz Felipe Vecchietti, Kyujin Choi, Sanem Sariel,
Dongsoo Har | Two-stage training algorithm for AI robot soccer | This work is submitted to Peer J Computer Science and is currently
under review. If published, we put the DOI to the paper | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In multi-agent reinforcement learning, the cooperative learning behavior of
agents is very important. In the field of heterogeneous multi-agent
reinforcement learning, cooperative behavior among different types of agents in
a group is pursued. Learning a joint-action set during centralized training is
an attractive way to obtain such cooperative behavior, however, this method
brings limited learning performance with heterogeneous agents. To improve the
learning performance of heterogeneous agents during centralized training,
two-stage heterogeneous centralized training which allows the training of
multiple roles of heterogeneous agents is proposed. During training, two
training processes are conducted in a series. One of the two stages is to
attempt training each agent according to its role, aiming at the maximization
of individual role rewards. The other is for training the agents as a whole to
make them learn cooperative behaviors while attempting to maximize shared
collective rewards, e.g., team rewards. Because these two training processes
are conducted in a series in every timestep, agents can learn how to maximize
role rewards and team rewards simultaneously. The proposed method is applied to
5 versus 5 AI robot soccer for validation. Simulation results show that the
proposed method can train the robots of the robot soccer team effectively,
achieving higher role rewards and higher team rewards as compared to other
approaches that can be used to solve problems of training cooperative
multi-agent.
| [
{
"version": "v1",
"created": "Tue, 13 Apr 2021 04:24:13 GMT"
}
] | 1,618,358,400,000 | [
[
"Kim",
"Taeyoung",
""
],
[
"Vecchietti",
"Luiz Felipe",
""
],
[
"Choi",
"Kyujin",
""
],
[
"Sariel",
"Sanem",
""
],
[
"Har",
"Dongsoo",
""
]
] |
2104.06054 | Viet-Man Le | Viet-Man Le | Group Recommendation Techniques for Feature Modeling and Configuration | to appear in the ICSE-DS'21 Proceedings | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In large-scale feature models, feature modeling and configuration processes
are highly expected to be done by a group of stakeholders. In this context,
recommendation techniques can increase the efficiency of feature-model design
and find optimal configurations for groups of stakeholders. Existing studies
show plenty of issues concerning feature model navigation support, group
members' satisfaction, and conflict resolution. This study proposes group
recommendation techniques for feature modeling and configuration on the basis
of addressing the mentioned issues.
| [
{
"version": "v1",
"created": "Tue, 13 Apr 2021 09:34:27 GMT"
}
] | 1,618,358,400,000 | [
[
"Le",
"Viet-Man",
""
]
] |
2104.06106 | Takumi Tanabe | Takumi Tanabe, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto | Level Generation for Angry Birds with Sequential VAE and Latent Variable
Evolution | The Genetic and Evolutionary Computation Conference 2021 (GECCO '21) | null | 10.1145/3449639.3459290 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Video game level generation based on machine learning (ML), in particular,
deep generative models, has attracted attention as a technique to automate
level generation. However, applications of existing ML-based level generations
are mostly limited to tile-based level representation. When ML techniques are
applied to game domains with non-tile-based level representation, such as Angry
Birds, where objects in a level are specified by real-valued parameters, ML
often fails to generate playable levels. In this study, we develop a
deep-generative-model-based level generation for the game domain of Angry
Birds. To overcome these drawbacks, we propose a sequential encoding of a level
and process it as text data, whereas existing approaches employ a tile-based
encoding and process it as an image. Experiments show that the proposed level
generator drastically improves the stability and diversity of generated levels
compared with existing approaches. We apply latent variable evolution with the
proposed generator to control the feature of a generated level computed through
an AI agent's play, while keeping the level stable and natural.
| [
{
"version": "v1",
"created": "Tue, 13 Apr 2021 11:23:39 GMT"
}
] | 1,618,358,400,000 | [
[
"Tanabe",
"Takumi",
""
],
[
"Fukuchi",
"Kazuto",
""
],
[
"Sakuma",
"Jun",
""
],
[
"Akimoto",
"Youhei",
""
]
] |
2104.06172 | Gilles Audemard | Gilles Audemard, Steve Bellart, Louenas Bounia, Fr\'ed\'eric Koriche,
Jean-Marie Lagniez, Pierre Marquis | On the Computational Intelligibility of Boolean Classifiers | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper, we investigate the computational intelligibility of Boolean
classifiers, characterized by their ability to answer XAI queries in polynomial
time. The classifiers under consideration are decision trees, DNF formulae,
decision lists, decision rules, tree ensembles, and Boolean neural nets. Using
9 XAI queries, including both explanation queries and verification queries, we
show the existence of large intelligibility gap between the families of
classifiers. On the one hand, all the 9 XAI queries are tractable for decision
trees. On the other hand, none of them is tractable for DNF formulae, decision
lists, random forests, boosted decision trees, Boolean multilayer perceptrons,
and binarized neural networks.
| [
{
"version": "v1",
"created": "Tue, 13 Apr 2021 13:24:39 GMT"
},
{
"version": "v2",
"created": "Tue, 7 Sep 2021 10:05:00 GMT"
}
] | 1,631,059,200,000 | [
[
"Audemard",
"Gilles",
""
],
[
"Bellart",
"Steve",
""
],
[
"Bounia",
"Louenas",
""
],
[
"Koriche",
"Frédéric",
""
],
[
"Lagniez",
"Jean-Marie",
""
],
[
"Marquis",
"Pierre",
""
]
] |
2104.06344 | Manling Li | Manling Li, Sha Li, Zhenhailong Wang, Lifu Huang, Kyunghyun Cho, Heng
Ji, Jiawei Han, Clare Voss | The Future is not One-dimensional: Complex Event Schema Induction by
Graph Modeling for Event Prediction | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Event schemas encode knowledge of stereotypical structures of events and
their connections. As events unfold, schemas are crucial to act as a
scaffolding. Previous work on event schema induction focuses either on atomic
events or linear temporal event sequences, ignoring the interplay between
events via arguments and argument relations. We introduce a new concept of
Temporal Complex Event Schema: a graph-based schema representation that
encompasses events, arguments, temporal connections and argument relations. In
addition, we propose a Temporal Event Graph Model that predicts event instances
following the temporal complex event schema. To build and evaluate such
schemas, we release a new schema learning corpus containing 6,399 documents
accompanied with event graphs, and we have manually constructed gold-standard
schemas. Intrinsic evaluations based on schema matching and instance graph
perplexity, prove the superior quality of our probabilistic graph schema
library compared to linear representations. Extrinsic evaluation on
schema-guided future event prediction further demonstrates the predictive power
of our event graph model, significantly outperforming human schemas and
baselines by more than 23.8% on HITS@1.
| [
{
"version": "v1",
"created": "Tue, 13 Apr 2021 16:41:05 GMT"
},
{
"version": "v2",
"created": "Thu, 15 Apr 2021 17:14:37 GMT"
},
{
"version": "v3",
"created": "Fri, 29 Apr 2022 04:44:59 GMT"
}
] | 1,651,449,600,000 | [
[
"Li",
"Manling",
""
],
[
"Li",
"Sha",
""
],
[
"Wang",
"Zhenhailong",
""
],
[
"Huang",
"Lifu",
""
],
[
"Cho",
"Kyunghyun",
""
],
[
"Ji",
"Heng",
""
],
[
"Han",
"Jiawei",
""
],
[
"Voss",
"Clare",
""
]
] |
2104.06681 | Konstantin Yakovlev S | Konstantin Yakovlev, Anton Andreychuk | Towards Time-Optimal Any-Angle Path Planning With Dynamic Obstacles | This is a pre-print of the paper accepted to ICAPS 2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Path finding is a well-studied problem in AI, which is often framed as graph
search. Any-angle path finding is a technique that augments the initial graph
with additional edges to build shorter paths to the goal. Indeed, optimal
algorithms for any-angle path finding in static environments exist. However,
when dynamic obstacles are present and time is the objective to be minimized,
these algorithms can no longer guarantee optimality. In this work, we elaborate
on why this is the case and what techniques can be used to solve the problem
optimally. We present two algorithms, grounded in the same idea, that can
obtain provably optimal solutions to the considered problem. One of them is a
naive algorithm and the other one is much more involved. We conduct a thorough
empirical evaluation showing that, in certain setups, the latter algorithm
might be as fast as the previously-known greedy non-optimal solver while
providing solutions of better quality. In some (rare) cases, the difference in
cost is up to 76%, while on average it is lower than one percent (the same cost
difference is typically observed between optimal and greedy any-angle solvers
in static environments).
| [
{
"version": "v1",
"created": "Wed, 14 Apr 2021 07:59:53 GMT"
}
] | 1,618,444,800,000 | [
[
"Yakovlev",
"Konstantin",
""
],
[
"Andreychuk",
"Anton",
""
]
] |
2104.06751 | Xin Lv | Xin Lv, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Yichi Zhang, Zelin
Dai | Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking
Reasoning Interpretability | null | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Multi-hop reasoning has been widely studied in recent years to obtain more
interpretable link prediction. However, we find in experiments that many paths
given by these models are actually unreasonable, while little works have been
done on interpretability evaluation for them. In this paper, we propose a
unified framework to quantitatively evaluate the interpretability of multi-hop
reasoning models so as to advance their development. In specific, we define
three metrics including path recall, local interpretability, and global
interpretability for evaluation, and design an approximate strategy to
calculate them using the interpretability scores of rules. Furthermore, we
manually annotate all possible rules and establish a Benchmark to detect the
Interpretability of Multi-hop Reasoning (BIMR). In experiments, we run nine
baselines on our benchmark. The experimental results show that the
interpretability of current multi-hop reasoning models is less satisfactory and
is still far from the upper bound given by our benchmark. Moreover, the
rule-based models outperform the multi-hop reasoning models in terms of
performance and interpretability, which points to a direction for future
research, i.e., we should investigate how to better incorporate rule
information into the multi-hop reasoning model. Our codes and datasets can be
obtained from https://github.com/THU-KEG/BIMR.
| [
{
"version": "v1",
"created": "Wed, 14 Apr 2021 10:12:05 GMT"
},
{
"version": "v2",
"created": "Thu, 9 Sep 2021 02:55:40 GMT"
}
] | 1,631,232,000,000 | [
[
"Lv",
"Xin",
""
],
[
"Cao",
"Yixin",
""
],
[
"Hou",
"Lei",
""
],
[
"Li",
"Juanzi",
""
],
[
"Liu",
"Zhiyuan",
""
],
[
"Zhang",
"Yichi",
""
],
[
"Dai",
"Zelin",
""
]
] |
2104.06890 | Ruo-Ze Liu | Ruo-Ze Liu, Wenhai Wang, Yanjie Shen, Zhiqi Li, Yang Yu, Tong Lu | An Introduction of mini-AlphaStar | 11 pages, 2 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | StarCraft II (SC2) is a real-time strategy game in which players produce and
control multiple units to fight against opponent's units. Due to its
difficulties, such as huge state space, various action space, a long time
horizon, and imperfect information, SC2 has been a research hotspot in
reinforcement learning. Recently, an agent called AlphaStar (AS) has been
proposed, which shows good performance, obtaining a high win rate of 99.8%
against human players. We implemented a mini-scaled version of it called
mini-AlphaStar (mAS) based on AS's paper and pseudocode. The difference between
AS and mAS is that we substituted the hyper-parameters of AS with smaller ones
for mini-scale training. Codes of mAS are all open-sourced
(https://github.com/liuruoze/mini-AlphaStar) for future research.
| [
{
"version": "v1",
"created": "Wed, 14 Apr 2021 14:31:51 GMT"
},
{
"version": "v2",
"created": "Wed, 17 Nov 2021 11:57:35 GMT"
}
] | 1,637,193,600,000 | [
[
"Liu",
"Ruo-Ze",
""
],
[
"Wang",
"Wenhai",
""
],
[
"Shen",
"Yanjie",
""
],
[
"Li",
"Zhiqi",
""
],
[
"Yu",
"Yang",
""
],
[
"Lu",
"Tong",
""
]
] |
2104.06910 | Jessica Morley | Jessica Morley, Caroline Morton, Kassandra Karpathakis, Mariarosaria
Taddeo, Luciano Floridi | Towards a framework for evaluating the safety, acceptability and
efficacy of AI systems for health: an initial synthesis | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The potential presented by Artificial Intelligence (AI) for healthcare has
long been recognised by the technical community. More recently, this potential
has been recognised by policymakers, resulting in considerable public and
private investment in the development of AI for healthcare across the globe.
Despite this, excepting limited success stories, real-world implementation of
AI systems into front-line healthcare has been limited. There are numerous
reasons for this, but a main contributory factor is the lack of internationally
accepted, or formalised, regulatory standards to assess AI safety and impact
and effectiveness. This is a well-recognised problem with numerous ongoing
research and policy projects to overcome it. Our intention here is to
contribute to this problem-solving effort by seeking to set out a minimally
viable framework for evaluating the safety, acceptability and efficacy of AI
systems for healthcare. We do this by conducting a systematic search across
Scopus, PubMed and Google Scholar to identify all the relevant literature
published between January 1970 and November 2020 related to the evaluation of:
output performance; efficacy; and real-world use of AI systems, and
synthesising the key themes according to the stages of evaluation: pre-clinical
(theoretical phase); exploratory phase; definitive phase; and post-market
surveillance phase (monitoring). The result is a framework to guide AI system
developers, policymakers, and regulators through a sufficient evaluation of an
AI system designed for use in healthcare.
| [
{
"version": "v1",
"created": "Wed, 14 Apr 2021 15:00:39 GMT"
}
] | 1,618,444,800,000 | [
[
"Morley",
"Jessica",
""
],
[
"Morton",
"Caroline",
""
],
[
"Karpathakis",
"Kassandra",
""
],
[
"Taddeo",
"Mariarosaria",
""
],
[
"Floridi",
"Luciano",
""
]
] |
2104.06982 | Kim de Bie | Kim de Bie, Ana Lucic, Hinda Haned | To Trust or Not to Trust a Regressor: Estimating and Explaining
Trustworthiness of Regression Predictions | Accepted to ICML 2021 Workshop on Human in the Loop Learning (HILL) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In hybrid human-AI systems, users need to decide whether or not to trust an
algorithmic prediction while the true error in the prediction is unknown. To
accommodate such settings, we introduce RETRO-VIZ, a method for (i) estimating
and (ii) explaining trustworthiness of regression predictions. It consists of
RETRO, a quantitative estimate of the trustworthiness of a prediction, and VIZ,
a visual explanation that helps users identify the reasons for the (lack of)
trustworthiness of a prediction. We find that RETRO-scores negatively correlate
with prediction error across 117 experimental settings, indicating that RETRO
provides a useful measure to distinguish trustworthy predictions from
untrustworthy ones. In a user study with 41 participants, we find that
VIZ-explanations help users identify whether a prediction is trustworthy or
not: on average, 95.1% of participants correctly select the more trustworthy
prediction, given a pair of predictions. In addition, an average of 75.6% of
participants can accurately describe why a prediction seems to be (not)
trustworthy. Finally, we find that the vast majority of users subjectively
experience RETRO-VIZ as a useful tool to assess the trustworthiness of
algorithmic predictions.
| [
{
"version": "v1",
"created": "Wed, 14 Apr 2021 17:04:20 GMT"
},
{
"version": "v2",
"created": "Wed, 28 Jul 2021 13:29:09 GMT"
}
] | 1,627,516,800,000 | [
[
"de Bie",
"Kim",
""
],
[
"Lucic",
"Ana",
""
],
[
"Haned",
"Hinda",
""
]
] |
2104.07225 | Krzysztof Fiok | Krzysztof Fiok (1), Waldemar Karwowski (1), Edgar Gutierrez (1)(2),
Mohammad Reza Davahli (1), Maciej Wilamowski (3), Tareq Ahram (1), Awad
Al-Juaid (4), and Jozef Zurada (5) ((1) Department of Industrial Engineering
and Management Systems, University of Central Florida, USA, (2) Center for
Latin-American Logistics Innovation, LOGyCA, Bogota, Colombia (3) Faculty of
Economic Sciences, University of Warsaw, Warsaw, Poland (4) Department of
Industrial Engineering, College of Engineering, Taif University, Saudi Arabia
(5) Business School, University of Louisville, USA) | Text Guide: Improving the quality of long text classification by a text
selection method based on feature importance | This is the reviewed and accepted for publication version of the
article by the IEEE Access Journal. One of the important modifications is
publication of the code along with the paper. The code can be used to apply
Text Guide to a data set of ones choice. The code is available at:
https://github.com/krzysztoffiok/TextGuide | null | 10.1109/ACCESS.2021.3099758 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The performance of text classification methods has improved greatly over the
last decade for text instances of less than 512 tokens. This limit has been
adopted by most state-of-the-research transformer models due to the high
computational cost of analyzing longer text instances. To mitigate this problem
and to improve classification for longer texts, researchers have sought to
resolve the underlying causes of the computational cost and have proposed
optimizations for the attention mechanism, which is the key element of every
transformer model. In our study, we are not pursuing the ultimate goal of long
text classification, i.e., the ability to analyze entire text instances at one
time while preserving high performance at a reasonable computational cost.
Instead, we propose a text truncation method called Text Guide, in which the
original text length is reduced to a predefined limit in a manner that improves
performance over naive and semi-naive approaches while preserving low
computational costs. Text Guide benefits from the concept of feature
importance, a notion from the explainable artificial intelligence domain. We
demonstrate that Text Guide can be used to improve the performance of recent
language models specifically designed for long text classification, such as
Longformer. Moreover, we discovered that parameter optimization is the key to
Text Guide performance and must be conducted before the method is deployed.
Future experiments may reveal additional benefits provided by this new method.
| [
{
"version": "v1",
"created": "Thu, 15 Apr 2021 04:10:08 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Oct 2021 08:24:33 GMT"
}
] | 1,635,206,400,000 | [
[
"Fiok",
"Krzysztof",
""
],
[
"Karwowski",
"Waldemar",
""
],
[
"Gutierrez",
"Edgar",
""
],
[
"Davahli",
"Mohammad Reza",
""
],
[
"Wilamowski",
"Maciej",
""
],
[
"Ahram",
"Tareq",
""
],
[
"Al-Juaid",
"Awad",
""
],
[
"Zurada",
"Jozef",
""
]
] |
2104.07276 | Divya Grover | Divya Grover, Christos Dimitrakakis | Adaptive Belief Discretization for POMDP Planning | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Partially Observable Markov Decision Processes (POMDP) is a widely used model
to represent the interaction of an environment and an agent, under state
uncertainty. Since the agent does not observe the environment state, its
uncertainty is typically represented through a probabilistic belief. While the
set of possible beliefs is infinite, making exact planning intractable, the
belief space's complexity (and hence planning complexity) is characterized by
its covering number. Many POMDP solvers uniformly discretize the belief space
and give the planning error in terms of the (typically unknown) covering
number. We instead propose an adaptive belief discretization scheme, and give
its associated planning error. We furthermore characterize the covering number
with respect to the POMDP parameters. This allows us to specify the exact
memory requirements on the planner, needed to bound the value function error.
We then propose a novel, computationally efficient solver using this scheme. We
demonstrate that our algorithm is highly competitive with the state of the art
in a variety of scenarios.
| [
{
"version": "v1",
"created": "Thu, 15 Apr 2021 07:04:32 GMT"
}
] | 1,618,531,200,000 | [
[
"Grover",
"Divya",
""
],
[
"Dimitrakakis",
"Christos",
""
]
] |
2104.07587 | Sola Shirai | Sola Shirai, Oshani Seneviratne, and Deborah L. McGuinness | Applying Personal Knowledge Graphs to Health | Extended abstract for the PHKG2020 workshop | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Knowledge graphs that encapsulate personal health information, or personal
health knowledge graphs (PHKG), can help enable personalized health care in
knowledge-driven systems. In this paper we provide a short survey of existing
work surrounding the emerging paradigm of PHKGs and highlight the major
challenges that remain. We find that while some preliminary exploration exists
on the topic of personal knowledge graphs, development of PHKGs remains
under-explored. A range of challenges surrounding the collection, linkage, and
maintenance of personal health knowledge remains to be addressed to fully
realize PHKGs.
| [
{
"version": "v1",
"created": "Thu, 15 Apr 2021 16:44:27 GMT"
}
] | 1,618,531,200,000 | [
[
"Shirai",
"Sola",
""
],
[
"Seneviratne",
"Oshani",
""
],
[
"McGuinness",
"Deborah L.",
""
]
] |
2104.07666 | Antoine Rolland | Antoine Rolland (ERIC), Jean-Baptiste Aubin (PSPM), Ir\`ene Gannaz
(PSPM), Samuela Leoni | A Note on Data Simulations for Voting by Evaluation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Voting rules based on evaluation inputs rather than preference orders have
been recently proposed, like majority judgement, range voting or approval
voting. Traditionally, probabilistic analysis of voting rules supposes the use
of simulation models to generate preferences data, like the Impartial Culture
(IC) or Impartial and Anonymous Culture (IAC) models. But these simulation
models are not suitable for the analysis of evaluation-based voting rules as
they generate preference orders instead of the needed evaluations. We propose
in this paper several simulation models for generating evaluation-based voting
inputs. These models, inspired by classical ones, are defined, tested and
compared for recommendation purpose.
| [
{
"version": "v1",
"created": "Thu, 15 Apr 2021 07:50:32 GMT"
}
] | 1,618,790,400,000 | [
[
"Rolland",
"Antoine",
"",
"ERIC"
],
[
"Aubin",
"Jean-Baptiste",
"",
"PSPM"
],
[
"Gannaz",
"Irène",
"",
"PSPM"
],
[
"Leoni",
"Samuela",
""
]
] |
2104.08419 | Jiapeng Wu | Jiapeng Wu, Yishi Xu, Yingxue Zhang, Chen Ma, Mark Coates and Jackie
Chi Kit Cheung | TIE: A Framework for Embedding-based Incremental Temporal Knowledge
Graph Completion | SIGIR 2021 long paper. 13 pages, 4 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Reasoning in a temporal knowledge graph (TKG) is a critical task for
information retrieval and semantic search. It is particularly challenging when
the TKG is updated frequently. The model has to adapt to changes in the TKG for
efficient training and inference while preserving its performance on historical
knowledge. Recent work approaches TKG completion (TKGC) by augmenting the
encoder-decoder framework with a time-aware encoding function. However, naively
fine-tuning the model at every time step using these methods does not address
the problems of 1) catastrophic forgetting, 2) the model's inability to
identify the change of facts (e.g., the change of the political affiliation and
end of a marriage), and 3) the lack of training efficiency. To address these
challenges, we present the Time-aware Incremental Embedding (TIE) framework,
which combines TKG representation learning, experience replay, and temporal
regularization. We introduce a set of metrics that characterizes the
intransigence of the model and propose a constraint that associates the deleted
facts with negative labels. Experimental results on Wikidata12k and YAGO11k
datasets demonstrate that the proposed TIE framework reduces training time by
about ten times and improves on the proposed metrics compared to vanilla
full-batch training. It comes without a significant loss in performance for any
traditional measures. Extensive ablation studies reveal performance trade-offs
among different evaluation metrics, which is essential for decision-making
around real-world TKG applications.
| [
{
"version": "v1",
"created": "Sat, 17 Apr 2021 01:40:46 GMT"
},
{
"version": "v2",
"created": "Mon, 3 May 2021 00:32:29 GMT"
},
{
"version": "v3",
"created": "Sun, 9 May 2021 03:00:52 GMT"
}
] | 1,620,691,200,000 | [
[
"Wu",
"Jiapeng",
""
],
[
"Xu",
"Yishi",
""
],
[
"Zhang",
"Yingxue",
""
],
[
"Ma",
"Chen",
""
],
[
"Coates",
"Mark",
""
],
[
"Cheung",
"Jackie Chi Kit",
""
]
] |
2104.08543 | Katya Kudashkina | Katya Kudashkina, Yi Wan, Abhishek Naik, Richard S. Sutton | Planning with Expectation Models for Control | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In model-based reinforcement learning (MBRL), Wan et al. (2019) showed
conditions under which the environment model could produce the expectation of
the next feature vector rather than the full distribution, or a sample thereof,
with no loss in planning performance. Such expectation models are of interest
when the environment is stochastic and non-stationary, and the model is
approximate, such as when it is learned using function approximation. In these
cases a full distribution model may be impractical and a sample model may be
either more expensive computationally or of high variance. Wan et al.
considered only planning for prediction to evaluate a fixed policy. In this
paper, we treat the control case - planning to improve and find a good
approximate policy. We prove that planning with an expectation model must
update a state-value function, not an action-value function as previously
suggested (e.g., Sorg & Singh, 2010). This opens the question of how planning
influences action selections. We consider three strategies for this and present
general MBRL algorithms for each. We identify the strengths and weaknesses of
these algorithms in computational experiments. Our algorithms and experiments
are the first to treat MBRL with expectation models in a general setting.
| [
{
"version": "v1",
"created": "Sat, 17 Apr 2021 13:37:14 GMT"
}
] | 1,618,876,800,000 | [
[
"Kudashkina",
"Katya",
""
],
[
"Wan",
"Yi",
""
],
[
"Naik",
"Abhishek",
""
],
[
"Sutton",
"Richard S.",
""
]
] |
2104.08555 | Qin Liang | Qin Liang, Minjie Zhang, Fenghui Ren, Takayuki Ito | A Robust Model for Trust Evaluation during Interactions between Agents
in a Sociable Environment | 13 pages, 5 figures | null | null | SSMCS2019-08 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Trust evaluation is an important topic in both research and applications in
sociable environments. This paper presents a model for trust evaluation between
agents by the combination of direct trust, indirect trust through neighbouring
links and the reputation of an agent in the environment (i.e. social network)
to provide the robust evaluation. Our approach is typology independent from
social network structures and in a decentralized manner without a central
controller, so it can be used in broad domains.
| [
{
"version": "v1",
"created": "Sat, 17 Apr 2021 14:38:02 GMT"
}
] | 1,618,876,800,000 | [
[
"Liang",
"Qin",
""
],
[
"Zhang",
"Minjie",
""
],
[
"Ren",
"Fenghui",
""
],
[
"Ito",
"Takayuki",
""
]
] |
2104.08641 | Diego Perez Liebana Dr. | Diego Perez-Liebana, Cristina Guerrero-Romero, Alexander Dockhorn,
Linjie Xu, Jorge Hurtado, Dominik Jeurissen | Generating Diverse and Competitive Play-Styles for Strategy Games | 8 pages, 2 figures, published in Proc. IEEE CoG 2021 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Designing agents that are able to achieve different play-styles while
maintaining a competitive level of play is a difficult task, especially for
games for which the research community has not found super-human performance
yet, like strategy games. These require the AI to deal with large action
spaces, long-term planning and partial observability, among other well-known
factors that make decision-making a hard problem. On top of this, achieving
distinct play-styles using a general algorithm without reducing playing
strength is not trivial. In this paper, we propose Portfolio Monte Carlo Tree
Search with Progressive Unpruning for playing a turn-based strategy game
(Tribes) and show how it can be parameterized so a quality-diversity algorithm
(MAP-Elites) is used to achieve different play-styles while keeping a
competitive level of play. Our results show that this algorithm is capable of
achieving these goals even for an extensive collection of game levels beyond
those used for training.
| [
{
"version": "v1",
"created": "Sat, 17 Apr 2021 20:33:24 GMT"
},
{
"version": "v2",
"created": "Mon, 28 Jun 2021 08:59:31 GMT"
}
] | 1,624,924,800,000 | [
[
"Perez-Liebana",
"Diego",
""
],
[
"Guerrero-Romero",
"Cristina",
""
],
[
"Dockhorn",
"Alexander",
""
],
[
"Xu",
"Linjie",
""
],
[
"Hurtado",
"Jorge",
""
],
[
"Jeurissen",
"Dominik",
""
]
] |
2104.08747 | Xue Yu | Yu Xue, Yihang Tang, Xin Xu, Jiayu Liang, Ferrante Neri | Multi-objective Feature Selection with Missing Data in Classification | 1 | null | 10.1109/TETCI.2021.3074147 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Feature selection (FS) is an important research topic in machine learning.
Usually, FS is modelled as a+ bi-objective optimization problem whose
objectives are: 1) classification accuracy; 2) number of features. One of the
main issues in real-world applications is missing data. Databases with missing
data are likely to be unreliable. Thus, FS performed on a data set missing some
data is also unreliable. In order to directly control this issue plaguing the
field, we propose in this study a novel modelling of FS: we include reliability
as the third objective of the problem. In order to address the modified
problem, we propose the application of the non-dominated sorting genetic
algorithm-III (NSGA-III). We selected six incomplete data sets from the
University of California Irvine (UCI) machine learning repository. We used the
mean imputation method to deal with the missing data. In the experiments,
k-nearest neighbors (K-NN) is used as the classifier to evaluate the feature
subsets. Experimental results show that the proposed three-objective model
coupled with NSGA-III efficiently addresses the FS problem for the six data
sets included in this study.
| [
{
"version": "v1",
"created": "Sun, 18 Apr 2021 07:12:39 GMT"
}
] | 1,618,963,200,000 | [
[
"Xue",
"Yu",
""
],
[
"Tang",
"Yihang",
""
],
[
"Xu",
"Xin",
""
],
[
"Liang",
"Jiayu",
""
],
[
"Neri",
"Ferrante",
""
]
] |
2104.08769 | Ziqian Zeng | Ziqian Zeng, Rashidul Islam, Kamrun Naher Keya, James Foulds, Yangqiu
Song, Shimei Pan | Fair Representation Learning for Heterogeneous Information Networks | Accepted at ICWSM 2021 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recently, much attention has been paid to the societal impact of AI,
especially concerns regarding its fairness. A growing body of research has
identified unfair AI systems and proposed methods to debias them, yet many
challenges remain. Representation learning for Heterogeneous Information
Networks (HINs), a fundamental building block used in complex network mining,
has socially consequential applications such as automated career counseling,
but there have been few attempts to ensure that it will not encode or amplify
harmful biases, e.g. sexism in the job market. To address this gap, in this
paper we propose a comprehensive set of de-biasing methods for fair HINs
representation learning, including sampling-based, projection-based, and graph
neural networks (GNNs)-based techniques. We systematically study the behavior
of these algorithms, especially their capability in balancing the trade-off
between fairness and prediction accuracy. We evaluate the performance of the
proposed methods in an automated career counseling application where we
mitigate gender bias in career recommendation. Based on the evaluation results
on two datasets, we identify the most effective fair HINs representation
learning techniques under different conditions.
| [
{
"version": "v1",
"created": "Sun, 18 Apr 2021 08:28:18 GMT"
}
] | 1,618,876,800,000 | [
[
"Zeng",
"Ziqian",
""
],
[
"Islam",
"Rashidul",
""
],
[
"Keya",
"Kamrun Naher",
""
],
[
"Foulds",
"James",
""
],
[
"Song",
"Yangqiu",
""
],
[
"Pan",
"Shimei",
""
]
] |
2104.08805 | Sergio Rozada | Sergio Rozada, Victor Tenorio, and Antonio G. Marques | Low-rank State-action Value-function Approximation | null | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Value functions are central to Dynamic Programming and Reinforcement Learning
but their exact estimation suffers from the curse of dimensionality,
challenging the development of practical value-function (VF) estimation
algorithms. Several approaches have been proposed to overcome this issue, from
non-parametric schemes that aggregate states or actions to parametric
approximations of state and action VFs via, e.g., linear estimators or deep
neural networks. Relevantly, several high-dimensional state problems can be
well-approximated by an intrinsic low-rank structure. Motivated by this and
leveraging results from low-rank optimization, this paper proposes different
stochastic algorithms to estimate a low-rank factorization of the $Q(s, a)$
matrix. This is a non-parametric alternative to VF approximation that
dramatically reduces the computational and sample complexities relative to
classical $Q$-learning methods that estimate $Q(s,a)$ separately for each
state-action pair.
| [
{
"version": "v1",
"created": "Sun, 18 Apr 2021 10:31:39 GMT"
}
] | 1,618,876,800,000 | [
[
"Rozada",
"Sergio",
""
],
[
"Tenorio",
"Victor",
""
],
[
"Marques",
"Antonio G.",
""
]
] |
2104.08819 | Manjushree Laddha | Manjushree D. Laddha, Varsha T. Lokare, Arvind W. Kiwelekar and Laxman
D. Netak | Classifications of the Summative Assessment for Revised Blooms Taxonomy
by using Deep Learning | 8 pages, 7 figures, 2 tables | International Journal of Engineering Trends and Technology
69.3(2021):211-218 | 10.14445/22315381/IJETT-V69I3P232 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Education is the basic step of understanding the truth and the preparation of
the intelligence to reflect. Focused on the rational capacity of the human
being the Cognitive process and knowledge dimensions of Revised Blooms Taxonomy
helps to differentiate the procedure of studying into six types of various
cognitive processes and four types of knowledge dimensions. These types are
synchronized in the increasing level of difficulty. In this paper Software
Engineering courses of B.Tech Computer Engineering and Information Technology
offered by various Universities and Educational Institutes have been
investigated for Revised Blooms Taxonomy RBT. Questions are a very useful
constituent. Knowledge intelligence and strength of the learners can be tested
by applying questions.The fundamental goal of this paper is to create a
relative study of the classification of the summative assessment based on
Revised Blooms Taxonomy using the Convolutional Neural Networks CNN Long
Short-Term Memory LSTM of Deep Learning techniques in an endeavor to attain
significant accomplishment and elevated precision levels.
| [
{
"version": "v1",
"created": "Sun, 18 Apr 2021 11:21:48 GMT"
}
] | 1,618,876,800,000 | [
[
"Laddha",
"Manjushree D.",
""
],
[
"Lokare",
"Varsha T.",
""
],
[
"Kiwelekar",
"Arvind W.",
""
],
[
"Netak",
"Laxman D.",
""
]
] |
2104.08890 | Adarsh Pyarelal | Adarsh Pyarelal, Aditya Banerjee, Kobus Barnard | Modular Procedural Generation for Voxel Maps | 8 pages, 7 figures, submitted to IEEE Conference on Games 2021 | null | 10.1007/978-3-031-21671-8_6 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Task environments developed in Minecraft are becoming increasingly popular
for artificial intelligence (AI) research. However, most of these are currently
constructed manually, thus failing to take advantage of procedural content
generation (PCG), a capability unique to virtual task environments. In this
paper, we present mcg, an open-source library to facilitate implementing PCG
algorithms for voxel-based environments such as Minecraft. The library is
designed with human-machine teaming research in mind, and thus takes a
'top-down' approach to generation, simultaneously generating low and high level
machine-readable representations that are suitable for empirical research.
These can be consumed by downstream AI applications that consider human spatial
cognition. The benefits of this approach include rapid, scalable, and efficient
development of virtual environments, the ability to control the statistics of
the environment at a semantic level, and the ability to generate novel
environments in response to player actions in real time.
| [
{
"version": "v1",
"created": "Sun, 18 Apr 2021 16:21:35 GMT"
}
] | 1,673,222,400,000 | [
[
"Pyarelal",
"Adarsh",
""
],
[
"Banerjee",
"Aditya",
""
],
[
"Barnard",
"Kobus",
""
]
] |
2104.08963 | Ly Trieu | Ly Ly Trieu, Tran Cao Son, Enrico Pontelli, and Marcello Balduccini | Generating explanations for answer set programming applications | Paper presented at SPIE 11746, Artificial Intelligence and Machine
Learning for Multi-Domain Operations Applications III, 117461L (12 April
2021), 14 pages. arXiv admin note: text overlap with arXiv:2008.01253 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We present an explanation system for applications that leverage Answer Set
Programming (ASP). Given a program P, an answer set A of P, and an atom a in
the program P, our system generates all explanation graphs of a which help
explain why a is true (or false) given the program P and the answer set A. We
illustrate the functionality of the system using some examples from the
literature.
| [
{
"version": "v1",
"created": "Sun, 18 Apr 2021 21:47:40 GMT"
}
] | 1,618,876,800,000 | [
[
"Trieu",
"Ly Ly",
""
],
[
"Son",
"Tran Cao",
""
],
[
"Pontelli",
"Enrico",
""
],
[
"Balduccini",
"Marcello",
""
]
] |
2104.09024 | Yao Wu | Yao Wu and Jian Cao and Guandong Xu and Yudong Tan | TFROM: A Two-sided Fairness-Aware Recommendation Model for Both
Customers and Providers | The 44th International ACM SIGIR Conference on Research and
Development in Information Retrieval | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | At present, most research on the fairness of recommender systems is conducted
either from the perspective of customers or from the perspective of product (or
service) providers. However, such a practice ignores the fact that when
fairness is guaranteed to one side, the fairness and rights of the other side
are likely to reduce. In this paper, we consider recommendation scenarios from
the perspective of two sides (customers and providers). From the perspective of
providers, we consider the fairness of the providers' exposure in recommender
system. For customers, we consider the fairness of the reduced quality of
recommendation results due to the introduction of fairness measures. We
theoretically analyzed the relationship between recommendation quality,
customers fairness, and provider fairness, and design a two-sided
fairness-aware recommendation model (TFROM) for both customers and providers.
Specifically, we design two versions of TFROM for offline and online
recommendation. The effectiveness of the model is verified on three real-world
data sets. The experimental results show that TFROM provides better two-sided
fairness while still maintaining a higher level of personalization than the
baseline algorithms.
| [
{
"version": "v1",
"created": "Mon, 19 Apr 2021 02:46:54 GMT"
}
] | 1,618,876,800,000 | [
[
"Wu",
"Yao",
""
],
[
"Cao",
"Jian",
""
],
[
"Xu",
"Guandong",
""
],
[
"Tan",
"Yudong",
""
]
] |
2104.09058 | Qinyuan Wu | Qinyuan Wu and Yong Deng | A Negation Quantum Decision Model to Predict the Interference Effect in
Categorization | 27 pages, 4 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Categorization is a significant task in decision-making, which is a key part
of human behavior. An interference effect is caused by categorization in some
cases, which breaks the total probability principle. A negation quantum model
(NQ model) is developed in this article to predict the interference. Taking the
advantage of negation to bring more information in the distribution from a
different perspective, the proposed model is a combination of the negation of a
probability distribution and the quantum decision model. Information of the
phase contained in quantum probability and the special calculation method to it
can easily represented the interference effect. The results of the proposed NQ
model is closely to the real experiment data and has less error than the
existed models.
| [
{
"version": "v1",
"created": "Mon, 19 Apr 2021 05:30:00 GMT"
}
] | 1,618,876,800,000 | [
[
"Wu",
"Qinyuan",
""
],
[
"Deng",
"Yong",
""
]
] |
2104.09203 | Mingfu Xue | Shichang Sun, Mingfu Xue, Jian Wang, Weiqiang Liu | Protecting the Intellectual Properties of Deep Neural Networks with an
Additional Class and Steganographic Images | null | Applied Intelligence, 24 March 2022 | 10.1007/s10489-022-03339-0 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, the research on protecting the intellectual properties (IP) of deep
neural networks (DNN) has attracted serious concerns. A number of DNN copyright
protection methods have been proposed. However, most of the existing
watermarking methods focus on verifying the copyright of the model, which do
not support the authentication and management of users' fingerprints, thus can
not satisfy the requirements of commercial copyright protection. In addition,
the query modification attack which was proposed recently can invalidate most
of the existing backdoor-based watermarking methods. To address these
challenges, in this paper, we propose a method to protect the intellectual
properties of DNN models by using an additional class and steganographic
images. Specifically, we use a set of watermark key samples to embed an
additional class into the DNN, so that the watermarked DNN will classify the
watermark key sample as the predefined additional class in the copyright
verification stage. We adopt the least significant bit (LSB) image
steganography to embed users' fingerprints into watermark key images. Each user
will be assigned with a unique fingerprint image so that the user's identity
can be authenticated later. Experimental results demonstrate that, the proposed
method can protect the copyright of DNN models effectively. On Fashion-MNIST
and CIFAR-10 datasets, the proposed method can obtain 100% watermark accuracy
and 100% fingerprint authentication success rate. In addition, the proposed
method is demonstrated to be robust to the model fine-tuning attack, model
pruning attack, and the query modification attack. Compared with three existing
watermarking methods (the logo-based, noise-based, and adversarial frontier
stitching watermarking methods), the proposed method has better performance on
watermark accuracy and robustness against the query modification attack.
| [
{
"version": "v1",
"created": "Mon, 19 Apr 2021 11:03:53 GMT"
}
] | 1,656,979,200,000 | [
[
"Sun",
"Shichang",
""
],
[
"Xue",
"Mingfu",
""
],
[
"Wang",
"Jian",
""
],
[
"Liu",
"Weiqiang",
""
]
] |
2104.09492 | Rodolfo Garc\'ia Berm\'udez | Camilo Vel\'azquez-Rodr\'iguez, Rodolfo Garc\'ia-Berm\'udez, Fernando
Rojas-Ruiz, Roberto Becerra-Garc\'ia, Luis Vel\'azquez | Automatic glissade determination through a mathematical model in
electrooculographic records | null | Bioinformatics and Biomedical Engineering. Springer International
Publishing; 2017. p. 546-56. (Lecture Notes in Computer Science) | 10.1007/978-3-319-56148-6_49 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The glissadic overshoot is characterized by an unwanted type of movement
known as glissades. The glissades are a short ocular movement that describe the
failure of the neural programming of saccades to move the eyes in order to
reach a specific target. In this paper we develop a procedure to determine if a
specific saccade have a glissade appended to the end of it. The use of the
third partial sum of the Gauss series as mathematical model, a comparison
between some specific parameters and the RMSE error are the steps made to reach
this goal. Finally a machine learning algorithm is trained, returning expected
responses of the presence or not of this kind of ocular movement.
| [
{
"version": "v1",
"created": "Mon, 19 Apr 2021 17:56:55 GMT"
}
] | 1,618,876,800,000 | [
[
"Velázquez-Rodríguez",
"Camilo",
""
],
[
"García-Bermúdez",
"Rodolfo",
""
],
[
"Rojas-Ruiz",
"Fernando",
""
],
[
"Becerra-García",
"Roberto",
""
],
[
"Velázquez",
"Luis",
""
]
] |
2104.09586 | Hamed Jelodar | Hamed Jelodar, Richard Frank | Semantic Knowledge Discovery and Discussion Mining of Incel Online
Community: Topic modeling | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Online forums provide a unique opportunity for online users to share comments
and exchange information on a particular topic. Understanding user behaviour is
valuable to organizations and has applications for social and security
strategies, for instance, identifying user opinions within a community or
predicting future behaviour. Discovering the semantic aspects in Incel forums
are the main goal of this research; we apply Natural language processing
techniques based on topic modeling to latent topic discovery and opinion mining
of users from a popular online Incel discussion forum. To prepare the input
data for our study, we extracted the comments from Incels.co. The research
experiments show that Artificial Intelligence (AI) based on NLP models can be
effective for semantic and emotion knowledge discovery and retrieval of useful
information from the Incel community. For example, we discovered
semantic-related words that describe issues within a large volume of Incel
comments, which is difficult with manual methods.
| [
{
"version": "v1",
"created": "Mon, 19 Apr 2021 19:39:07 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Apr 2021 16:57:14 GMT"
}
] | 1,619,049,600,000 | [
[
"Jelodar",
"Hamed",
""
],
[
"Frank",
"Richard",
""
]
] |
2104.09780 | Yu Liu | Yu Liu, Quanming Yao, Yong Li | Role-Aware Modeling for N-ary Relational Knowledge Bases | WWW2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | N-ary relational knowledge bases (KBs) represent knowledge with binary and
beyond-binary relational facts. Especially, in an n-ary relational fact, the
involved entities play different roles, e.g., the ternary relation
PlayCharacterIn consists of three roles, ACTOR, CHARACTER and MOVIE. However,
existing approaches are often directly extended from binary relational KBs,
i.e., knowledge graphs, while missing the important semantic property of role.
Therefore, we start from the role level, and propose a Role-Aware Modeling, RAM
for short, for facts in n-ary relational KBs. RAM explores a latent space that
contains basis vectors, and represents roles by linear combinations of these
vectors. This way encourages semantically related roles to have close
representations. RAM further introduces a pattern matrix that captures the
compatibility between the role and all involved entities. To this end, it
presents a multilinear scoring function to measure the plausibility of a fact
composed by certain roles and entities. We show that RAM achieves both
theoretical full expressiveness and computation efficiency, which also provides
an elegant generalization for approaches in binary relational KBs. Experiments
demonstrate that RAM outperforms representative baselines on both n-ary and
binary relational datasets.
| [
{
"version": "v1",
"created": "Tue, 20 Apr 2021 06:37:22 GMT"
}
] | 1,618,963,200,000 | [
[
"Liu",
"Yu",
""
],
[
"Yao",
"Quanming",
""
],
[
"Li",
"Yong",
""
]
] |
2104.09936 | Zhenning Li | Zhenning Li, Hao Yu, Guohui Zhang, Shangjia Dong, Cheng-Zhong Xu | Network-wide traffic signal control optimization using a multi-agent
deep reinforcement learning | null | Transportation Research Part C: Emerging Technologies Volume 125,
April 2021, 103059 | 10.1016/j.trc.2021.103059 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Inefficient traffic control may cause numerous problems such as traffic
congestion and energy waste. This paper proposes a novel multi-agent
reinforcement learning method, named KS-DDPG (Knowledge Sharing Deep
Deterministic Policy Gradient) to achieve optimal control by enhancing the
cooperation between traffic signals. By introducing the knowledge-sharing
enabled communication protocol, each agent can access to the collective
representation of the traffic environment collected by all agents. The proposed
method is evaluated through two experiments respectively using synthetic and
real-world datasets. The comparison with state-of-the-art reinforcement
learning-based and conventional transportation methods demonstrate the proposed
KS-DDPG has significant efficiency in controlling large-scale transportation
networks and coping with fluctuations in traffic flow. In addition, the
introduced communication mechanism has also been proven to speed up the
convergence of the model without significantly increasing the computational
burden.
| [
{
"version": "v1",
"created": "Tue, 20 Apr 2021 12:53:08 GMT"
}
] | 1,626,220,800,000 | [
[
"Li",
"Zhenning",
""
],
[
"Yu",
"Hao",
""
],
[
"Zhang",
"Guohui",
""
],
[
"Dong",
"Shangjia",
""
],
[
"Xu",
"Cheng-Zhong",
""
]
] |
2104.10353 | Zixuan Li | Zixuan Li, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, Huawei
Shen, Yuanzhuo Wang and Xueqi Cheng | Temporal Knowledge Graph Reasoning Based on Evolutional Representation
Learning | SIGIR 2021 Full Paper | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge Graph (KG) reasoning that predicts missing facts for incomplete KGs
has been widely explored. However, reasoning over Temporal KG (TKG) that
predicts facts in the future is still far from resolved. The key to predict
future facts is to thoroughly understand the historical facts. A TKG is
actually a sequence of KGs corresponding to different timestamps, where all
concurrent facts in each KG exhibit structural dependencies and temporally
adjacent facts carry informative sequential patterns. To capture these
properties effectively and efficiently, we propose a novel Recurrent Evolution
network based on Graph Convolution Network (GCN), called RE-GCN, which learns
the evolutional representations of entities and relations at each timestamp by
modeling the KG sequence recurrently. Specifically, for the evolution unit, a
relation-aware GCN is leveraged to capture the structural dependencies within
the KG at each timestamp. In order to capture the sequential patterns of all
facts in parallel, the historical KG sequence is modeled auto-regressively by
the gate recurrent components. Moreover, the static properties of entities such
as entity types, are also incorporated via a static graph constraint component
to obtain better entity representations. Fact prediction at future timestamps
can then be realized based on the evolutional entity and relation
representations. Extensive experiments demonstrate that the RE-GCN model
obtains substantial performance and efficiency improvement for the temporal
reasoning tasks on six benchmark datasets. Especially, it achieves up to
11.46\% improvement in MRR for entity prediction with up to 82 times speedup
comparing to the state-of-the-art baseline.
| [
{
"version": "v1",
"created": "Wed, 21 Apr 2021 05:12:21 GMT"
}
] | 1,619,049,600,000 | [
[
"Li",
"Zixuan",
""
],
[
"Jin",
"Xiaolong",
""
],
[
"Li",
"Wei",
""
],
[
"Guan",
"Saiping",
""
],
[
"Guo",
"Jiafeng",
""
],
[
"Shen",
"Huawei",
""
],
[
"Wang",
"Yuanzhuo",
""
],
[
"Cheng",
"Xueqi",
""
]
] |
2104.10429 | Alexander Dockhorn | Alexander Dockhorn, Jorge Hurtado-Grueso, Dominik Jeurissen, Linjie
Xu, Diego Perez-Liebana | Portfolio Search and Optimization for General Strategy Game-Playing | 8 pages, 5 figures, submitted to CEC 2021 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Portfolio methods represent a simple but efficient type of action abstraction
which has shown to improve the performance of search-based agents in a range of
strategy games. We first review existing portfolio techniques and propose a new
algorithm for optimization and action-selection based on the Rolling Horizon
Evolutionary Algorithm. Moreover, a series of variants are developed to solve
problems in different aspects. We further analyze the performance of discussed
agents in a general strategy game-playing task. For this purpose, we run
experiments on three different game-modes of the Stratega framework. For the
optimization of the agents' parameters and portfolio sets we study the use of
the N-tuple Bandit Evolutionary Algorithm. The resulting portfolio sets suggest
a high diversity in play-styles while being able to consistently beat the
sample agents. An analysis of the agents' performance shows that the proposed
algorithm generalizes well to all game-modes and is able to outperform other
portfolio methods.
| [
{
"version": "v1",
"created": "Wed, 21 Apr 2021 09:28:28 GMT"
}
] | 1,619,049,600,000 | [
[
"Dockhorn",
"Alexander",
""
],
[
"Hurtado-Grueso",
"Jorge",
""
],
[
"Jeurissen",
"Dominik",
""
],
[
"Xu",
"Linjie",
""
],
[
"Perez-Liebana",
"Diego",
""
]
] |
2104.10535 | Matias Greco | Pablo Araneda, Matias Greco, Jorge A. Baier | Exploiting Learned Policies in Focal Search | Accepted in SoCS 2021 | In Proceedings of the International Symposium on Combinatorial
Search (Vol. 12, No. 1, pp. 2-10) 2021 | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recent machine-learning approaches to deterministic search and
domain-independent planning employ policy learning to speed up search.
Unfortunately, when attempting to solve a search problem by successively
applying a policy, no guarantees can be given on solution quality. The problem
of how to effectively use a learned policy within a bounded-suboptimal search
algorithm remains largely as an open question. In this paper, we propose
various ways in which such policies can be integrated into Focal Search,
assuming that the policy is a neural network classifier. Furthermore, we
provide mathematical foundations for some of the resulting algorithms. To
evaluate the resulting algorithms over a number of policies with varying
accuracy, we use synthetic policies which can be generated for a target
accuracy for problems where the search space can be held in memory. We evaluate
our focal search variants over three benchmark domains using our synthetic
approach, and on the 15-puzzle using a neural network learned using 1.5 million
examples. We observe that Discrepancy Focal Search, which we show expands the
node which maximizes an approximation of the probability that its corresponding
path is a prefix of an optimal path, obtains, in general, the best results in
terms of runtime and solution quality.
| [
{
"version": "v1",
"created": "Wed, 21 Apr 2021 13:50:40 GMT"
},
{
"version": "v2",
"created": "Tue, 3 Aug 2021 21:30:03 GMT"
}
] | 1,628,121,600,000 | [
[
"Araneda",
"Pablo",
""
],
[
"Greco",
"Matias",
""
],
[
"Baier",
"Jorge A.",
""
]
] |
2104.10743 | Sarath Sreedharan | Sarath Sreedharan, Anagha Kulkarni, David E. Smith, Subbarao
Kambhampati | A Unifying Bayesian Formulation of Measures of Interpretability in
Human-AI | arXiv admin note: substantial text overlap with arXiv:2011.10920 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Existing approaches for generating human-aware agent behaviors have
considered different measures of interpretability in isolation. Further, these
measures have been studied under differing assumptions, thus precluding the
possibility of designing a single framework that captures these measures under
the same assumptions. In this paper, we present a unifying Bayesian framework
that models a human observer's evolving beliefs about an agent and thereby
define the problem of Generalized Human-Aware Planning. We will show that the
definitions of interpretability measures like explicability, legibility and
predictability from the prior literature fall out as special cases of our
general framework. Through this framework, we also bring a previously ignored
fact to light that the human-robot interactions are in effect open-world
problems, particularly as a result of modeling the human's beliefs over the
agent. Since the human may not only hold beliefs unknown to the agent but may
also form new hypotheses about the agent when presented with novel or
unexpected behaviors.
| [
{
"version": "v1",
"created": "Wed, 21 Apr 2021 20:06:33 GMT"
}
] | 1,619,136,000,000 | [
[
"Sreedharan",
"Sarath",
""
],
[
"Kulkarni",
"Anagha",
""
],
[
"Smith",
"David E.",
""
],
[
"Kambhampati",
"Subbarao",
""
]
] |
2104.10789 | Michael Cook | Michael Cook | The Road Less Travelled: Trying And Failing To Generate Walking
Simulators | Originally written for the Foundations of Digital Games 2021
Reflections track | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automated game design is a rapidly growing area of research, yet many aspects
of game design lie largely unexamined still, as most systems focus on
two-dimensional games with clear objectives and goal-oriented gameplay. This
paper describes several attempts to build an automated game designer for 3D
games more focused on space, atmosphere and experience. We describe our
attempts to build these systems, why they failed, and what steps and future
work we believe would be useful for future attempts by others.
| [
{
"version": "v1",
"created": "Wed, 21 Apr 2021 23:05:10 GMT"
},
{
"version": "v2",
"created": "Fri, 23 Apr 2021 16:29:16 GMT"
}
] | 1,619,395,200,000 | [
[
"Cook",
"Michael",
""
]
] |
2104.10796 | Zelong Li | Zelong Li, Jianchao Ji, Zuohui Fu, Yingqiang Ge, Shuyuan Xu, Chong
Chen, Yongfeng Zhang | Efficient Non-Sampling Knowledge Graph Embedding | 10 pages, 3 figures. The first two authors contributed equally to the
work. Accepted to WWW 2021 | null | 10.1145/3442381.3449859 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge Graph (KG) is a flexible structure that is able to describe the
complex relationship between data entities. Currently, most KG embedding models
are trained based on negative sampling, i.e., the model aims to maximize some
similarity of the connected entities in the KG, while minimizing the similarity
of the sampled disconnected entities. Negative sampling helps to reduce the
time complexity of model learning by only considering a subset of negative
instances, which may fail to deliver stable model performance due to the
uncertainty in the sampling procedure. To avoid such deficiency, we propose a
new framework for KG embedding -- Efficient Non-Sampling Knowledge Graph
Embedding (NS-KGE). The basic idea is to consider all of the negative instances
in the KG for model learning, and thus to avoid negative sampling. The
framework can be applied to square-loss based knowledge graph embedding models
or models whose loss can be converted to a square loss. A natural side-effect
of this non-sampling strategy is the increased computational complexity of
model learning. To solve the problem, we leverage mathematical derivations to
reduce the complexity of non-sampling loss function, which eventually provides
us both better efficiency and better accuracy in KG embedding compared with
existing models. Experiments on benchmark datasets show that our NS-KGE
framework can achieve a better performance on efficiency and accuracy over
traditional negative sampling based models, and that the framework is
applicable to a large class of knowledge graph embedding models.
| [
{
"version": "v1",
"created": "Wed, 21 Apr 2021 23:36:39 GMT"
},
{
"version": "v2",
"created": "Fri, 30 Apr 2021 19:47:54 GMT"
},
{
"version": "v3",
"created": "Wed, 16 Jun 2021 15:25:34 GMT"
}
] | 1,623,888,000,000 | [
[
"Li",
"Zelong",
""
],
[
"Ji",
"Jianchao",
""
],
[
"Fu",
"Zuohui",
""
],
[
"Ge",
"Yingqiang",
""
],
[
"Xu",
"Shuyuan",
""
],
[
"Chen",
"Chong",
""
],
[
"Zhang",
"Yongfeng",
""
]
] |
2104.10845 | Li Zhang | Yuxuan Chen, Li Zhang, Shijian Li, Gang Pan | Optimize Neural Fictitious Self-Play in Regret Minimization Thinking | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Optimization of deep learning algorithms to approach Nash Equilibrium remains
a significant problem in imperfect information games, e.g. StarCraft and poker.
Neural Fictitious Self-Play (NFSP) has provided an effective way to learn
approximate Nash Equilibrium without prior domain knowledge in imperfect
information games. However, optimality gap was left as an optimization problem
of NFSP and by solving the problem, the performance of NFSP could be improved.
In this study, focusing on the optimality gap of NFSP, we have proposed a new
method replacing NFSP's best response computation with regret matching method.
The new algorithm can make the optimality gap converge to zero as it iterates,
thus converge faster than original NFSP. We have conduct experiments on three
typical environments of perfect-information games and imperfect information
games in OpenSpiel and all showed that our new algorithm performances better
than original NFSP.
| [
{
"version": "v1",
"created": "Thu, 22 Apr 2021 03:24:23 GMT"
}
] | 1,619,136,000,000 | [
[
"Chen",
"Yuxuan",
""
],
[
"Zhang",
"Li",
""
],
[
"Li",
"Shijian",
""
],
[
"Pan",
"Gang",
""
]
] |
2104.10857 | Yun Li | Yun Li, Zhe Liu, Lina Yao, Xiaojun Chang | Attribute-Modulated Generative Meta Learning for Zero-Shot
Classification | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to
semantically related unseen classes, which are absent during training. The
promising strategies for ZSL are to synthesize visual features of unseen
classes conditioned on semantic side information and to incorporate
meta-learning to eliminate the model's inherent bias towards seen classes.
While existing meta generative approaches pursue a common model shared across
task distributions, we aim to construct a generative network adaptive to task
characteristics. To this end, we propose an Attribute-Modulated generAtive
meta-model for Zero-shot learning (AMAZ). Our model consists of an
attribute-aware modulation network, an attribute-augmented generative network,
and an attribute-weighted classifier. Given unseen classes, the modulation
network adaptively modulates the generator by applying task-specific
transformations so that the generative network can adapt to highly diverse
tasks. The weighted classifier utilizes the data quality to enhance the
training procedure, further improving the model performance. Our empirical
evaluations on four widely-used benchmarks show that AMAZ outperforms
state-of-the-art methods by 3.8% and 3.1% in ZSL and generalized ZSL settings,
respectively, demonstrating the superiority of our method. Our experiments on a
zero-shot image retrieval task show AMAZ's ability to synthesize instances that
portray real visual characteristics.
| [
{
"version": "v1",
"created": "Thu, 22 Apr 2021 04:16:43 GMT"
},
{
"version": "v2",
"created": "Sat, 24 Jul 2021 12:32:31 GMT"
},
{
"version": "v3",
"created": "Tue, 28 Dec 2021 03:25:55 GMT"
}
] | 1,640,822,400,000 | [
[
"Li",
"Yun",
""
],
[
"Liu",
"Zhe",
""
],
[
"Yao",
"Lina",
""
],
[
"Chang",
"Xiaojun",
""
]
] |
2104.11067 | Ulrich Furbach | Ulrike Barthelme{\ss}, Ulrich Furbach | K\"unstliche Intelligenz, quo vadis? | in German | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This paper outlines the state of the art in AI. It then describes basic
machine learning and knowledge processing techniques. Based on this, some
possibilities and limitations of future AI developments are discussed.
| [
{
"version": "v1",
"created": "Wed, 21 Apr 2021 09:30:16 GMT"
}
] | 1,619,136,000,000 | [
[
"Barthelmeß",
"Ulrike",
""
],
[
"Furbach",
"Ulrich",
""
]
] |
2104.11106 | Adrian Remonda | Adrian Remonda, Sarah Krebs, Eduardo Veas, Granit Luzhnica, Roman Kern | Formula RL: Deep Reinforcement Learning for Autonomous Racing using
Telemetry Data | null | IJCAI 2019 - Workshop on Scaling-Up Reinforcement Learning:SURL -
Macau, China | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper explores the use of reinforcement learning (RL) models for
autonomous racing. In contrast to passenger cars, where safety is the top
priority, a racing car aims to minimize the lap-time. We frame the problem as a
reinforcement learning task with a multidimensional input consisting of the
vehicle telemetry, and a continuous action space. To find out which RL methods
better solve the problem and whether the obtained models generalize to driving
on unknown tracks, we put 10 variants of deep deterministic policy gradient
(DDPG) to race in two experiments: i)~studying how RL methods learn to drive a
racing car and ii)~studying how the learning scenario influences the capability
of the models to generalize. Our studies show that models trained with RL are
not only able to drive faster than the baseline open source handcrafted bots
but also generalize to unknown tracks.
| [
{
"version": "v1",
"created": "Thu, 22 Apr 2021 14:40:12 GMT"
},
{
"version": "v2",
"created": "Mon, 13 Jun 2022 14:00:52 GMT"
}
] | 1,655,164,800,000 | [
[
"Remonda",
"Adrian",
""
],
[
"Krebs",
"Sarah",
""
],
[
"Veas",
"Eduardo",
""
],
[
"Luzhnica",
"Granit",
""
],
[
"Kern",
"Roman",
""
]
] |
2104.11360 | X. San Liang | X. San Liang | Normalized multivariate time series causality analysis and causal graph
reconstruction | 17 pages, 2 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Causality analysis is an important problem lying at the heart of science, and
is of particular importance in data science and machine learning. An endeavor
during the past 16 years viewing causality as real physical notion so as to
formulate it from first principles, however, seems to go unnoticed. This study
introduces to the community this line of work, with a long-due generalization
of the information flow-based bivariate time series causal inference to
multivariate series, based on the recent advance in theoretical development.
The resulting formula is transparent, and can be implemented as a
computationally very efficient algorithm for application. It can be normalized,
and tested for statistical significance. Different from the previous work along
this line where only information flows are estimated, here an algorithm is also
implemented to quantify the influence of a unit to itself. While this forms a
challenge in some causal inferences, here it comes naturally, and hence the
identification of self-loops in a causal graph is fulfilled automatically as
the causalities along edges are inferred.
To demonstrate the power of the approach, presented here are two applications
in extreme situations. The first is a network of multivariate processes buried
in heavy noises (with the noise-to-signal ratio exceeding 100), and the second
a network with nearly synchronized chaotic oscillators. In both graphs,
confounding processes exist. While it seems to be a huge challenge to
reconstruct from given series these causal graphs, an easy application of the
algorithm immediately reveals the desideratum. Particularly, the confounding
processes have been accurately differentiated. Considering the surge of
interest in the community, this study is very timely.
| [
{
"version": "v1",
"created": "Fri, 23 Apr 2021 00:46:35 GMT"
}
] | 1,619,395,200,000 | [
[
"Liang",
"X. San",
""
]
] |
2104.11454 | Cheng Luo | Cheng Luo, Dayiheng Liu, Chanjuan Li, Li Lu, Jiancheng Lv | Prediction, Selection, and Generation: Exploration of Knowledge-Driven
Conversation System | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In open-domain conversational systems, it is important but challenging to
leverage background knowledge. We can use the incorporation of knowledge to
make the generation of dialogue controllable, and can generate more diverse
sentences that contain real knowledge. In this paper, we combine the knowledge
bases and pre-training model to propose a knowledge-driven conversation system.
The system includes modules such as dialogue topic prediction, knowledge
matching and dialogue generation. Based on this system, we study the
performance factors that maybe affect the generation of knowledge-driven
dialogue: topic coarse recall algorithm, number of knowledge choices,
generation model choices, etc., and finally made the system reach
state-of-the-art. These experimental results will provide some guiding
significance for the future research of this task. As far as we know, this is
the first work to study and analyze the effects of the related factors.
| [
{
"version": "v1",
"created": "Fri, 23 Apr 2021 07:59:55 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Apr 2021 02:19:37 GMT"
},
{
"version": "v3",
"created": "Wed, 5 May 2021 06:58:12 GMT"
}
] | 1,620,259,200,000 | [
[
"Luo",
"Cheng",
""
],
[
"Liu",
"Dayiheng",
""
],
[
"Li",
"Chanjuan",
""
],
[
"Lu",
"Li",
""
],
[
"Lv",
"Jiancheng",
""
]
] |
2104.11597 | Zhiyuan Zhou | Zhiyuan Zhou, Kai Xuan, Zhifu Tao, Ligang Zhou | Generalized-TODIM Method for Multi-criteria Decision Making with Basic
Uncertain Information and its Application | 24 pages, 2 figure, 1 table | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Due to the fact that basic uncertain information provides a simple form for
decision information with certainty degree, it has been developed to reflect
the quality of observed or subjective assessments. In order to study the
algebra structure and preference relation of basic uncertain information, we
develop some algebra operations for basic uncertain information. The order
relation of such type of information has also been considered. Finally, to
apply the developed algebra operations and order relations, a generalized TODIM
method for multi-attribute decision making with basic uncertain information is
given. The numerical example shows that the developed decision procedure is
valid.
| [
{
"version": "v1",
"created": "Mon, 19 Apr 2021 04:18:53 GMT"
},
{
"version": "v2",
"created": "Tue, 27 Apr 2021 15:28:58 GMT"
}
] | 1,619,568,000,000 | [
[
"Zhou",
"Zhiyuan",
""
],
[
"Xuan",
"Kai",
""
],
[
"Tao",
"Zhifu",
""
],
[
"Zhou",
"Ligang",
""
]
] |
2104.11699 | Keping Yu | Keping Yu, Zhiwei Guo, Yu Shen, Wei Wang, Jerry Chun-Wei Lin, Takuro
Sato | Secure Artificial Intelligence of Things for Implicit Group
Recommendations | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The emergence of Artificial Intelligence of Things (AIoT) has provided novel
insights for many social computing applications such as group recommender
systems. As distance among people has been greatly shortened, it has been a
more general demand to provide personalized services to groups instead of
individuals. In order to capture group-level preference features from
individuals, existing methods were mostly established via aggregation and face
two aspects of challenges: secure data management workflow is absent, and
implicit preference feedbacks is ignored. To tackle current difficulties, this
paper proposes secure Artificial Intelligence of Things for implicit Group
Recommendations (SAIoT-GR). As for hardware module, a secure IoT structure is
developed as the bottom support platform. As for software module, collaborative
Bayesian network model and non-cooperative game are can be introduced as
algorithms. Such a secure AIoT architecture is able to maximize the advantages
of the two modules. In addition, a large number of experiments are carried out
to evaluate the performance of the SAIoT-GR in terms of efficiency and
robustness.
| [
{
"version": "v1",
"created": "Fri, 23 Apr 2021 16:38:26 GMT"
}
] | 1,619,395,200,000 | [
[
"Yu",
"Keping",
""
],
[
"Guo",
"Zhiwei",
""
],
[
"Shen",
"Yu",
""
],
[
"Wang",
"Wei",
""
],
[
"Lin",
"Jerry Chun-Wei",
""
],
[
"Sato",
"Takuro",
""
]
] |
2104.11951 | Xavier Gillard | Xavier Gillard, Vianney Copp\'e, Pierre Schaus, Andr\'e Augusto Cire | Improving the filtering of Branch-And-Bound MDD solver (extended) | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper presents and evaluates two pruning techniques to reinforce the
efficiency of constraint optimization solvers based on multi-valued
decision-diagrams (MDD). It adopts the branch-and-bound framework proposed by
Bergman et al. in 2016 to solve dynamic programs to optimality. In particular,
our paper presents and evaluates the effectiveness of the local-bound (LocB)
and rough upper-bound pruning (RUB). LocB is a new and effective rule that
leverages the approximate MDD structure to avoid the exploration of
non-interesting nodes. RUB is a rule to reduce the search space during the
development of bounded-width-MDDs. The experimental study we conducted on the
Maximum Independent Set Problem (MISP), Maximum Cut Problem (MCP), Maximum 2
Satisfiability (MAX2SAT) and the Traveling Salesman Problem with Time Windows
(TSPTW) shows evidence indicating that rough-upper-bound and local-bound
pruning have a high impact on optimization solvers based on branch-and-bound
with MDDs. In particular, it shows that RUB delivers excellent results but
requires some effort when defining the model. Also, it shows that LocB provides
a significant improvement automatically; without necessitating any
user-supplied information. Finally, it also shows that rough-upper-bound and
local-bound pruning are not mutually exclusive, and their combined benefit
supersedes the individual benefit of using each technique.
| [
{
"version": "v1",
"created": "Sat, 24 Apr 2021 13:42:42 GMT"
}
] | 1,619,481,600,000 | [
[
"Gillard",
"Xavier",
""
],
[
"Coppé",
"Vianney",
""
],
[
"Schaus",
"Pierre",
""
],
[
"Cire",
"André Augusto",
""
]
] |
2104.12278 | Lu Cheng | Lu Cheng, Ahmadreza Mosallanezhad, Paras Sheth, Huan Liu | Causal Learning for Socially Responsible AI | 8 pages, 3 figures, accepted at IJCAI21 survey track | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | There have been increasing concerns about Artificial Intelligence (AI) due to
its unfathomable potential power. To make AI address ethical challenges and
shun undesirable outcomes, researchers proposed to develop socially responsible
AI (SRAI). One of these approaches is causal learning (CL). We survey
state-of-the-art methods of CL for SRAI. We begin by examining the seven CL
tools to enhance the social responsibility of AI, then review how existing
works have succeeded using these tools to tackle issues in developing SRAI such
as fairness. The goal of this survey is to bring forefront the potentials and
promises of CL for SRAI.
| [
{
"version": "v1",
"created": "Sun, 25 Apr 2021 22:09:11 GMT"
},
{
"version": "v2",
"created": "Mon, 2 May 2022 18:37:08 GMT"
}
] | 1,651,622,400,000 | [
[
"Cheng",
"Lu",
""
],
[
"Mosallanezhad",
"Ahmadreza",
""
],
[
"Sheth",
"Paras",
""
],
[
"Liu",
"Huan",
""
]
] |
2104.12379 | Luca Erculiani Mr | Fausto Giunchiglia and Luca Erculiani and Andrea Passerini | Towards Visual Semantics | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Lexical Semantics is concerned with how words encode mental representations
of the world, i.e., concepts . We call this type of concepts, classification
concepts . In this paper, we focus on Visual Semantics , namely on how humans
build concepts representing what they perceive visually. We call this second
type of concepts, substance concepts . As shown in the paper, these two types
of concepts are different and, furthermore, the mapping between them is
many-to-many. In this paper we provide a theory and an algorithm for how to
build substance concepts which are in a one-to-one correspondence with
classifications concepts, thus paving the way to the seamless integration
between natural language descriptions and visual perception. This work builds
upon three main intuitions: (i) substance concepts are modeled as visual
objects , namely sequences of similar frames, as perceived in multiple
encounters ; (ii) substance concepts are organized into a visual subsumption
hierarchy based on the notions of Genus and Differentia ; (iii) the human
feedback is exploited not to name objects, but, rather, to align the hierarchy
of substance concepts with that of classification concepts. The learning
algorithm is implemented for the base case of a hierarchy of depth two. The
experiments, though preliminary, show that the algorithm manages to acquire the
notions of Genus and Differentia with reasonable accuracy, this despite seeing
a small number of examples and receiving supervision on a fraction of them.
| [
{
"version": "v1",
"created": "Mon, 26 Apr 2021 07:28:02 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Sep 2021 13:14:15 GMT"
}
] | 1,631,664,000,000 | [
[
"Giunchiglia",
"Fausto",
""
],
[
"Erculiani",
"Luca",
""
],
[
"Passerini",
"Andrea",
""
]
] |
2104.12871 | Melanie Mitchell | Melanie Mitchell | Why AI is Harder Than We Think | 12 pages; typos corrected in newest version | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Since its beginning in the 1950s, the field of artificial intelligence has
cycled several times between periods of optimistic predictions and massive
investment ("AI spring") and periods of disappointment, loss of confidence, and
reduced funding ("AI winter"). Even with today's seemingly fast pace of AI
breakthroughs, the development of long-promised technologies such as
self-driving cars, housekeeping robots, and conversational companions has
turned out to be much harder than many people expected. One reason for these
repeating cycles is our limited understanding of the nature and complexity of
intelligence itself. In this paper I describe four fallacies in common
assumptions made by AI researchers, which can lead to overconfident predictions
about the field. I conclude by discussing the open questions spurred by these
fallacies, including the age-old challenge of imbuing machines with humanlike
common sense.
| [
{
"version": "v1",
"created": "Mon, 26 Apr 2021 20:39:18 GMT"
},
{
"version": "v2",
"created": "Wed, 28 Apr 2021 15:51:25 GMT"
}
] | 1,619,654,400,000 | [
[
"Mitchell",
"Melanie",
""
]
] |
2104.13046 | Shuai Wang | Shuai Wang, Penghui Wei, Jiahao Zhao, Wenji Mao | A Knowledge Enhanced Learning and Semantic Composition Model for
Multi-Claim Fact Checking | 28 pages, 4 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To inhibit the spread of rumorous information and its severe consequences,
traditional fact checking aims at retrieving relevant evidence to verify the
veracity of a given claim. Fact checking methods typically use knowledge graphs
(KGs) as external repositories and develop reasoning mechanism to retrieve
evidence for verifying the triple claim. However, existing methods only focus
on verifying a single claim. As real-world rumorous information is more complex
and a textual statement is often composed of multiple clauses (i.e. represented
as multiple claims instead of a single one), multiclaim fact checking is not
only necessary but more important for practical applications. Although previous
methods for verifying a single triple can be applied repeatedly to verify
multiple triples one by one, they ignore the contextual information implied in
a multi-claim statement and could not learn the rich semantic information in
the statement as a whole. In this paper, we propose an end-to-end knowledge
enhanced learning and verification method for multi-claim fact checking. Our
method consists of two modules, KG-based learning enhancement and multi-claim
semantic composition. To fully utilize the contextual information, the KG-based
learning enhancement module learns the dynamic context-specific representations
via selectively aggregating relevant attributes of entities. To capture the
compositional semantics of multiple triples, the multi-claim semantic
composition module constructs the graph structure to model claim-level
interactions, and integrates global and salient local semantics with multi-head
attention. Experimental results on a real-world dataset and two benchmark
datasets show the effectiveness of our method for multi-claim fact checking
over KG.
| [
{
"version": "v1",
"created": "Tue, 27 Apr 2021 08:43:14 GMT"
}
] | 1,619,568,000,000 | [
[
"Wang",
"Shuai",
""
],
[
"Wei",
"Penghui",
""
],
[
"Zhao",
"Jiahao",
""
],
[
"Mao",
"Wenji",
""
]
] |
2104.13155 | Li Weigang | Li Weigang, Liriam Enamoto, Denise Leyi Li, Geraldo Pereira Rocha
Filho | Watershed of Artificial Intelligence: Human Intelligence, Machine
Intelligence, and Biological Intelligence | This article reviews the Once Learning mechanism and divides
Artificial Intelligence into three categories: Artificial Human Intelligence
(AHI), Artificial Machine Intelligence (AMI), and Artificial Biological
Intelligence (ABI). The paper is with 16 pages and 3 tables | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This article reviews the "Once learning" mechanism that was proposed 23 years
ago and the subsequent successes of "One-shot learning" in image classification
and "You Only Look Once - YOLO" in objective detection. Analyzing the current
development of Artificial Intelligence (AI), the proposal is that AI should be
clearly divided into the following categories: Artificial Human Intelligence
(AHI), Artificial Machine Intelligence (AMI), and Artificial Biological
Intelligence (ABI), which will also be the main directions of theory and
application development for AI. As a watershed for the branches of AI, some
classification standards and methods are discussed: 1) Human-oriented,
machine-oriented, and biological-oriented AI R&D; 2) Information input
processed by Dimensionality-up or Dimensionality-reduction; 3) The use of
one/few or large samples for knowledge learning.
| [
{
"version": "v1",
"created": "Tue, 27 Apr 2021 13:03:25 GMT"
},
{
"version": "v2",
"created": "Fri, 7 May 2021 18:34:10 GMT"
}
] | 1,620,691,200,000 | [
[
"Weigang",
"Li",
""
],
[
"Enamoto",
"Liriam",
""
],
[
"Li",
"Denise Leyi",
""
],
[
"Filho",
"Geraldo Pereira Rocha",
""
]
] |
2104.13791 | Giulio Mazzi | Giulio Mazzi, Alberto Castellini, Alessandro Farinelli | Rule-based Shielding for Partially Observable Monte-Carlo Planning | arXiv admin note: substantial text overlap with arXiv:2012.12732 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Partially Observable Monte-Carlo Planning (POMCP) is a powerful online
algorithm able to generate approximate policies for large Partially Observable
Markov Decision Processes. The online nature of this method supports
scalability by avoiding complete policy representation. The lack of an explicit
representation however hinders policy interpretability and makes policy
verification very complex. In this work, we propose two contributions. The
first is a method for identifying unexpected actions selected by POMCP with
respect to expert prior knowledge of the task. The second is a shielding
approach that prevents POMCP from selecting unexpected actions. The first
method is based on Satisfiability Modulo Theory (SMT). It inspects traces
(i.e., sequences of belief-action-observation triplets) generated by POMCP to
compute the parameters of logical formulas about policy properties defined by
the expert. The second contribution is a module that uses online the logical
formulas to identify anomalous actions selected by POMCP and substitutes those
actions with actions that satisfy the logical formulas fulfilling expert
knowledge. We evaluate our approach on Tiger, a standard benchmark for POMDPs,
and a real-world problem related to velocity regulation in mobile robot
navigation. Results show that the shielded POMCP outperforms the standard POMCP
in a case study in which a wrong parameter of POMCP makes it select wrong
actions from time to time. Moreover, we show that the approach keeps good
performance also if the parameters of the logical formula are optimized using
trajectories containing some wrong actions.
| [
{
"version": "v1",
"created": "Wed, 28 Apr 2021 14:23:38 GMT"
}
] | 1,619,654,400,000 | [
[
"Mazzi",
"Giulio",
""
],
[
"Castellini",
"Alberto",
""
],
[
"Farinelli",
"Alessandro",
""
]
] |
2104.14073 | Renjie Li | Renjie Li, Xinyi Wang, Katherine Lawler, Saurabh Garg, Quan Bai, Jane
Alty | Applications of Artificial Intelligence to aid detection of dementia: a
narrative review on current capabilities and future directions | 11 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | With populations ageing, the number of people with dementia worldwide is
expected to triple to 152 million by 2050. Seventy percent of cases are due to
Alzheimer's disease (AD) pathology and there is a 10-20 year 'pre-clinical'
period before significant cognitive decline occurs. We urgently need, cost
effective, objective methods to detect AD, and other dementias, at an early
stage. Risk factor modification could prevent 40% of cases and drug trials
would have greater chances of success if participants are recruited at an
earlier stage. Currently, detection of dementia is largely by pen and paper
cognitive tests but these are time consuming and insensitive to pre-clinical
phases. Specialist brain scans and body fluid biomarkers can detect the
earliest stages of dementia but are too invasive or expensive for widespread
use. With the advancement of technology, Artificial Intelligence (AI) shows
promising results in assisting with detection of early-stage dementia. Existing
AI-aided methods and potential future research directions are reviewed and
discussed.
| [
{
"version": "v1",
"created": "Thu, 29 Apr 2021 01:54:36 GMT"
}
] | 1,619,740,800,000 | [
[
"Li",
"Renjie",
""
],
[
"Wang",
"Xinyi",
""
],
[
"Lawler",
"Katherine",
""
],
[
"Garg",
"Saurabh",
""
],
[
"Bai",
"Quan",
""
],
[
"Alty",
"Jane",
""
]
] |
2104.14426 | Andrew Cropper | Andrew Cropper and Rolf Morel | Predicate Invention by Learning From Failures | Rejected manuscript for IJCAI21 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Discovering novel high-level concepts is one of the most important steps
needed for human-level AI. In inductive logic programming (ILP), discovering
novel high-level concepts is known as predicate invention (PI). Although seen
as crucial since the founding of ILP, PI is notoriously difficult and most ILP
systems do not support it. In this paper, we introduce POPPI, an ILP system
that formulates the PI problem as an answer set programming problem. Our
experiments show that (i) PI can drastically improve learning performance when
useful, (ii) PI is not too costly when unnecessary, and (iii) POPPI can
substantially outperform existing ILP systems.
| [
{
"version": "v1",
"created": "Thu, 29 Apr 2021 15:44:35 GMT"
}
] | 1,619,740,800,000 | [
[
"Cropper",
"Andrew",
""
],
[
"Morel",
"Rolf",
""
]
] |
2104.14461 | Mark Keane | Mark T Keane and Eoin M Kenny and Mohammed Temraz and Derek Greene and
Barry Smyth | Twin Systems for DeepCBR: A Menagerie of Deep Learning and Case-Based
Reasoning Pairings for Explanation and Data Augmentation | 7 pages,4 figures, 2 tables | IJCAI-21 Workshop on DL-CBR-AML, July 2021 | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recently, it has been proposed that fruitful synergies may exist between Deep
Learning (DL) and Case Based Reasoning (CBR); that there are insights to be
gained by applying CBR ideas to problems in DL (what could be called DeepCBR).
In this paper, we report on a program of research that applies CBR solutions to
the problem of Explainable AI (XAI) in the DL. We describe a series of
twin-systems pairings of opaque DL models with transparent CBR models that
allow the latter to explain the former using factual, counterfactual and
semi-factual explanation strategies. This twinning shows that functional
abstractions of DL (e.g., feature weights, feature importance and decision
boundaries) can be used to drive these explanatory solutions. We also raise the
prospect that this research also applies to the problem of Data Augmentation in
DL, underscoring the fecundity of these DeepCBR ideas.
| [
{
"version": "v1",
"created": "Thu, 29 Apr 2021 16:26:06 GMT"
},
{
"version": "v2",
"created": "Sun, 13 Jun 2021 16:00:01 GMT"
}
] | 1,623,715,200,000 | [
[
"Keane",
"Mark T",
""
],
[
"Kenny",
"Eoin M",
""
],
[
"Temraz",
"Mohammed",
""
],
[
"Greene",
"Derek",
""
],
[
"Smyth",
"Barry",
""
]
] |
2104.14602 | Anas Shrinah | Anas Shrinah, Derek Long and Kerstin Eder | D-VAL: An automatic functional equivalence validation tool for planning
domain models | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper introduces an approach to validate the functional equivalence of
planning domain models. Validating the functional equivalence of planning
domain models is the problem of formally confirming that two planning domain
models can be used to solve the same set of problems for any set of objects.
The need for techniques to validate the functional equivalence of planning
domain models has been highlighted in previous research and has applications in
model learning, development and extension. We prove the soundness and
completeness of our method. We also develop D-VAL, an automatic functional
equivalence validation tool for planning domain models. Empirical evaluation
shows that D-VAL validates the functional equivalence of all examined domains
in less than 43 seconds. Additionally, we provide a benchmark to evaluate the
feasibility and performance of this and future related work.
| [
{
"version": "v1",
"created": "Thu, 29 Apr 2021 18:40:23 GMT"
},
{
"version": "v2",
"created": "Sun, 26 Feb 2023 07:29:09 GMT"
}
] | 1,677,542,400,000 | [
[
"Shrinah",
"Anas",
""
],
[
"Long",
"Derek",
""
],
[
"Eder",
"Kerstin",
""
]
] |
2105.00060 | Cynthia Rudin | Michael Anis Mihdi Afnan, Cynthia Rudin, Vincent Conitzer, Julian
Savulescu, Abhishek Mishra, Yanhe Liu, Masoud Afnan | Ethical Implementation of Artificial Intelligence to Select Embryos in
In Vitro Fertilization | null | AIES 2021 | 10.1145/3461702.3462589 | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | AI has the potential to revolutionize many areas of healthcare. Radiology,
dermatology, and ophthalmology are some of the areas most likely to be impacted
in the near future, and they have received significant attention from the
broader research community. But AI techniques are now also starting to be used
in in vitro fertilization (IVF), in particular for selecting which embryos to
transfer to the woman. The contribution of AI to IVF is potentially
significant, but must be done carefully and transparently, as the ethical
issues are significant, in part because this field involves creating new
people. We first give a brief introduction to IVF and review the use of AI for
embryo selection. We discuss concerns with the interpretation of the reported
results from scientific and practical perspectives. We then consider the
broader ethical issues involved. We discuss in detail the problems that result
from the use of black-box methods in this context and advocate strongly for the
use of interpretable models. Importantly, there have been no published trials
of clinical effectiveness, a problem in both the AI and IVF communities, and we
therefore argue that clinical implementation at this point would be premature.
Finally, we discuss ways for the broader AI community to become involved to
ensure scientifically sound and ethically responsible development of AI in IVF.
| [
{
"version": "v1",
"created": "Fri, 30 Apr 2021 19:46:31 GMT"
}
] | 1,620,086,400,000 | [
[
"Afnan",
"Michael Anis Mihdi",
""
],
[
"Rudin",
"Cynthia",
""
],
[
"Conitzer",
"Vincent",
""
],
[
"Savulescu",
"Julian",
""
],
[
"Mishra",
"Abhishek",
""
],
[
"Liu",
"Yanhe",
""
],
[
"Afnan",
"Masoud",
""
]
] |
2105.00157 | Tanner Bohn | Charles X. Ling, Tanner Bohn | A Deep Learning Framework for Lifelong Machine Learning | 27 pages, 19 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans can learn a variety of concepts and skills incrementally over the
course of their lives while exhibiting many desirable properties, such as
continual learning without forgetting, forward transfer and backward transfer
of knowledge, and learning a new concept or task with only a few examples.
Several lines of machine learning research, such as lifelong machine learning,
few-shot learning, and transfer learning attempt to capture these properties.
However, most previous approaches can only demonstrate subsets of these
properties, often by different complex mechanisms. In this work, we propose a
simple yet powerful unified deep learning framework that supports almost all of
these properties and approaches through one central mechanism. Experiments on
toy examples support our claims. We also draw connections between many
peculiarities of human learning (such as memory loss and "rain man") and our
framework.
As academics, we often lack resources required to build and train, deep
neural networks with billions of parameters on hundreds of TPUs. Thus, while
our framework is still conceptual, and our experiment results are surely not
SOTA, we hope that this unified lifelong learning framework inspires new work
towards large-scale experiments and understanding human learning in general.
This paper is summarized in two short YouTube videos:
https://youtu.be/gCuUyGETbTU (part 1) and https://youtu.be/XsaGI01b-1o (part
2).
| [
{
"version": "v1",
"created": "Sat, 1 May 2021 03:43:25 GMT"
}
] | 1,620,086,400,000 | [
[
"Ling",
"Charles X.",
""
],
[
"Bohn",
"Tanner",
""
]
] |
2105.00375 | Harish Panneer Selvam | Harish Panneer Selvam, Yan Li, Pengyue Wang, William F. Northrop,
Shashi Shekhar | Vehicle Emissions Prediction with Physics-Aware AI Models: Preliminary
Results | Accepted by Association for Advancement of Artificial Intelligence
(AAAI) Fall Symposium Series 2020: Physics-Guided AI to Accelerate Scientific
Discovery (https://sites.google.com/vt.edu/pgai-aaai-20) | PGAI-AAAI-20(2020) | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Given an on-board diagnostics (OBD) dataset and a physics-based emissions
prediction model, this paper aims to develop an accurate and
computational-efficient AI (Artificial Intelligence) method that predicts
vehicle emissions. The problem is of societal importance because vehicular
emissions lead to climate change and impact human health. This problem is
challenging because the OBD data does not contain enough parameters needed by
high-order physics models. Conversely, related work has shown that low-order
physics models have poor predictive accuracy when using available OBD data.
This paper uses a divergent window co-occurrence pattern detection method to
develop a spatiotemporal variability-aware AI model for predicting emission
values from the OBD datasets. We conducted a case study using real-world OBD
data from a local public transportation agency. Results show that the proposed
AI method has approximately 65% improved predictive accuracy than a non-AI
low-order physics model and is approximately 35% more accurate than a baseline
model.
| [
{
"version": "v1",
"created": "Sun, 2 May 2021 01:52:59 GMT"
}
] | 1,620,259,200,000 | [
[
"Selvam",
"Harish Panneer",
""
],
[
"Li",
"Yan",
""
],
[
"Wang",
"Pengyue",
""
],
[
"Northrop",
"William F.",
""
],
[
"Shekhar",
"Shashi",
""
]
] |
2105.00388 | Wen Zhang | Wen Zhang, Chi-Man Wong, Ganqiang Ye, Bo Wen, Wei Zhang, Huajun Chen | Billion-scale Pre-trained E-commerce Product Knowledge Graph Model | Paper accepted by ICDE2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, knowledge graphs have been widely applied to organize data
in a uniform way and enhance many tasks that require knowledge, for example,
online shopping which has greatly facilitated people's life. As a backbone for
online shopping platforms, we built a billion-scale e-commerce product
knowledge graph for various item knowledge services such as item
recommendation. However, such knowledge services usually include tedious data
selection and model design for knowledge infusion, which might bring
inappropriate results. Thus, to avoid this problem, we propose a Pre-trained
Knowledge Graph Model (PKGM) for our billion-scale e-commerce product knowledge
graph, providing item knowledge services in a uniform way for embedding-based
models without accessing triple data in the knowledge graph. Notably, PKGM
could also complete knowledge graphs during servicing, thereby overcoming the
common incompleteness issue in knowledge graphs. We test PKGM in three
knowledge-related tasks including item classification, same item
identification, and recommendation. Experimental results show PKGM successfully
improves the performance of each task.
| [
{
"version": "v1",
"created": "Sun, 2 May 2021 04:28:22 GMT"
}
] | 1,620,086,400,000 | [
[
"Zhang",
"Wen",
""
],
[
"Wong",
"Chi-Man",
""
],
[
"Ye",
"Ganqiang",
""
],
[
"Wen",
"Bo",
""
],
[
"Zhang",
"Wei",
""
],
[
"Chen",
"Huajun",
""
]
] |
2105.00525 | Anagha Kulkarni | Anagha Kulkarni, Siddharth Srivastava and Subbarao Kambhampati | Planning for Proactive Assistance in Environments with Partial
Observability | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper addresses the problem of synthesizing the behavior of an AI agent
that provides proactive task assistance to a human in settings like factory
floors where they may coexist in a common environment. Unlike in the case of
requested assistance, the human may not be expecting proactive assistance and
hence it is crucial for the agent to ensure that the human is aware of how the
assistance affects her task. This becomes harder when there is a possibility
that the human may neither have full knowledge of the AI agent's capabilities
nor have full observability of its activities. Therefore, our \textit{proactive
assistant} is guided by the following three principles: \textbf{(1)} its
activity decreases the human's cost towards her goal; \textbf{(2)} the human is
able to recognize the potential reduction in her cost; \textbf{(3)} its
activity optimizes the human's overall cost (time/resources) of achieving her
goal. Through empirical evaluation and user studies, we demonstrate the
usefulness of our approach.
| [
{
"version": "v1",
"created": "Sun, 2 May 2021 18:12:06 GMT"
},
{
"version": "v2",
"created": "Sat, 4 Sep 2021 15:29:50 GMT"
}
] | 1,630,972,800,000 | [
[
"Kulkarni",
"Anagha",
""
],
[
"Srivastava",
"Siddharth",
""
],
[
"Kambhampati",
"Subbarao",
""
]
] |
2105.00648 | Yoo Yongmin | Yongmin Yoo, Tak-Sung Heo, Yeongjoon Park, Kyungsun Kim | A novel hybrid methodology of measuring sentence similarity | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem of measuring sentence similarity is an essential issue in the
natural language processing (NLP) area. It is necessary to measure the
similarity between sentences accurately. There are many approaches to measuring
sentence similarity. Deep learning methodology shows a state-of-the-art
performance in many natural language processing fields and is used a lot in
sentence similarity measurement methods. However, in the natural language
processing field, considering the structure of the sentence or the word
structure that makes up the sentence is also important. In this study, we
propose a methodology combined with both deep learning methodology and a method
considering lexical relationships. Our evaluation metric is the Pearson
correlation coefficient and Spearman correlation coefficient. As a result, the
proposed method outperforms the current approaches on a KorSTS standard
benchmark Korean dataset. Moreover, it performs a maximum of 65% increase than
only using deep learning methodology. Experiments show that our proposed method
generally results in better performance than those with only a deep learning
model.
| [
{
"version": "v1",
"created": "Mon, 3 May 2021 06:50:54 GMT"
},
{
"version": "v2",
"created": "Thu, 20 May 2021 06:31:04 GMT"
},
{
"version": "v3",
"created": "Mon, 14 Jun 2021 07:56:38 GMT"
},
{
"version": "v4",
"created": "Tue, 15 Jun 2021 23:25:44 GMT"
},
{
"version": "v5",
"created": "Mon, 21 Jun 2021 02:27:56 GMT"
}
] | 1,624,320,000,000 | [
[
"Yoo",
"Yongmin",
""
],
[
"Heo",
"Tak-Sung",
""
],
[
"Park",
"Yeongjoon",
""
],
[
"Kim",
"Kyungsun",
""
]
] |
2105.00762 | Kwanyoung Park | Kwanyoung Park, Hyunseok Oh, Youngki Lee | VECA : A Toolkit for Building Virtual Environments to Train and Test
Human-like Agents | 7 pages, 5 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Building human-like agent, which aims to learn and think like human
intelligence, has long been an important research topic in AI. To train and
test human-like agents, we need an environment that imposes the agent to rich
multimodal perception and allows comprehensive interactions for the agent,
while also easily extensible to develop custom tasks. However, existing
approaches do not support comprehensive interaction with the environment or
lack variety in modalities. Also, most of the approaches are difficult or even
impossible to implement custom tasks. In this paper, we propose a novel
VR-based toolkit, VECA, which enables building fruitful virtual environments to
train and test human-like agents. In particular, VECA provides a humanoid agent
and an environment manager, enabling the agent to receive rich human-like
perception and perform comprehensive interactions. To motivate VECA, we also
provide 24 interactive tasks, which represent (but are not limited to) four
essential aspects in early human development: joint-level locomotion and
control, understanding contexts of objects, multimodal learning, and
multi-agent learning. To show the usefulness of VECA on training and testing
human-like learning agents, we conduct experiments on VECA and show that users
can build challenging tasks for engaging human-like algorithms, and the
features supported by VECA are critical on training human-like agents.
| [
{
"version": "v1",
"created": "Mon, 3 May 2021 11:42:27 GMT"
}
] | 1,620,086,400,000 | [
[
"Park",
"Kwanyoung",
""
],
[
"Oh",
"Hyunseok",
""
],
[
"Lee",
"Youngki",
""
]
] |
2105.01036 | Anand Rao | Shaz Hoda, Amitoj Singh, Anand Rao, Remzi Ural, Nicholas Hodson | Consumer Demand Modeling During COVID-19 Pandemic | 8 pages, 7 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The current pandemic has introduced substantial uncertainty to traditional
methods for demand planning. These uncertainties stem from the disease
progression, government interventions, economy and consumer behavior. While
most of the emerging literature on the pandemic has focused on disease
progression, a few have focused on consequent regulations and their impact on
individual behavior. The contributions of this paper include a quantitative
behavior model of fear of COVID-19, impact of government interventions on
consumer behavior, and impact of consumer behavior on consumer choice and hence
demand for goods. It brings together multiple models for disease progression,
consumer behavior and demand estimation-thus bridging the gap between disease
progression and consumer demand. We use panel regression to understand the
drivers of demand during the pandemic and Bayesian inference to simplify the
regulation landscape that can help build scenarios for resilient demand
planning. We illustrate this resilient demand planning model using a specific
example of gas retailing. We find that demand is sensitive to fear of COVID-19:
as the number of COVID-19 cases increase over the previous week, the demand for
gas decreases -- though this dissipates over time. Further, government
regulations restrict access to different services, thereby reducing mobility,
which in itself reduces demand.
| [
{
"version": "v1",
"created": "Mon, 3 May 2021 17:36:06 GMT"
}
] | 1,620,086,400,000 | [
[
"Hoda",
"Shaz",
""
],
[
"Singh",
"Amitoj",
""
],
[
"Rao",
"Anand",
""
],
[
"Ural",
"Remzi",
""
],
[
"Hodson",
"Nicholas",
""
]
] |
2105.01115 | Jakub Kowalski | Rados{\l}aw Miernik, Jakub Kowalski | Evolving Evaluation Functions for Collectible Card Game AI | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we presented a study regarding two important aspects of
evolving feature-based game evaluation functions: the choice of genome
representation and the choice of opponent used to test the model. We compared
three representations. One simpler and more limited, based on a vector of
weights that are used in a linear combination of predefined game features. And
two more complex, based on binary and n-ary trees. On top of this test, we also
investigated the influence of fitness defined as a simulation-based function
that: plays against a fixed weak opponent, plays against a fixed strong
opponent, and plays against the best individual from the previous population.
For a testbed, we have chosen a recently popular domain of digital collectible
card games. We encoded our experiments in a programming game, Legends of Code
and Magic, used in Strategy Card Game AI Competition. However, as the problems
stated are of general nature we are convinced that our observations are
applicable in the other domains as well.
| [
{
"version": "v1",
"created": "Mon, 3 May 2021 18:39:06 GMT"
}
] | 1,620,172,800,000 | [
[
"Miernik",
"Radosław",
""
],
[
"Kowalski",
"Jakub",
""
]
] |
2105.01227 | Zi-Jian Ni | Zi-jian Ni, Wei Liu | Causal factors discovering from Chinese construction accident cases | 21 pages, 8 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In China, construction accidents have killed more people than any other
industry since 2012. The factors which led to the accident have complex
interaction. Real data about accidents is the key to reveal the mechanism among
these factors. But the data from the questionnaire and interview has inherent
defects. Many behaviors that impact safety are illegal. In China, most of the
cases are from accident investigation reports. Finding out the cause of the
accident and liability affirmation are the core of incident investigation
reports. So the truth of some answers from the respondents is doubtful. With a
series of NLP technologies, in this paper, causal factors of construction
accidents are extracted and organized from Chinese incident case texts.
Finally, three kinds of neglected causal factors are discovered after data
analysis.
| [
{
"version": "v1",
"created": "Tue, 4 May 2021 00:36:17 GMT"
}
] | 1,620,172,800,000 | [
[
"Ni",
"Zi-jian",
""
],
[
"Liu",
"Wei",
""
]
] |
2105.01269 | Ishita Padhiar | Ishita Padhiar, Oshani Seneviratne, Shruthi Chari, Daniel Gruen,
Deborah L. McGuinness | Semantic Modeling for Food Recommendation Explanations | 7 pages, 4 figures, 1 table, 3 listings | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | With the increased use of AI methods to provide recommendations in the
health, specifically in the food dietary recommendation space, there is also an
increased need for explainability of those recommendations. Such explanations
would benefit users of recommendation systems by empowering them with
justifications for following the system's suggestions. We present the Food
Explanation Ontology (FEO) that provides a formalism for modeling explanations
to users for food-related recommendations. FEO models food recommendations,
using concepts from the explanation domain to create responses to user
questions about food recommendations they receive from AI systems such as
personalized knowledge base question answering systems. FEO uses a modular,
extensible structure that lends itself to a variety of explanations while still
preserving important semantic details to accurately represent explanations of
food recommendations. In order to evaluate this system, we used a set of
competency questions derived from explanation types present in literature that
are relevant to food recommendations. Our motivation with the use of FEO is to
empower users to make decisions about their health, fully equipped with an
understanding of the AI recommender systems as they relate to user questions,
by providing reasoning behind their recommendations in the form of
explanations.
| [
{
"version": "v1",
"created": "Tue, 4 May 2021 03:25:36 GMT"
}
] | 1,620,172,800,000 | [
[
"Padhiar",
"Ishita",
""
],
[
"Seneviratne",
"Oshani",
""
],
[
"Chari",
"Shruthi",
""
],
[
"Gruen",
"Daniel",
""
],
[
"McGuinness",
"Deborah L.",
""
]
] |
2105.01419 | Tianyu Liu | Hang Yu, Tianyu Liu, Jie Lu and Guangquan Zhang | Automatic Learning to Detect Concept Drift | null | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Many methods have been proposed to detect concept drift, i.e., the change in
the distribution of streaming data, due to concept drift causes a decrease in
the prediction accuracy of algorithms. However, the most of current detection
methods are based on the assessment of the degree of change in the data
distribution, cannot identify the type of concept drift. In this paper, we
propose Active Drift Detection with Meta learning (Meta-ADD), a novel framework
that learns to classify concept drift by tracking the changed pattern of error
rates. Specifically, in the training phase, we extract meta-features based on
the error rates of various concept drift, after which a meta-detector is
developed via a prototypical neural network by representing various concept
drift classes as corresponding prototypes. In the detection phase, the learned
meta-detector is fine-tuned to adapt to the corresponding data stream via
stream-based active learning. Hence, Meta-ADD uses machine learning to learn to
detect concept drifts and identify their types automatically, which can
directly support drift understand. The experiment results verify the
effectiveness of Meta-ADD.
| [
{
"version": "v1",
"created": "Tue, 4 May 2021 11:10:39 GMT"
}
] | 1,620,172,800,000 | [
[
"Yu",
"Hang",
""
],
[
"Liu",
"Tianyu",
""
],
[
"Lu",
"Jie",
""
],
[
"Zhang",
"Guangquan",
""
]
] |
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