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2202.03759 | Dominique Mercier | Dominique Mercier, Jwalin Bhatt, Andreas Dengel, Sheraz Ahmed | Time to Focus: A Comprehensive Benchmark Using Time Series Attribution
Methods | 12 pages, 6 figures, 8 tables, Presented at ICAART 2022 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the last decade neural network have made huge impact both in industry and
research due to their ability to extract meaningful features from imprecise or
complex data, and by achieving super human performance in several domains.
However, due to the lack of transparency the use of these networks is hampered
in the areas with safety critical areas. In safety-critical areas, this is
necessary by law. Recently several methods have been proposed to uncover this
black box by providing interpreation of predictions made by these models. The
paper focuses on time series analysis and benchmark several state-of-the-art
attribution methods which compute explanations for convolutional classifiers.
The presented experiments involve gradient-based and perturbation-based
attribution methods. A detailed analysis shows that perturbation-based
approaches are superior concerning the Sensitivity and occlusion game. These
methods tend to produce explanations with higher continuity. Contrarily, the
gradient-based techniques are superb in runtime and Infidelity. In addition, a
validation the dependence of the methods on the trained model, feasible
application domains, and individual characteristics is attached. The findings
accentuate that choosing the best-suited attribution method is strongly
correlated with the desired use case. Neither category of attribution methods
nor a single approach has shown outstanding performance across all aspects.
| [
{
"version": "v1",
"created": "Tue, 8 Feb 2022 10:06:13 GMT"
}
] | 1,644,364,800,000 | [
[
"Mercier",
"Dominique",
""
],
[
"Bhatt",
"Jwalin",
""
],
[
"Dengel",
"Andreas",
""
],
[
"Ahmed",
"Sheraz",
""
]
] |
2202.03888 | Mohit Kumar | Mohit Kumar, Samuel Kolb, Stefano Teso, Luc De Raedt | Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Combinatorial optimisation problems are ubiquitous in artificial
intelligence. Designing the underlying models, however, requires substantial
expertise, which is a limiting factor in practice. The models typically consist
of hard and soft constraints, or combine hard constraints with an objective
function. We introduce a novel setting for learning combinatorial optimisation
problems from contextual examples. These positive and negative examples show -
in a particular context - whether the solutions are good enough or not. We
develop our framework using the MAX-SAT formalism as it is simple yet powerful
setting having these features. We study the learnability of MAX-SAT models. Our
theoretical results show that high-quality MAX-SAT models can be learned from
contextual examples in the realisable and agnostic settings, as long as the
data satisfies an intuitive "representativeness" condition. We also contribute
two implementations based on our theoretical results: one leverages ideas from
syntax-guided synthesis while the other makes use of stochastic local search
techniques. The two implementations are evaluated by recovering synthetic and
benchmark models from contextual examples. The experimental results support our
theoretical analysis, showing that MAX-SAT models can be learned from
contextual examples. Among the two implementations, the stochastic local search
learner scales much better than the syntax-guided implementation while
providing comparable or better models.
| [
{
"version": "v1",
"created": "Tue, 8 Feb 2022 14:22:38 GMT"
}
] | 1,644,364,800,000 | [
[
"Kumar",
"Mohit",
""
],
[
"Kolb",
"Samuel",
""
],
[
"Teso",
"Stefano",
""
],
[
"De Raedt",
"Luc",
""
]
] |
2202.03971 | Orfeas Menis Mastromichalakis | Edmund Dervakos, Orfeas Menis-Mastromichalakis, Alexandros Chortaras,
Giorgos Stamou | Computing Rule-Based Explanations of Machine Learning Classifiers using
Knowledge Graphs | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The use of symbolic knowledge representation and reasoning as a way to
resolve the lack of transparency of machine learning classifiers is a research
area that lately attracts many researchers. In this work, we use knowledge
graphs as the underlying framework providing the terminology for representing
explanations for the operation of a machine learning classifier. In particular,
given a description of the application domain of the classifier in the form of
a knowledge graph, we introduce a novel method for extracting and representing
black-box explanations of its operation, in the form of first-order logic rules
expressed in the terminology of the knowledge graph.
| [
{
"version": "v1",
"created": "Tue, 8 Feb 2022 16:21:49 GMT"
}
] | 1,644,364,800,000 | [
[
"Dervakos",
"Edmund",
""
],
[
"Menis-Mastromichalakis",
"Orfeas",
""
],
[
"Chortaras",
"Alexandros",
""
],
[
"Stamou",
"Giorgos",
""
]
] |
2202.04236 | Wenjun Tang | Kuan-Cheng Lee, Hong-Tzer Yang, and Wenjun Tang | Data-Driven Online Interactive Bidding Strategy for Demand Response | 31 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Demand response (DR), as one of the important energy resources in the
future's grid, provides the services of peak shaving, enhancing the efficiency
of renewable energy utilization with a short response period, and low cost.
Various categories of DR are established, e.g. automated DR, incentive DR,
emergency DR, and demand bidding. However, with the practical issue of the
unawareness of residential and commercial consumers' utility models, the
researches about demand bidding aggregator involved in the electricity market
are just at the beginning stage. For this issue, the bidding price and bidding
quantity are two required decision variables while considering the
uncertainties due to the market and participants. In this paper, we determine
the bidding and purchasing strategy simultaneously employing the smart meter
data and functions. A two-agent deep deterministic policy gradient method is
developed to optimize the decisions through learning historical bidding
experiences. The online learning further utilizes the daily newest bidding
experience attained to ensure trend tracing and self-adaptation. Two
environment simulators are adopted for testifying the robustness of the model.
The results prove that when facing diverse situations the proposed model can
earn the optimal profit via off/online learning the bidding rules and robustly
making the proper bid.
| [
{
"version": "v1",
"created": "Wed, 9 Feb 2022 02:44:20 GMT"
}
] | 1,644,451,200,000 | [
[
"Lee",
"Kuan-Cheng",
""
],
[
"Yang",
"Hong-Tzer",
""
],
[
"Tang",
"Wenjun",
""
]
] |
2202.04311 | Yuxi Mi | Yuxi Mi, Yiheng Sun, Jihong Guan, Shuigeng Zhou | Identifying Backdoor Attacks in Federated Learning via Anomaly Detection | APWeb-WAIM 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Federated learning has seen increased adoption in recent years in response to
the growing regulatory demand for data privacy. However, the opaque local
training process of federated learning also sparks rising concerns about model
faithfulness. For instance, studies have revealed that federated learning is
vulnerable to backdoor attacks, whereby a compromised participant can
stealthily modify the model's behavior in the presence of backdoor triggers.
This paper proposes an effective defense against the attack by examining shared
model updates. We begin with the observation that the embedding of backdoors
influences the participants' local model weights in terms of the magnitude and
orientation of their model gradients, which can manifest as distinguishable
disparities. We enable a robust identification of backdoors by studying the
statistical distribution of the models' subsets of gradients. Concretely, we
first segment the model gradients into fragment vectors that represent small
portions of model parameters. We then employ anomaly detection to locate the
distributionally skewed fragments and prune the participants with the most
outliers. We embody the findings in a novel defense method, ARIBA. We
demonstrate through extensive analyses that our proposed methods effectively
mitigate state-of-the-art backdoor attacks with minimal impact on task utility.
| [
{
"version": "v1",
"created": "Wed, 9 Feb 2022 07:07:42 GMT"
},
{
"version": "v2",
"created": "Wed, 23 Aug 2023 16:17:40 GMT"
}
] | 1,692,835,200,000 | [
[
"Mi",
"Yuxi",
""
],
[
"Sun",
"Yiheng",
""
],
[
"Guan",
"Jihong",
""
],
[
"Zhou",
"Shuigeng",
""
]
] |
2202.04376 | Xinyu Li | Xinyu Li, Yang Xu, Xiaohu Zhang, Wenzhong Shi, Yang Yue, Qingquan Li | Improving short-term bike sharing demand forecast through an irregular
convolutional neural network | 20 pages with 9 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | As an important task for the management of bike sharing systems, accurate
forecast of travel demand could facilitate dispatch and relocation of bicycles
to improve user satisfaction. In recent years, many deep learning algorithms
have been introduced to improve bicycle usage forecast. A typical practice is
to integrate convolutional (CNN) and recurrent neural network (RNN) to capture
spatial-temporal dependency in historical travel demand. For typical CNN, the
convolution operation is conducted through a kernel that moves across a
"matrix-format" city to extract features over spatially adjacent urban areas.
This practice assumes that areas close to each other could provide useful
information that improves prediction accuracy. However, bicycle usage in
neighboring areas might not always be similar, given spatial variations in
built environment characteristics and travel behavior that affect cycling
activities. Yet, areas that are far apart can be relatively more similar in
temporal usage patterns. To utilize the hidden linkage among these distant
urban areas, the study proposes an irregular convolutional Long-Short Term
Memory model (IrConv+LSTM) to improve short-term bike sharing demand forecast.
The model modifies traditional CNN with irregular convolutional architecture to
extract dependency among "semantic neighbors". The proposed model is evaluated
with a set of benchmark models in five study sites, which include one dockless
bike sharing system in Singapore, and four station-based systems in Chicago,
Washington, D.C., New York, and London. We find that IrConv+LSTM outperforms
other benchmark models in the five cities. The model also achieves superior
performance in areas with varying levels of bicycle usage and during peak
periods. The findings suggest that "thinking beyond spatial neighbors" can
further improve short-term travel demand prediction of urban bike sharing
systems.
| [
{
"version": "v1",
"created": "Wed, 9 Feb 2022 10:21:45 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Feb 2022 06:46:22 GMT"
}
] | 1,644,796,800,000 | [
[
"Li",
"Xinyu",
""
],
[
"Xu",
"Yang",
""
],
[
"Zhang",
"Xiaohu",
""
],
[
"Shi",
"Wenzhong",
""
],
[
"Yue",
"Yang",
""
],
[
"Li",
"Qingquan",
""
]
] |
2202.04411 | Kiran Madhusudhanan | Shayan Jawed, Mofassir ul Islam Arif, Ahmed Rashed, Kiran
Madhusudhanan, Shereen Elsayed, Mohsan Jameel, Alexei Volk, Andre Hintsches,
Marlies Kornfeld, Katrin Lange, Lars Schmidt-Thieme | A.I. and Data-Driven Mobility at Volkswagen Financial Services AG | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Machine learning is being widely adapted in industrial applications owing to
the capabilities of commercially available hardware and rapidly advancing
research. Volkswagen Financial Services (VWFS), as a market leader in vehicle
leasing services, aims to leverage existing proprietary data and the latest
research to enhance existing and derive new business processes. The
collaboration between Information Systems and Machine Learning Lab (ISMLL) and
VWFS serves to realize this goal. In this paper, we propose methods in the
fields of recommender systems, object detection, and forecasting that enable
data-driven decisions for the vehicle life-cycle at VWFS.
| [
{
"version": "v1",
"created": "Wed, 9 Feb 2022 11:45:38 GMT"
}
] | 1,644,451,200,000 | [
[
"Jawed",
"Shayan",
""
],
[
"Arif",
"Mofassir ul Islam",
""
],
[
"Rashed",
"Ahmed",
""
],
[
"Madhusudhanan",
"Kiran",
""
],
[
"Elsayed",
"Shereen",
""
],
[
"Jameel",
"Mohsan",
""
],
[
"Volk",
"Alexei",
""
],
[
"Hintsches",
"Andre",
""
],
[
"Kornfeld",
"Marlies",
""
],
[
"Lange",
"Katrin",
""
],
[
"Schmidt-Thieme",
"Lars",
""
]
] |
2202.04427 | Jian Zhao | Jian Zhao, Yue Zhang, Xunhan Hu, Weixun Wang, Wengang Zhou, Jianye
Hao, Jiangcheng Zhu, Houqiang Li | Revisiting QMIX: Discriminative Credit Assignment by Gradient Entropy
Regularization | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In cooperative multi-agent systems, agents jointly take actions and receive a
team reward instead of individual rewards. In the absence of individual reward
signals, credit assignment mechanisms are usually introduced to discriminate
the contributions of different agents so as to achieve effective cooperation.
Recently, the value decomposition paradigm has been widely adopted to realize
credit assignment, and QMIX has become the state-of-the-art solution. In this
paper, we revisit QMIX from two aspects. First, we propose a new perspective on
credit assignment measurement and empirically show that QMIX suffers limited
discriminability on the assignment of credits to agents. Second, we propose a
gradient entropy regularization with QMIX to realize a discriminative credit
assignment, thereby improving the overall performance. The experiments
demonstrate that our approach can comparatively improve learning efficiency and
achieve better performance.
| [
{
"version": "v1",
"created": "Wed, 9 Feb 2022 12:37:55 GMT"
},
{
"version": "v2",
"created": "Wed, 16 Feb 2022 06:24:29 GMT"
}
] | 1,645,056,000,000 | [
[
"Zhao",
"Jian",
""
],
[
"Zhang",
"Yue",
""
],
[
"Hu",
"Xunhan",
""
],
[
"Wang",
"Weixun",
""
],
[
"Zhou",
"Wengang",
""
],
[
"Hao",
"Jianye",
""
],
[
"Zhu",
"Jiangcheng",
""
],
[
"Li",
"Houqiang",
""
]
] |
2202.04611 | Weihang Yuan | Weihang Yuan, Hector Munoz-Avila, Venkatsampath Raja Gogineni, Sravya
Kondrakunta, Michael Cox, Lifang He | Task Modifiers for HTN Planning and Acting | Presented at The Ninth Advances in Cognitive Systems (ACS) Conference
2021 (arXiv:2201.06134) | null | null | ACS2021/18 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ability of an agent to change its objectives in response to unexpected
events is desirable in dynamic environments. In order to provide this
capability to hierarchical task network (HTN) planning, we propose an extension
of the paradigm called task modifiers, which are functions that receive a task
list and a state and produce a new task list. We focus on a particular type of
problems in which planning and execution are interleaved and the ability to
handle exogenous events is crucial. To determine the efficacy of this approach,
we evaluate the performance of our task modifier implementation in two
environments, one of which is a simulation that differs substantially from
traditional HTN domains.
| [
{
"version": "v1",
"created": "Wed, 9 Feb 2022 18:10:20 GMT"
}
] | 1,644,451,200,000 | [
[
"Yuan",
"Weihang",
""
],
[
"Munoz-Avila",
"Hector",
""
],
[
"Gogineni",
"Venkatsampath Raja",
""
],
[
"Kondrakunta",
"Sravya",
""
],
[
"Cox",
"Michael",
""
],
[
"He",
"Lifang",
""
]
] |
2202.04787 | Brad Dillman | Olivia Brown, Brad Dillman | Proceedings of the Robust Artificial Intelligence System Assurance
(RAISA) Workshop 2022 | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Robust Artificial Intelligence System Assurance (RAISA) workshop will
focus on research, development and application of robust artificial
intelligence (AI) and machine learning (ML) systems. Rather than studying
robustness with respect to particular ML algorithms, our approach will be to
explore robustness assurance at the system architecture level, during both
development and deployment, and within the human-machine teaming context. While
the research community is converging on robust solutions for individual AI
models in specific scenarios, the problem of evaluating and assuring the
robustness of an AI system across its entire life cycle is much more complex.
Moreover, the operational context in which AI systems are deployed necessitates
consideration of robustness and its relation to principles of fairness,
privacy, and explainability.
| [
{
"version": "v1",
"created": "Thu, 10 Feb 2022 01:15:50 GMT"
}
] | 1,644,537,600,000 | [
[
"Brown",
"Olivia",
""
],
[
"Dillman",
"Brad",
""
]
] |
2202.04954 | Moshe Shienman | Moshe Shienman and Vadim Indelman | D2A-BSP: Distilled Data Association Belief Space Planning with
Performance Guarantees Under Budget Constraints | 8 pages, 2 figures, Accepted to IEEE International Conference on
Robotics and Automation (ICRA) 2022, *Outstanding Paper Award Finalist* | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Unresolved data association in ambiguous and perceptually aliased
environments leads to multi-modal hypotheses on both the robot's and the
environment state. To avoid catastrophic results, when operating in such
ambiguous environments, it is crucial to reason about data association within
Belief Space Planning (BSP). However, explicitly considering all possible data
associations, the number of hypotheses grows exponentially with the planning
horizon and determining the optimal action sequence quickly becomes
intractable. Moreover, with hard budget constraints where some non-negligible
hypotheses must be pruned, achieving performance guarantees is crucial. In this
work we present a computationally efficient novel approach that utilizes only a
distilled subset of hypotheses to solve BSP problems while reasoning about data
association. Furthermore, to provide performance guarantees, we derive error
bounds with respect to the optimal solution. We then demonstrate our approach
in an extremely aliased environment, where we manage to significantly reduce
computation time without compromising on the quality of the solution.
| [
{
"version": "v1",
"created": "Thu, 10 Feb 2022 11:13:24 GMT"
},
{
"version": "v2",
"created": "Sun, 17 Jul 2022 07:18:55 GMT"
}
] | 1,658,188,800,000 | [
[
"Shienman",
"Moshe",
""
],
[
"Indelman",
"Vadim",
""
]
] |
2202.04977 | Ryan Watkins PhD | Ryan Watkins and Soheil Human | Needs-aware Artificial Intelligence: AI that 'serves [human] needs' | 3-10-2022 Reference #6 updates with arXiv link, 5-15-22 final version
for publication in AI & Ethics | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | By defining the current limits (and thereby the frontiers), many boundaries
are shaping, and will continue to shape, the future of Artificial Intelligence
(AI). We push on these boundaries in order to make further progress into what
were yesterday's frontiers. They are both pliable and resilient - always
creating new boundaries of what AI can (or should) achieve. Among these are
technical boundaries (such as processing capacity), psychological boundaries
(such as human trust in AI systems), ethical boundaries (such as with AI
weapons), and conceptual boundaries (such as the AI people can imagine). It is
within this final category while it can play a fundamental role in all other
boundaries} that we find the construct of needs and the limitations that our
current concept of need places on the future AI.
| [
{
"version": "v1",
"created": "Thu, 10 Feb 2022 12:19:48 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Mar 2022 20:30:25 GMT"
},
{
"version": "v3",
"created": "Thu, 26 May 2022 17:55:41 GMT"
}
] | 1,653,609,600,000 | [
[
"Watkins",
"Ryan",
""
],
[
"Human",
"Soheil",
""
]
] |
2202.05511 | Jonas Philipp Haldimann | Jonas Haldimann, Christoph Beierle | Inference with System W Satisfies Syntax Splitting | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we investigate inductive inference with system W from
conditional belief bases with respect to syntax splitting. The concept of
syntax splitting for inductive inference states that inferences about
independent parts of the signature should not affect each other. This was
captured in work by Kern-Isberner, Beierle, and Brewka in the form of
postulates for inductive inference operators expressing syntax splitting as a
combination of relevance and independence; it was also shown that c-inference
fulfils syntax splitting, while system P inference and system Z both fail to
satisfy it. System W is a recently introduced inference system for nonmonotonic
reasoning that captures and properly extends system Z as well as c-inference.
We show that system W fulfils the syntax splitting postulates for inductive
inference operators by showing that it satisfies the required properties of
relevance and independence. This makes system W another inference operator
besides c-inference that fully complies with syntax splitting, while in
contrast to c-inference, also extending rational closure.
| [
{
"version": "v1",
"created": "Fri, 11 Feb 2022 08:59:41 GMT"
}
] | 1,644,796,800,000 | [
[
"Haldimann",
"Jonas",
""
],
[
"Beierle",
"Christoph",
""
]
] |
2202.05793 | Tran Cao Son | Tran Cao Son and Enrico Pontelli and Marcello Balduccini and Torsten
Schaub | Answer Set Planning: A Survey | 68 pages, 6 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Answer Set Planning refers to the use of Answer Set Programming (ASP) to
compute plans, i.e., solutions to planning problems, that transform a given
state of the world to another state. The development of efficient and scalable
answer set solvers has provided a significant boost to the development of
ASP-based planning systems. This paper surveys the progress made during the
last two and a half decades in the area of answer set planning, from its
foundations to its use in challenging planning domains. The survey explores the
advantages and disadvantages of answer set planning. It also discusses typical
applications of answer set planning and presents a set of challenges for future
research.
| [
{
"version": "v1",
"created": "Fri, 11 Feb 2022 17:42:47 GMT"
}
] | 1,644,796,800,000 | [
[
"Son",
"Tran Cao",
""
],
[
"Pontelli",
"Enrico",
""
],
[
"Balduccini",
"Marcello",
""
],
[
"Schaub",
"Torsten",
""
]
] |
2202.05938 | Cl\'ement Quinton | Pierre Bourhis (1), Laurence Duchien (1), J\'er\'emie Dusart (1),
Emmanuel Lonca (2), Pierre Marquis (2 and 3), Cl\'ement Quinton (1) ((1)
University of Lille, CNRS, Inria, Centrale Lille, UMR 9189 CRIStAL, (2) Univ.
Artois, CNRS, UMR 8188 CRIL, (3) Institut Universitaire de France) | Pseudo Polynomial-Time Top-k Algorithms for d-DNNF Circuits | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We are interested in computing $k$ most preferred models of a given d-DNNF
circuit $C$, where the preference relation is based on an algebraic structure
called a monotone, totally ordered, semigroup $(K, \otimes, <)$. In our
setting, every literal in $C$ has a value in $K$ and the value of an assignment
is an element of $K$ obtained by aggregating using $\otimes$ the values of the
corresponding literals. We present an algorithm that computes $k$ models of $C$
among those having the largest values w.r.t. $<$, and show that this algorithm
runs in time polynomial in $k$ and in the size of $C$. We also present a pseudo
polynomial-time algorithm for deriving the top-$k$ values that can be reached,
provided that an additional (but not very demanding) requirement on the
semigroup is satisfied. Under the same assumption, we present a pseudo
polynomial-time algorithm that transforms $C$ into a d-DNNF circuit $C'$
satisfied exactly by the models of $C$ having a value among the top-$k$ ones.
Finally, focusing on the semigroup $(\mathbb{N}, +, <)$, we compare on a large
number of instances the performances of our compilation-based algorithm for
computing $k$ top solutions with those of an algorithm tackling the same
problem, but based on a partial weighted MaxSAT solver.
| [
{
"version": "v1",
"created": "Fri, 11 Feb 2022 23:53:43 GMT"
},
{
"version": "v2",
"created": "Thu, 5 May 2022 21:51:53 GMT"
}
] | 1,652,054,400,000 | [
[
"Bourhis",
"Pierre",
"",
"2 and 3"
],
[
"Duchien",
"Laurence",
"",
"2 and 3"
],
[
"Dusart",
"Jérémie",
"",
"2 and 3"
],
[
"Lonca",
"Emmanuel",
"",
"2 and 3"
],
[
"Marquis",
"Pierre",
"",
"2 and 3"
],
[
"Quinton",
"Clément",
""
]
] |
2202.05957 | James Davis | Jim Davis | Confident AI | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper, we propose "Confident AI" as a means to designing Artificial
Intelligence (AI) and Machine Learning (ML) systems with both algorithm and
user confidence in model predictions and reported results. The 4 basic tenets
of Confident AI are Repeatability, Believability, Sufficiency, and
Adaptability. Each of the tenets is used to explore fundamental issues in
current AI/ML systems and together provide an overall approach to Confident AI.
| [
{
"version": "v1",
"created": "Sat, 12 Feb 2022 02:26:46 GMT"
}
] | 1,644,883,200,000 | [
[
"Davis",
"Jim",
""
]
] |
2202.06015 | Mieczys{\l}aw K{\l}opotek | Mieczyslaw A. Klopotek and Robert A. Klopotek | Towards Continuous Consistency Axiom | 42 pages, 6 tables, 9 figures | Applied Intelligence 2022 | 10.1007/s10489-022-03710-1 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Development of new algorithms in the area of machine learning, especially
clustering, comparative studies of such algorithms as well as testing according
to software engineering principles requires availability of labeled data sets.
While standard benchmarks are made available, a broader range of such data sets
is necessary in order to avoid the problem of overfitting. In this context,
theoretical works on axiomatization of clustering algorithms, especially axioms
on clustering preserving transformations are quite a cheap way to produce
labeled data sets from existing ones. However, the frequently cited axiomatic
system of Kleinberg:2002, as we show in this paper, is not applicable for
finite dimensional Euclidean spaces, in which many algorithms like $k$-means,
operate. In particular, the so-called outer-consistency axiom fails upon making
small changes in datapoint positions and inner-consistency axiom is valid only
for identity transformation in general settings.
Hence we propose an alternative axiomatic system, in which Kleinberg's inner
consistency axiom is replaced by a centric consistency axiom and outer
consistency axiom is replaced by motion consistency axiom. We demonstrate that
the new system is satisfiable for a hierarchical version of $k$-means with
auto-adjusted $k$, hence it is not contradictory. Additionally, as $k$-means
creates convex clusters only, we demonstrate that it is possible to create a
version detecting concave clusters and still the axiomatic system can be
satisfied. The practical application area of such an axiomatic system may be
the generation of new labeled test data from existent ones for clustering
algorithm testing. %We propose the gravitational consistency as a replacement
which does not have this deficiency.
| [
{
"version": "v1",
"created": "Sat, 12 Feb 2022 08:25:01 GMT"
}
] | 1,658,707,200,000 | [
[
"Klopotek",
"Mieczyslaw A.",
""
],
[
"Klopotek",
"Robert A.",
""
]
] |
2202.07065 | Philippe Giabbanelli | Maciej K Wozniak, Samvel Mkhitaryan, Philippe j. Giabbanelli | Automatic Generation of Individual Fuzzy Cognitive Maps from
Longitudinal Data | null | null | 10.1007/978-3-031-08757-8_27 | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Fuzzy Cognitive Maps (FCMs) are computational models that represent how
factors (nodes) change over discrete interactions based on causal impacts
(weighted directed edges) from other factors. This approach has traditionally
been used as an aggregate, similarly to System Dynamics, to depict the
functioning of a system. There has been a growing interest in taking this
aggregate approach at the individual-level, for example by equipping each agent
of an Agent-Based Model with its own FCM to express its behavior. Although
frameworks and studies have already taken this approach, an ongoing limitation
has been the difficulty of creating as many FCMs as there are individuals.
Indeed, current studies have been able to create agents whose traits are
different, but whose decision-making modules are often identical, thus limiting
the behavioral heterogeneity of the simulated population. In this paper, we
address this limitation by using Genetic Algorithms to create one FCM for each
agent, thus providing the means to automatically create a virtual population
with heterogeneous behaviors. Our algorithm builds on prior work from Stach and
colleagues by introducing additional constraints into the process and applying
it over longitudinal, individual-level data. A case study from a real-world
intervention on nutrition confirms that our approach can generate heterogeneous
agents that closely follow the trajectories of their real-world human
counterparts. Future works include technical improvements such as lowering the
computational time of the approach, or case studies in computational
intelligence that use our virtual populations to test new behavior change
interventions.
| [
{
"version": "v1",
"created": "Mon, 14 Feb 2022 22:11:58 GMT"
}
] | 1,668,470,400,000 | [
[
"Wozniak",
"Maciej K",
""
],
[
"Mkhitaryan",
"Samvel",
""
],
[
"Giabbanelli",
"Philippe j.",
""
]
] |
2202.07096 | Tri Minh Nguyen | Tri Minh Nguyen, Thin Nguyen, Truyen Tran | Learning to Discover Medicines | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Discovering new medicines is the hallmark of human endeavor to live a better
and longer life. Yet the pace of discovery has slowed down as we need to
venture into more wildly unexplored biomedical space to find one that matches
today's high standard. Modern AI-enabled by powerful computing, large
biomedical databases, and breakthroughs in deep learning-offers a new hope to
break this loop as AI is rapidly maturing, ready to make a huge impact in the
area. In this paper we review recent advances in AI methodologies that aim to
crack this challenge. We organize the vast and rapidly growing literature of AI
for drug discovery into three relatively stable sub-areas: (a) representation
learning over molecular sequences and geometric graphs; (b) data-driven
reasoning where we predict molecular properties and their binding, optimize
existing compounds, generate de novo molecules, and plan the synthesis of
target molecules; and (c) knowledge-based reasoning where we discuss the
construction and reasoning over biomedical knowledge graphs. We will also
identify open challenges and chart possible research directions for the years
to come.
| [
{
"version": "v1",
"created": "Mon, 14 Feb 2022 23:43:51 GMT"
}
] | 1,644,969,600,000 | [
[
"Nguyen",
"Tri Minh",
""
],
[
"Nguyen",
"Thin",
""
],
[
"Tran",
"Truyen",
""
]
] |
2202.07412 | Wen Zhang | Wen Zhang, Jiaoyan Chen, Juan Li, Zezhong Xu, Jeff Z. Pan, Huajun Chen | Knowledge Graph Reasoning with Logics and Embeddings: Survey and
Perspective | This is a survey of Knowledge Graph Reasoning with Logics and
Embeddings. We discuss methods from diverse perspectives | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge graph (KG) reasoning is becoming increasingly popular in both
academia and industry. Conventional KG reasoning based on symbolic logic is
deterministic, with reasoning results being explainable, while modern
embedding-based reasoning can deal with uncertainty and predict plausible
knowledge, often with high efficiency via vector computation. A promising
direction is to integrate both logic-based and embedding-based methods, with
the vision to have advantages of both. It has attracted wide research attention
with more and more works published in recent years. In this paper, we
comprehensively survey these works, focusing on how logics and embeddings are
integrated. We first briefly introduce preliminaries, then systematically
categorize and discuss works of logic and embedding-aware KG reasoning from
different perspectives, and finally conclude and discuss the challenges and
further directions.
| [
{
"version": "v1",
"created": "Tue, 15 Feb 2022 13:59:54 GMT"
}
] | 1,644,969,600,000 | [
[
"Zhang",
"Wen",
""
],
[
"Chen",
"Jiaoyan",
""
],
[
"Li",
"Juan",
""
],
[
"Xu",
"Zezhong",
""
],
[
"Pan",
"Jeff Z.",
""
],
[
"Chen",
"Huajun",
""
]
] |
2202.07553 | Xuanxiang Huang | Xuanxiang Huang, Joao Marques-Silva | On Deciding Feature Membership in Explanations of SDD & Related
Classifiers | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | When reasoning about explanations of Machine Learning (ML) classifiers, a
pertinent query is to decide whether some sensitive features can serve for
explaining a given prediction. Recent work showed that the feature membership
problem (FMP) is hard for $\Sigma_2^P$ for a broad class of classifiers. In
contrast, this paper shows that for a number of families of classifiers, FMP is
in NP. Concretely, the paper proves that any classifier for which an
explanation can be computed in polynomial time, then deciding feature
membership in an explanation can be decided with one NP oracle call. The paper
then proposes propositional encodings for classifiers represented with
Sentential Decision Diagrams (SDDs) and for other related propositional
languages. The experimental results confirm the practical efficiency of the
proposed approach.
| [
{
"version": "v1",
"created": "Tue, 15 Feb 2022 16:38:53 GMT"
}
] | 1,644,969,600,000 | [
[
"Huang",
"Xuanxiang",
""
],
[
"Marques-Silva",
"Joao",
""
]
] |
2202.07596 | Giovanni Casini | Giovanni Casini, Umberto Straccia | A General Framework for Modelling Conditional Reasoning -- Preliminary
Report | 21 pages, 2 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We introduce and investigate here a formalisation for conditionals that
allows the definition of a broad class of reasoning systems. This framework
covers the most popular kinds of conditional reasoning in logic-based KR: the
semantics we propose is appropriate for a structural analysis of those
conditionals that do not satisfy closure properties associated to classical
logics.
| [
{
"version": "v1",
"created": "Tue, 15 Feb 2022 17:33:39 GMT"
}
] | 1,644,969,600,000 | [
[
"Casini",
"Giovanni",
""
],
[
"Straccia",
"Umberto",
""
]
] |
2202.07760 | Fabrizio Maria Maggi | Williams Rizzi, Marco Comuzzi, Chiara Di Francescomarino, Chiara
Ghidini, Suhwan Lee, Fabrizio Maria Maggi, Alexander Nolte | Explainable Predictive Process Monitoring: A User Evaluation | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Explainability is motivated by the lack of transparency of black-box Machine
Learning approaches, which do not foster trust and acceptance of Machine
Learning algorithms. This also happens in the Predictive Process Monitoring
field, where predictions, obtained by applying Machine Learning techniques,
need to be explained to users, so as to gain their trust and acceptance. In
this work, we carry on a user evaluation on explanation approaches for
Predictive Process Monitoring aiming at investigating whether and how the
explanations provided (i) are understandable; (ii) are useful in decision
making tasks;(iii) can be further improved for process analysts, with different
Machine Learning expertise levels. The results of the user evaluation show
that, although explanation plots are overall understandable and useful for
decision making tasks for Business Process Management users -- with and without
experience in Machine Learning -- differences exist in the comprehension and
usage of different plots, as well as in the way users with different Machine
Learning expertise understand and use them.
| [
{
"version": "v1",
"created": "Tue, 15 Feb 2022 22:24:21 GMT"
}
] | 1,645,056,000,000 | [
[
"Rizzi",
"Williams",
""
],
[
"Comuzzi",
"Marco",
""
],
[
"Di Francescomarino",
"Chiara",
""
],
[
"Ghidini",
"Chiara",
""
],
[
"Lee",
"Suhwan",
""
],
[
"Maggi",
"Fabrizio Maria",
""
],
[
"Nolte",
"Alexander",
""
]
] |
2202.07919 | Rui Li | Rui Li, Jianan Zhao, Chaozhuo Li, Di He, Yiqi Wang, Yuming Liu, Hao
Sun, Senzhang Wang, Weiwei Deng, Yanming Shen, Xing Xie, Qi Zhang | HousE: Knowledge Graph Embedding with Householder Parameterization | Accepted by ICML 2022 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The effectiveness of knowledge graph embedding (KGE) largely depends on the
ability to model intrinsic relation patterns and mapping properties. However,
existing approaches can only capture some of them with insufficient modeling
capacity. In this work, we propose a more powerful KGE framework named HousE,
which involves a novel parameterization based on two kinds of Householder
transformations: (1) Householder rotations to achieve superior capacity of
modeling relation patterns; (2) Householder projections to handle sophisticated
relation mapping properties. Theoretically, HousE is capable of modeling
crucial relation patterns and mapping properties simultaneously. Besides, HousE
is a generalization of existing rotation-based models while extending the
rotations to high-dimensional spaces. Empirically, HousE achieves new
state-of-the-art performance on five benchmark datasets. Our code is available
at https://github.com/anrep/HousE.
| [
{
"version": "v1",
"created": "Wed, 16 Feb 2022 08:13:23 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Jun 2022 15:26:25 GMT"
},
{
"version": "v3",
"created": "Sun, 19 Jun 2022 12:52:43 GMT"
}
] | 1,655,856,000,000 | [
[
"Li",
"Rui",
""
],
[
"Zhao",
"Jianan",
""
],
[
"Li",
"Chaozhuo",
""
],
[
"He",
"Di",
""
],
[
"Wang",
"Yiqi",
""
],
[
"Liu",
"Yuming",
""
],
[
"Sun",
"Hao",
""
],
[
"Wang",
"Senzhang",
""
],
[
"Deng",
"Weiwei",
""
],
[
"Shen",
"Yanming",
""
],
[
"Xie",
"Xing",
""
],
[
"Zhang",
"Qi",
""
]
] |
2202.08856 | Kai Sauerwald | Kai Sauerwald and Christoph Beierle | Iterated Belief Change, Computationally | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Iterated Belief Change is the research area that investigates principles for
the dynamics of beliefs over (possibly unlimited) many subsequent belief
changes. In this paper, we demonstrate how iterated belief change is connected
to computation. In particular, we show that iterative belief revision is Turing
complete, even under the condition that broadly accepted principles like the
Darwiche-Pearl postulates for iterated revision hold.
| [
{
"version": "v1",
"created": "Thu, 17 Feb 2022 19:01:20 GMT"
}
] | 1,645,401,600,000 | [
[
"Sauerwald",
"Kai",
""
],
[
"Beierle",
"Christoph",
""
]
] |
2202.08992 | Zhongqiang Ren | Zhongqiang Ren, Richard Zhan, Sivakumar Rathinam, Maxim Likhachev and
Howie Choset | Enhanced Multi-Objective A* Using Balanced Binary Search Trees | Accepted to SoCS 2022, 11 pages, 4 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work addresses a Multi-Objective Shortest Path Problem (MO-SPP) on a
graph where the goal is to find a set of Pareto-optimal solutions from a start
node to a destination in the graph. A family of approaches based on MOA* have
been developed to solve MO-SPP in the literature. Typically, these approaches
maintain a "frontier" set at each node during the search process to keep track
of the non-dominated, partial paths to reach that node. This search process
becomes computationally expensive when the number of objectives increases as
the number of Pareto-optimal solutions becomes large. In this work, we
introduce a new method to efficiently maintain these frontiers for multiple
objectives by incrementally constructing balanced binary search trees within
the MOA* search framework. We first show that our approach correctly finds the
Pareto-optimal front, and then provide extensive simulation results for
problems with three, four and five objectives to show that our method runs
faster than existing techniques by up to an order of magnitude.
| [
{
"version": "v1",
"created": "Fri, 18 Feb 2022 02:54:58 GMT"
},
{
"version": "v2",
"created": "Sat, 19 Mar 2022 12:23:05 GMT"
},
{
"version": "v3",
"created": "Sat, 28 May 2022 16:09:55 GMT"
}
] | 1,653,955,200,000 | [
[
"Ren",
"Zhongqiang",
""
],
[
"Zhan",
"Richard",
""
],
[
"Rathinam",
"Sivakumar",
""
],
[
"Likhachev",
"Maxim",
""
],
[
"Choset",
"Howie",
""
]
] |
2202.09163 | Claudia Schon | Claudia Schon | Selection Strategies for Commonsense Knowledge | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Selection strategies are broadly used in first-order logic theorem proving to
select those parts of a large knowledge base that are necessary to proof a
theorem at hand. Usually, these selection strategies do not take the meaning of
symbol names into account. In knowledge bases with commonsense knowledge,
symbol names are usually chosen to have a meaning and this meaning provides
valuable information for selection strategies. We introduce the vector-based
selection strategy, a purely statistical selection technique for commonsense
knowledge based on word embeddings. We compare different commonsense knowledge
selection techniques for the purpose of theorem proving and demonstrate the
usefulness of vector-based selection with a case study.
| [
{
"version": "v1",
"created": "Fri, 18 Feb 2022 12:28:09 GMT"
},
{
"version": "v2",
"created": "Mon, 21 Feb 2022 06:39:05 GMT"
}
] | 1,645,488,000,000 | [
[
"Schon",
"Claudia",
""
]
] |
2202.09301 | Breno Maur\'icio de Freitas Viana | Breno M. F. Viana, Leonardo T. Pereira, Claudio F. M. Toledo
(Universidade de S\~ao Paulo) | Illuminating the Space of Dungeon Maps, Locked-door Missions and Enemy
Placement Through MAP-Elites | 9 pages, 7 figures, submitted to FDG '22 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Procedural Content Generation (PCG) methods are valuable tools to speed up
the game development process. Moreover, PCG may also present in games as
features, such as the procedural dungeon generation (PDG) in Moonlighter
(Digital Sun, 2018). This paper introduces an extended version of an
evolutionary dungeon generator by incorporating a MAP-Elites population. Our
dungeon levels are discretized with rooms that may have locked-door missions
and enemies within them. We encoded the dungeons through a tree structure to
ensure the feasibility of missions. We performed computational and user
feedback experiments to evaluate our PDG approach. They show that our approach
accurately converges almost the whole MAP-Elite population for most executions.
Finally, players' feedback indicates that they enjoyed the generated levels,
and they could not indicate an algorithm as a level generator.
| [
{
"version": "v1",
"created": "Fri, 18 Feb 2022 17:06:04 GMT"
},
{
"version": "v2",
"created": "Wed, 6 Apr 2022 20:32:01 GMT"
}
] | 1,649,376,000,000 | [
[
"Viana",
"Breno M. F.",
"",
"Universidade de São Paulo"
],
[
"Pereira",
"Leonardo T.",
"",
"Universidade de São Paulo"
],
[
"Toledo",
"Claudio F. M.",
"",
"Universidade de São Paulo"
]
] |
2202.09464 | Chengjin Xu | Chengjin Xu, Mojtaba Nayyeri, Yung-Yu Chen, and Jens Lehmann | Geometric Algebra based Embeddings for Static and Temporal Knowledge
Graph Completion | There are some theorem mistakes in the Appendix section need to be
fixed. And we are still trying to solve them. We submitted the Arxiv version
for providing the supplementary analysis, but now we hope to withdraw the
current version to avoid misleading the readers from Arxiv | null | 10.1109/TKDE.2022.3151435 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Recent years, Knowledge Graph Embeddings (KGEs) have shown promising
performance on link prediction tasks by mapping the entities and relations from
a Knowledge Graph (KG) into a geometric space and thus have gained increasing
attentions. In addition, many recent Knowledge Graphs involve evolving data,
e.g., the fact (\textit{Obama}, \textit{PresidentOf}, \textit{USA}) is valid
only from 2009 to 2017. This introduces important challenges for knowledge
representation learning since such temporal KGs change over time. In this work,
we strive to move beyond the complex or hypercomplex space for KGE and propose
a novel geometric algebra based embedding approach, GeomE, which uses
multivector representations and the geometric product to model entities and
relations. GeomE subsumes several state-of-the-art KGE models and is able to
model diverse relations patterns. On top of this, we extend GeomE to TGeomE for
temporal KGE, which performs 4th-order tensor factorization of a temporal KG
and devises a new linear temporal regularization for time representation
learning. Moreover, we study the effect of time granularity on the performance
of TGeomE models. Experimental results show that our proposed models achieve
the state-of-the-art performances on link prediction over four commonly-used
static KG datasets and four well-established temporal KG datasets across
various metrics.
| [
{
"version": "v1",
"created": "Fri, 18 Feb 2022 22:52:46 GMT"
},
{
"version": "v2",
"created": "Thu, 24 Feb 2022 14:47:41 GMT"
},
{
"version": "v3",
"created": "Fri, 25 Feb 2022 17:57:28 GMT"
}
] | 1,646,006,400,000 | [
[
"Xu",
"Chengjin",
""
],
[
"Nayyeri",
"Mojtaba",
""
],
[
"Chen",
"Yung-Yu",
""
],
[
"Lehmann",
"Jens",
""
]
] |
2202.09606 | Feihu Che | Feihu Che, Guohua Yang, Pengpeng Shao, Dawei Zhang, Jianhua Tao | MixKG: Mixing for harder negative samples in knowledge graph | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge graph embedding~(KGE) aims to represent entities and relations into
low-dimensional vectors for many real-world applications. The representations
of entities and relations are learned via contrasting the positive and negative
triplets. Thus, high-quality negative samples are extremely important in KGE.
However, the present KGE models either rely on simple negative sampling
methods, which makes it difficult to obtain informative negative triplets; or
employ complex adversarial methods, which requires more training data and
strategies. In addition, these methods can only construct negative triplets
using the existing entities, which limits the potential to explore harder
negative triplets. To address these issues, we adopt mixing operation in
generating harder negative samples for knowledge graphs and introduce an
inexpensive but effective method called MixKG. Technically, MixKG first
proposes two kinds of criteria to filter hard negative triplets among the
sampled negatives: based on scoring function and based on correct entity
similarity. Then, MixKG synthesizes harder negative samples via the convex
combinations of the paired selected hard negatives. Experiments on two public
datasets and four classical KGE methods show MixKG is superior to previous
negative sampling algorithms.
| [
{
"version": "v1",
"created": "Sat, 19 Feb 2022 13:31:06 GMT"
}
] | 1,645,488,000,000 | [
[
"Che",
"Feihu",
""
],
[
"Yang",
"Guohua",
""
],
[
"Shao",
"Pengpeng",
""
],
[
"Zhang",
"Dawei",
""
],
[
"Tao",
"Jianhua",
""
]
] |
2202.09773 | Lige Ding | Lige Ding, Dong Zhao, Zhaofeng Wang, Guang Wang, Chang Tan, Lei Fan
and Huadong Ma | Learning to Help Emergency Vehicles Arrive Faster: A Cooperative
Vehicle-Road Scheduling Approach | 13 pages, 10 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ever-increasing heavy traffic congestion potentially impedes the
accessibility of emergency vehicles (EVs), resulting in detrimental impacts on
critical services and even safety of people's lives. Hence, it is significant
to propose an efficient scheduling approach to help EVs arrive faster. Existing
vehicle-centric scheduling approaches aim to recommend the optimal paths for
EVs based on the current traffic status while the road-centric scheduling
approaches aim to improve the traffic condition and assign a higher priority
for EVs to pass an intersection. With the intuition that real-time vehicle-road
information interaction and strategy coordination can bring more benefits, we
propose LEVID, a LEarning-based cooperative VehIcle-roaD scheduling approach
including a real-time route planning module and a collaborative traffic signal
control module, which interact with each other and make decisions iteratively.
The real-time route planning module adapts the artificial potential field
method to address the real-time changes of traffic signals and avoid falling
into a local optimum. The collaborative traffic signal control module leverages
a graph attention reinforcement learning framework to extract the latent
features of different intersections and abstract their interplay to learn
cooperative policies. Extensive experiments based on multiple real-world
datasets show that our approach outperforms the state-of-the-art baselines.
| [
{
"version": "v1",
"created": "Sun, 20 Feb 2022 10:25:15 GMT"
}
] | 1,645,488,000,000 | [
[
"Ding",
"Lige",
""
],
[
"Zhao",
"Dong",
""
],
[
"Wang",
"Zhaofeng",
""
],
[
"Wang",
"Guang",
""
],
[
"Tan",
"Chang",
""
],
[
"Fan",
"Lei",
""
],
[
"Ma",
"Huadong",
""
]
] |
2202.09836 | Alexander Steen | Alexander Steen, David Fuenmayor, Tobias Glei{\ss}ner, Geoff
Sutcliffe, Christoph Benzm\"uller | Automated Reasoning in Non-classical Logics in the TPTP World | 21 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Non-classical logics are used in a wide spectrum of disciplines, including
artificial intelligence, computer science, mathematics, and philosophy. The
de-facto standard infrastructure for automated theorem proving, the TPTP World,
currently supports only classical logics. Similar standards for non-classical
logic reasoning do not exist (yet). This hampers practical development of
reasoning systems, and limits their interoperability and application. This
paper describes the latest extension of the TPTP World, which provides
languages and infrastructure for reasoning in non-classical logics. The
extensions integrate seamlessly with the existing TPTP World.
| [
{
"version": "v1",
"created": "Sun, 20 Feb 2022 15:29:30 GMT"
}
] | 1,645,488,000,000 | [
[
"Steen",
"Alexander",
""
],
[
"Fuenmayor",
"David",
""
],
[
"Gleißner",
"Tobias",
""
],
[
"Sutcliffe",
"Geoff",
""
],
[
"Benzmüller",
"Christoph",
""
]
] |
2202.10381 | Sheng Zhang | Lihan Chen, Sihang Jiang, Jingping Liu, Chao Wang, Sheng Zhang,
Chenhao Xie, Jiaqing Liang, Yanghua Xiao and Rui Song | Rule Mining over Knowledge Graphs via Reinforcement Learning | Knowledge-Based Systems | KNOSYS_108371, 2022 | 10.1016/j.knosys.2022.108371 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge graphs (KGs) are an important source repository for a wide range of
applications and rule mining from KGs recently attracts wide research interest
in the KG-related research community. Many solutions have been proposed for the
rule mining from large-scale KGs, which however are limited in the inefficiency
of rule generation and ineffectiveness of rule evaluation. To solve these
problems, in this paper we propose a generation-then-evaluation rule mining
approach guided by reinforcement learning. Specifically, a two-phased framework
is designed. The first phase aims to train a reinforcement learning agent for
rule generation from KGs, and the second is to utilize the value function of
the agent to guide the step-by-step rule generation. We conduct extensive
experiments on several datasets and the results prove that our rule mining
solution achieves state-of-the-art performance in terms of efficiency and
effectiveness.
| [
{
"version": "v1",
"created": "Mon, 21 Feb 2022 17:18:31 GMT"
}
] | 1,645,488,000,000 | [
[
"Chen",
"Lihan",
""
],
[
"Jiang",
"Sihang",
""
],
[
"Liu",
"Jingping",
""
],
[
"Wang",
"Chao",
""
],
[
"Zhang",
"Sheng",
""
],
[
"Xie",
"Chenhao",
""
],
[
"Liang",
"Jiaqing",
""
],
[
"Xiao",
"Yanghua",
""
],
[
"Song",
"Rui",
""
]
] |
2202.10695 | Zhuolin Wu | Zhuolin Wu, Li Wang, Fangsheng Huang, Linjun Zhou, Yu Song, Chengpeng
Ye, Pengyu Nie, Hao Ren, Jinghua Hao, Renqing He, Zhizhao Sun | A Framework for Multi-stage Bonus Allocation in meal delivery Platform | 9 pages; submit to KDD 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Online meal delivery is undergoing explosive growth, as this service is
becoming increasingly popular. A meal delivery platform aims to provide
excellent and stable services for customers and restaurants. However, in
reality, several hundred thousand orders are canceled per day in the Meituan
meal delivery platform since they are not accepted by the crowd soucing
drivers. The cancellation of the orders is incredibly detrimental to the
customer's repurchase rate and the reputation of the Meituan meal delivery
platform. To solve this problem, a certain amount of specific funds is provided
by Meituan's business managers to encourage the crowdsourcing drivers to accept
more orders. To make better use of the funds, in this work, we propose a
framework to deal with the multi-stage bonus allocation problem for a meal
delivery platform. The objective of this framework is to maximize the number of
accepted orders within a limited bonus budget. This framework consists of a
semi-black-box acceptance probability model, a Lagrangian dual-based dynamic
programming algorithm, and an online allocation algorithm. The semi-black-box
acceptance probability model is employed to forecast the relationship between
the bonus allocated to order and its acceptance probability, the Lagrangian
dual-based dynamic programming algorithm aims to calculate the empirical
Lagrangian multiplier for each allocation stage offline based on the historical
data set, and the online allocation algorithm uses the results attained in the
offline part to calculate a proper delivery bonus for each order. To verify the
effectiveness and efficiency of our framework, both offline experiments on a
real-world data set and online A/B tests on the Meituan meal delivery platform
are conducted. Our results show that using the proposed framework, the total
order cancellations can be decreased by more than 25\% in reality.
| [
{
"version": "v1",
"created": "Tue, 22 Feb 2022 06:52:34 GMT"
}
] | 1,645,574,400,000 | [
[
"Wu",
"Zhuolin",
""
],
[
"Wang",
"Li",
""
],
[
"Huang",
"Fangsheng",
""
],
[
"Zhou",
"Linjun",
""
],
[
"Song",
"Yu",
""
],
[
"Ye",
"Chengpeng",
""
],
[
"Nie",
"Pengyu",
""
],
[
"Ren",
"Hao",
""
],
[
"Hao",
"Jinghua",
""
],
[
"He",
"Renqing",
""
],
[
"Sun",
"Zhizhao",
""
]
] |
2202.10774 | Maolin Yang | Maolin Yang and Pingyu Jiang | Social Computational Design Method for Generating Product Shapes with
GAN and Transformer Models | 6pages, 6 figures, conference paper | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A social computational design method is established, aiming at taking
advantages of the fast-developing artificial intelligence technologies for
intelligent product design. Supported with multi-agent system, shape grammar,
Generative adversarial network, Bayesian network, Transformer, etc., the method
is able to define the design solution space, prepare training samples, and
eventually acquire an intelligent model that can recommend design solutions
according to incomplete solutions for given design tasks. Product shape design
is used as entry point to demonstrate the method, however, the method can be
applied to tasks rather than shape design when the solutions can be properly
coded.
| [
{
"version": "v1",
"created": "Tue, 22 Feb 2022 09:51:32 GMT"
}
] | 1,645,574,400,000 | [
[
"Yang",
"Maolin",
""
],
[
"Jiang",
"Pingyu",
""
]
] |
2202.11333 | Gaston Zanitti | Gaston Zanitti (PARIETAL), Yamil Soto (UNS), Valentin Iovene
(PARIETAL), Maria Vanina Martinez, Ricardo Rodriguez, Gerardo Simari (UNS),
Demian Wassermann (PARIETAL) | Scalable Query Answering under Uncertainty to Neuroscientific
Ontological Knowledge: The NeuroLang Approach | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Researchers in neuroscience have a growing number of datasets available to
study the brain, which is made possible by recent technological advances. Given
the extent to which the brain has been studied, there is also available
ontological knowledge encoding the current state of the art regarding its
different areas, activation patterns, key words associated with studies, etc.
Furthermore, there is an inherent uncertainty associated with brain scans
arising from the mapping between voxels -- 3D pixels -- and actual points in
different individual brains. Unfortunately, there is currently no unifying
framework for accessing such collections of rich heterogeneous data under
uncertainty, making it necessary for researchers to rely on ad hoc tools. In
particular, one major weakness of current tools that attempt to address this
kind of task is that only very limited propositional query languages have been
developed. In this paper, we present NeuroLang, an ontology language with
existential rules, probabilistic uncertainty, and built-in mechanisms to
guarantee tractable query answering over very large datasets. After presenting
the language and its general query answering architecture, we discuss
real-world use cases showing how NeuroLang can be applied to practical
scenarios for which current tools are inadequate.
| [
{
"version": "v1",
"created": "Wed, 23 Feb 2022 07:34:03 GMT"
}
] | 1,645,660,800,000 | [
[
"Zanitti",
"Gaston",
"",
"PARIETAL"
],
[
"Soto",
"Yamil",
"",
"UNS"
],
[
"Iovene",
"Valentin",
"",
"PARIETAL"
],
[
"Martinez",
"Maria Vanina",
"",
"UNS"
],
[
"Rodriguez",
"Ricardo",
"",
"UNS"
],
[
"Simari",
"Gerardo",
"",
"UNS"
],
[
"Wassermann",
"Demian",
"",
"PARIETAL"
]
] |
2202.11532 | Fedor Scholz | Fedor Scholz, Christian Gumbsch, Sebastian Otte, Martin V. Butz | Inference of Affordances and Active Motor Control in Simulated Agents | 26 pages, 12 figures, submitted to Frontiers in Neurorobotics | null | 10.3389/fnbot.2022.881673 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Flexible, goal-directed behavior is a fundamental aspect of human life. Based
on the free energy minimization principle, the theory of active inference
formalizes the generation of such behavior from a computational neuroscience
perspective. Based on the theory, we introduce an output-probabilistic,
temporally predictive, modular artificial neural network architecture, which
processes sensorimotor information, infers behavior-relevant aspects of its
world, and invokes highly flexible, goal-directed behavior. We show that our
architecture, which is trained end-to-end to minimize an approximation of free
energy, develops latent states that can be interpreted as affordance maps. That
is, the emerging latent states signal which actions lead to which effects
dependent on the local context. In combination with active inference, we show
that flexible, goal-directed behavior can be invoked, incorporating the
emerging affordance maps. As a result, our simulated agent flexibly steers
through continuous spaces, avoids collisions with obstacles, and prefers
pathways that lead to the goal with high certainty. Additionally, we show that
the learned agent is highly suitable for zero-shot generalization across
environments: After training the agent in a handful of fixed environments with
obstacles and other terrains affecting its behavior, it performs similarly well
in procedurally generated environments containing different amounts of
obstacles and terrains of various sizes at different locations.
| [
{
"version": "v1",
"created": "Wed, 23 Feb 2022 14:13:04 GMT"
},
{
"version": "v2",
"created": "Fri, 18 Mar 2022 07:22:44 GMT"
},
{
"version": "v3",
"created": "Tue, 2 Aug 2022 07:36:13 GMT"
}
] | 1,659,484,800,000 | [
[
"Scholz",
"Fedor",
""
],
[
"Gumbsch",
"Christian",
""
],
[
"Otte",
"Sebastian",
""
],
[
"Butz",
"Martin V.",
""
]
] |
2202.11958 | Yihao Li | Fuhui Zhou, Yihao Li, Xinyuan Zhang, Qihui Wu, Xianfu Lei and Rose
Qingyang Hu | Cognitive Semantic Communication Systems Driven by Knowledge Graph | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Semantic communication is envisioned as a promising technique to break
through the Shannon limit. However, the existing semantic communication
frameworks do not involve inference and error correction, which limits the
achievable performance. In this paper, in order to tackle this issue, a
cognitive semantic communication framework is proposed by exploiting knowledge
graph. Moreover, a simple, general and interpretable solution for semantic
information detection is developed by exploiting triples as semantic symbols.
It also allows the receiver to correct errors occurring at the symbolic level.
Furthermore, the pre-trained model is fine-tuned to recover semantic
information, which overcomes the drawback that a fixed bit length coding is
used to encode sentences of different lengths. Simulation results on the public
WebNLG corpus show that our proposed system is superior to other benchmark
systems in terms of the data compression rate and the reliability of
communication.
| [
{
"version": "v1",
"created": "Thu, 24 Feb 2022 08:26:18 GMT"
}
] | 1,645,747,200,000 | [
[
"Zhou",
"Fuhui",
""
],
[
"Li",
"Yihao",
""
],
[
"Zhang",
"Xinyuan",
""
],
[
"Wu",
"Qihui",
""
],
[
"Lei",
"Xianfu",
""
],
[
"Hu",
"Rose Qingyang",
""
]
] |
2202.12003 | Shivani Bathla | Shivani Bathla and Vinita Vasudevan | IBIA: Bayesian Inference via Incremental Build-Infer-Approximate
operations on Clique Trees | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Exact inference in Bayesian networks is intractable and has an exponential
dependence on the size of the largest clique in the corresponding clique tree
(CT), necessitating approximations. Factor based methods to bound clique sizes
are more accurate than structure based methods, but expensive since they
involve inference of beliefs in a large number of candidate structure or region
graphs. We propose an alternative approach for approximate inference based on
an incremental build-infer-approximate (IBIA) paradigm, which converts the
Bayesian network into a data structure containing a sequence of linked clique
tree forests (SLCTF), with clique sizes bounded by a user-specified value. In
the incremental build stage of this approach, CTFs are constructed
incrementally by adding variables to the CTFs as long as clique sizes are
within the specified bound. Once the clique size constraint is reached, the CTs
in the CTF are calibrated in the infer stage of IBIA. The resulting clique
beliefs are used in the approximate phase to get an approximate CTF with
reduced clique sizes. The approximate CTF forms the starting point for the next
CTF in the sequence. These steps are repeated until all variables are added to
a CTF in the sequence. We prove that our algorithm for incremental construction
of clique trees always generates a valid CT and our approximation technique
preserves the joint beliefs of the variables within a clique. Based on this, we
show that the SLCTF data structure can be used for efficient approximate
inference of partition function and prior and posterior marginals. More than
500 benchmarks were used to test the method and the results show a significant
reduction in error when compared to other approximate methods, with competitive
runtimes.
| [
{
"version": "v1",
"created": "Thu, 24 Feb 2022 10:30:31 GMT"
},
{
"version": "v2",
"created": "Wed, 10 Aug 2022 04:28:07 GMT"
}
] | 1,660,176,000,000 | [
[
"Bathla",
"Shivani",
""
],
[
"Vasudevan",
"Vinita",
""
]
] |
2202.12039 | Catriona Kennedy | Catriona M. Kennedy | Metacognitive Agents for Ethical Decision Support: Conceptual Model and
Research Roadmap | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | An ethical value-action gap exists when there is a discrepancy between
intentions and actions. This discrepancy may be caused by social and structural
obstacles as well as cognitive biases. Computational models of cognition and
affect can provide insights into the value-action gap and how it can be
reduced. In particular, metacognition ("thinking about thinking") plays an
important role in many of these models as a mechanism for self-regulation and
reasoning about mental attitudes. This paper outlines a roadmap for translating
cognitive-affective models into assistant agents to help make value-aligned
decisions.
| [
{
"version": "v1",
"created": "Thu, 24 Feb 2022 11:39:57 GMT"
}
] | 1,645,747,200,000 | [
[
"Kennedy",
"Catriona M.",
""
]
] |
2202.12260 | Stefan Bosse | Stefan Bosse | Self-organising Urban Traffic control on micro-level using Reinforcement
Learning and Agent-based Modelling | null | null | 10.1007/978-3-030-55187-2_53 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most traffic flow control algorithms address switching cycle adaptation of
traffic signals and lights. This work addresses traffic flow optimisation by
self-organising micro-level control combining Reinforcement Learning and
rule-based agents for action selection performing long-range navigation in
urban environments. I.e., vehicles represented by agents adapt their decision
making for re-routing based on local environmental sensors. Agent-based
modelling and simulation is used to study emergence effects on urban city
traffic flows. An unified agent programming model enables simulation and
distributed data processing with possible incorporation of crowd sensing tasks
used as an additional sensor data base. Results from an agent-based simulation
of an artificial urban area show that the deployment of micro-level vehicle
navigation control just by learned individual decision making and re-routing
based on local environmental sensors can increase the efficiency of mobility in
terms of path length and travelling time.
| [
{
"version": "v1",
"created": "Thu, 24 Feb 2022 18:10:42 GMT"
}
] | 1,645,747,200,000 | [
[
"Bosse",
"Stefan",
""
]
] |
2202.12466 | Jiahui Duan | Jiahui Duan, Xialiang Tong, Fei Ni, Zhenan He, Lei Chen, Mingxuan Yuan | A Data-Driven Column Generation Algorithm For Bin Packing Problem in
Manufacturing Industry | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The bin packing problem exists widely in real logistic scenarios (e.g.,
packing pipeline, express delivery), with its goal to improve the packing
efficiency and reduce the transportation cost. In this NP-hard combinatorial
optimization problem, the position and quantity of each item in the box are
strictly restricted by complex constraints and special customer requirements.
Existing approaches are hard to obtain the optimal solution since rigorous
constraints cannot be handled within a reasonable computation load. In this
paper, for handling this difficulty, the packing knowledge is extracted from
historical data collected from the packing pipeline of Huawei. First, by fully
exploiting the relationship between historical packing records and input
orders(orders to be packed) , the problem is reformulated as a set cover
problem. Then, two novel strategies, the constraint handling and process
acceleration strategies are applied to the classic column generation approach
to solve this set cover problem. The cost of solving pricing problem for
generating new columns is high due to the complex constraints and customer
requirements. The proposed constraints handling strategy exploits the
historical packing records with the most negative value of the reduced cost.
Those constraints have been implicitly satisfied in these historical packing
records so that there is no need to conduct further evaluation on constraints,
thus the computational load is saved. To further eliminate the iteration
process of column generation algorithm and accelerate the optimization process,
a Learning to Price approach called Modified Pointer Network is proposed, by
which we can determine which historical packing records should be selected
directly. Through experiments on realworld datasets, we show our proposed
method can improve the packing success rate and decrease the computation time
simultaneously.
| [
{
"version": "v1",
"created": "Fri, 25 Feb 2022 02:38:24 GMT"
}
] | 1,646,006,400,000 | [
[
"Duan",
"Jiahui",
""
],
[
"Tong",
"Xialiang",
""
],
[
"Ni",
"Fei",
""
],
[
"He",
"Zhenan",
""
],
[
"Chen",
"Lei",
""
],
[
"Yuan",
"Mingxuan",
""
]
] |
2202.12566 | Peter Sch\"uller | Peter Sch\"uller, Jo\~ao Paolo Costeira, James Crowley, Jasmin
Grosinger, F\'elix Ingrand, Uwe K\"ockemann, Alessandro Saffiotti, Martin
Welss | Composing Complex and Hybrid AI Solutions | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Progress in several areas of computer science has been enabled by comfortable
and efficient means of experimentation, clear interfaces, and interchangable
components, for example using OpenCV for computer vision or ROS for robotics.
We describe an extension of the Acumos system towards enabling the above
features for general AI applications. Originally, Acumos was created for
telecommunication purposes, mainly for creating linear pipelines of machine
learning components. Our extensions include support for more generic components
with gRPC/Protobuf interfaces, automatic orchestration of graphically assembled
solutions including control loops, sub-component topologies, and event-based
communication,and provisions for assembling solutions which contain user
interfaces and shared storage areas. We provide examples of deployable
solutions and their interfaces. The framework is deployed at
http://aiexp.ai4europe.eu/ and its source code is managed as an open source
Eclipse project.
| [
{
"version": "v1",
"created": "Fri, 25 Feb 2022 08:57:06 GMT"
}
] | 1,646,006,400,000 | [
[
"Schüller",
"Peter",
""
],
[
"Costeira",
"João Paolo",
""
],
[
"Crowley",
"James",
""
],
[
"Grosinger",
"Jasmin",
""
],
[
"Ingrand",
"Félix",
""
],
[
"Köckemann",
"Uwe",
""
],
[
"Saffiotti",
"Alessandro",
""
],
[
"Welss",
"Martin",
""
]
] |
2202.12622 | Per R. Leikanger | Per R. Leikanger | Towards neoRL networks; the emergence of purposive graphs | Submission to RLDM 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The neoRL framework for purposive AI implements latent learning by emulated
cognitive maps, with general value functions (GVF) expressing operant desires
toward separate states. The agent's expectancy of reward, expressed as learned
projections in the considered space, allows the neoRL agent to extract
purposive behavior from the learned map according to the reward hypothesis. We
explore this allegory further, considering neoRL modules as nodes in a network
with desire as input and state-action Q-value as output; we see that action
sets with Euclidean significance imply an interpretation of state-action
vectors as Euclidean projections of desire. Autonomous desire from neoRL nodes
within the agent allows for deeper neoRL behavioral graphs. Experiments confirm
the effect of neoRL networks governed by autonomous desire, verifying the four
principles for purposive networks. A neoRL agent governed by purposive networks
can navigate Euclidean spaces in real-time while learning, exemplifying how
modern AI still can profit from inspiration from early psychology.
| [
{
"version": "v1",
"created": "Fri, 25 Feb 2022 11:19:05 GMT"
}
] | 1,646,006,400,000 | [
[
"Leikanger",
"Per R.",
""
]
] |
2202.12954 | Anthony Sarah | Anthony Sarah, Daniel Cummings, Sharath Nittur Sridhar, Sairam
Sundaresan, Maciej Szankin, Tristan Webb, J. Pablo Munoz | A Hardware-Aware System for Accelerating Deep Neural Network
Optimization | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advances in Neural Architecture Search (NAS) which extract specialized
hardware-aware configurations (a.k.a. "sub-networks") from a hardware-agnostic
"super-network" have become increasingly popular. While considerable effort has
been employed towards improving the first stage, namely, the training of the
super-network, the search for derivative high-performing sub-networks is still
largely under-explored. For example, some recent network morphism techniques
allow a super-network to be trained once and then have hardware-specific
networks extracted from it as needed. These methods decouple the super-network
training from the sub-network search and thus decrease the computational burden
of specializing to different hardware platforms. We propose a comprehensive
system that automatically and efficiently finds sub-networks from a pre-trained
super-network that are optimized to different performance metrics and hardware
configurations. By combining novel search tactics and algorithms with
intelligent use of predictors, we significantly decrease the time needed to
find optimal sub-networks from a given super-network. Further, our approach
does not require the super-network to be refined for the target task a priori,
thus allowing it to interface with any super-network. We demonstrate through
extensive experiments that our system works seamlessly with existing
state-of-the-art super-network training methods in multiple domains. Moreover,
we show how novel search tactics paired with evolutionary algorithms can
accelerate the search process for ResNet50, MobileNetV3 and Transformer while
maintaining objective space Pareto front diversity and demonstrate an 8x faster
search result than the state-of-the-art Bayesian optimization WeakNAS approach.
| [
{
"version": "v1",
"created": "Fri, 25 Feb 2022 20:07:29 GMT"
}
] | 1,646,092,800,000 | [
[
"Sarah",
"Anthony",
""
],
[
"Cummings",
"Daniel",
""
],
[
"Sridhar",
"Sharath Nittur",
""
],
[
"Sundaresan",
"Sairam",
""
],
[
"Szankin",
"Maciej",
""
],
[
"Webb",
"Tristan",
""
],
[
"Munoz",
"J. Pablo",
""
]
] |
2202.13003 | Geoffrey Pettet | Geoffrey Pettet, Ayan Mukhopadhyay, Abhishek Dubey | Decision Making in Non-Stationary Environments with Policy-Augmented
Monte Carlo Tree Search | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decision-making under uncertainty (DMU) is present in many important
problems. An open challenge is DMU in non-stationary environments, where the
dynamics of the environment can change over time. Reinforcement Learning (RL),
a popular approach for DMU problems, learns a policy by interacting with a
model of the environment offline. Unfortunately, if the environment changes the
policy can become stale and take sub-optimal actions, and relearning the policy
for the updated environment takes time and computational effort. An alternative
is online planning approaches such as Monte Carlo Tree Search (MCTS), which
perform their computation at decision time. Given the current environment, MCTS
plans using high-fidelity models to determine promising action trajectories.
These models can be updated as soon as environmental changes are detected to
immediately incorporate them into decision making. However, MCTS's convergence
can be slow for domains with large state-action spaces. In this paper, we
present a novel hybrid decision-making approach that combines the strengths of
RL and planning while mitigating their weaknesses. Our approach, called Policy
Augmented MCTS (PA-MCTS), integrates a policy's actin-value estimates into
MCTS, using the estimates to seed the action trajectories favored by the
search. We hypothesize that PA-MCTS will converge more quickly than standard
MCTS while making better decisions than the policy can make on its own when
faced with nonstationary environments. We test our hypothesis by comparing
PA-MCTS with pure MCTS and an RL agent applied to the classical CartPole
environment. We find that PC-MCTS can achieve higher cumulative rewards than
the policy in isolation under several environmental shifts while converging in
significantly fewer iterations than pure MCTS.
| [
{
"version": "v1",
"created": "Fri, 25 Feb 2022 22:31:37 GMT"
}
] | 1,646,092,800,000 | [
[
"Pettet",
"Geoffrey",
""
],
[
"Mukhopadhyay",
"Ayan",
""
],
[
"Dubey",
"Abhishek",
""
]
] |
2202.13041 | Wensheng Gan | Wensheng Gan, Guoting Chen, Hongzhi Yin, Philippe Fournier-Viger,
Chien-Ming Chen, and Philip S. Yu | Towards Revenue Maximization with Popular and Profitable Products | ACM/IMS Transactions on Data Science. 4 figures, 5 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Economic-wise, a common goal for companies conducting marketing is to
maximize the return revenue/profit by utilizing the various effective marketing
strategies. Consumer behavior is crucially important in economy and targeted
marketing, in which behavioral economics can provide valuable insights to
identify the biases and profit from customers. Finding credible and reliable
information on products' profitability is, however, quite difficult since most
products tends to peak at certain times w.r.t. seasonal sales cycle in a year.
On-Shelf Availability (OSA) plays a key factor for performance evaluation.
Besides, staying ahead of hot product trends means we can increase marketing
efforts without selling out the inventory. To fulfill this gap, in this paper,
we first propose a general profit-oriented framework to address the problem of
revenue maximization based on economic behavior, and compute the 0n-shelf
Popular and most Profitable Products (OPPPs) for the targeted marketing. To
tackle the revenue maximization problem, we model the k-satisfiable product
concept and propose an algorithmic framework for searching OPPP and its
variants. Extensive experiments are conducted on several real-world datasets to
evaluate the effectiveness and efficiency of the proposed algorithm.
| [
{
"version": "v1",
"created": "Sat, 26 Feb 2022 02:07:25 GMT"
}
] | 1,646,092,800,000 | [
[
"Gan",
"Wensheng",
""
],
[
"Chen",
"Guoting",
""
],
[
"Yin",
"Hongzhi",
""
],
[
"Fournier-Viger",
"Philippe",
""
],
[
"Chen",
"Chien-Ming",
""
],
[
"Yu",
"Philip S.",
""
]
] |
2202.13101 | Bhushan Jagyasi | Jinu Jayan, Saurabh Pashine, Pallavi Gawade, Bhushan Jagyasi, Sreedhar
Seetharam, Gopali Contractor, Rajesh kumar Palani, Harshit Sampgaon, Sandeep
Vaity, Tamal Bhattacharyya, Rengaraj Ramasubbu | Sustainability using Renewable Electricity (SuRE) towards NetZero
Emissions | 8 pages, 10 Figures, 3 tables, 20 References, IEEE Conference
template | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Demand for energy has increased significantly across the globe due to
increase in population and economic growth. Growth in energy demand poses
serious threat to the environment since majority of the energy sources are
non-renewable and based on fossil fuels, which leads to emission of harmful
greenhouse gases. Organizations across the world are facing challenges in
transitioning from fossil fuels-based sources to greener sources to reduce
their carbon footprint. As a step towards achieving Net-Zero emission target,
we present a scalable AI based solution that can be used by organizations to
increase their overall renewable electricity share in total energy consumption.
Our solution provides facilities with accurate energy demand forecast,
recommendation for procurement of renewable electricity to optimize cost and
carbon offset recommendations to compensate for Greenhouse Gas (GHG) emissions.
This solution has been used in production for more than a year for four
facilities and has increased their renewable electricity share significantly.
| [
{
"version": "v1",
"created": "Sat, 26 Feb 2022 10:04:26 GMT"
}
] | 1,646,092,800,000 | [
[
"Jayan",
"Jinu",
""
],
[
"Pashine",
"Saurabh",
""
],
[
"Gawade",
"Pallavi",
""
],
[
"Jagyasi",
"Bhushan",
""
],
[
"Seetharam",
"Sreedhar",
""
],
[
"Contractor",
"Gopali",
""
],
[
"Palani",
"Rajesh kumar",
""
],
[
"Sampgaon",
"Harshit",
""
],
[
"Vaity",
"Sandeep",
""
],
[
"Bhattacharyya",
"Tamal",
""
],
[
"Ramasubbu",
"Rengaraj",
""
]
] |
2202.13196 | Seonghyeon Lee | Seonghyeon Lee, Dongha Lee, Seongbo Jang, Hwanjo Yu | Toward Interpretable Semantic Textual Similarity via Optimal
Transport-based Contrastive Sentence Learning | ACL 2022 main + camera-ready version | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Recently, finetuning a pretrained language model to capture the similarity
between sentence embeddings has shown the state-of-the-art performance on the
semantic textual similarity (STS) task. However, the absence of an
interpretation method for the sentence similarity makes it difficult to explain
the model output. In this work, we explicitly describe the sentence distance as
the weighted sum of contextualized token distances on the basis of a
transportation problem, and then present the optimal transport-based distance
measure, named RCMD; it identifies and leverages semantically-aligned token
pairs. In the end, we propose CLRCMD, a contrastive learning framework that
optimizes RCMD of sentence pairs, which enhances the quality of sentence
similarity and their interpretation. Extensive experiments demonstrate that our
learning framework outperforms other baselines on both STS and
interpretable-STS benchmarks, indicating that it computes effective sentence
similarity and also provides interpretation consistent with human judgement.
The code and checkpoint are publicly available at
https://github.com/sh0416/clrcmd.
| [
{
"version": "v1",
"created": "Sat, 26 Feb 2022 17:28:02 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Apr 2022 01:03:08 GMT"
}
] | 1,649,980,800,000 | [
[
"Lee",
"Seonghyeon",
""
],
[
"Lee",
"Dongha",
""
],
[
"Jang",
"Seongbo",
""
],
[
"Yu",
"Hwanjo",
""
]
] |
2202.13250 | Peter Nightingale | \"Ozg\"ur Akg\"un, Ian P. Gent, Christopher Jefferson, Zeynep
Kiziltan, Ian Miguel, Peter Nightingale, Andr\'as Z. Salamon, Felix
Ulrich-Oltean | Automatic Tabulation in Constraint Models | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The performance of a constraint model can often be improved by converting a
subproblem into a single table constraint. In this paper we study heuristics
for identifying promising candidate subproblems, where converting the candidate
into a table constraint is likely to improve solver performance. We propose a
small set of heuristics to identify common cases, such as expressions that will
propagate weakly. The process of discovering promising subproblems and
tabulating them is entirely automated in the constraint modelling tool Savile
Row. Caches are implemented to avoid tabulating equivalent subproblems many
times. We give a simple algorithm to generate table constraints directly from a
constraint expression in \savilerow. We demonstrate good performance on the
benchmark problems used in earlier work on tabulation, and also for several new
problem classes. In some cases, the entirely automated process leads to orders
of magnitude improvements in solver performance.
| [
{
"version": "v1",
"created": "Sat, 26 Feb 2022 23:25:38 GMT"
}
] | 1,646,092,800,000 | [
[
"Akgün",
"Özgür",
""
],
[
"Gent",
"Ian P.",
""
],
[
"Jefferson",
"Christopher",
""
],
[
"Kiziltan",
"Zeynep",
""
],
[
"Miguel",
"Ian",
""
],
[
"Nightingale",
"Peter",
""
],
[
"Salamon",
"András Z.",
""
],
[
"Ulrich-Oltean",
"Felix",
""
]
] |
2202.13252 | Richard Sutton | Richard S. Sutton | The Quest for a Common Model of the Intelligent Decision Maker | Will appear as an extended abstract at the fifth Multi-disciplinary
Conference on Reinforcement Learning and Decision Making, held in Providence,
Rhode Island, June 8-11, 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The premise of the Multi-disciplinary Conference on Reinforcement Learning
and Decision Making is that multiple disciplines share an interest in
goal-directed decision making over time. The idea of this paper is to sharpen
and deepen this premise by proposing a perspective on the decision maker that
is substantive and widely held across psychology, artificial intelligence,
economics, control theory, and neuroscience, which I call the "common model of
the intelligent agent". The common model does not include anything specific to
any organism, world, or application domain. The common model does include
aspects of the decision maker's interaction with its world (there must be input
and output, and a goal) and internal components of the decision maker (for
perception, decision-making, internal evaluation, and a world model). I
identify these aspects and components, note that they are given different names
in different disciplines but refer essentially to the same ideas, and discuss
the challenges and benefits of devising a neutral terminology that can be used
across disciplines. It is time to recognize and build on the convergence of
multiple diverse disciplines on a substantive common model of the intelligent
agent.
| [
{
"version": "v1",
"created": "Sat, 26 Feb 2022 23:40:42 GMT"
},
{
"version": "v2",
"created": "Fri, 8 Apr 2022 01:09:12 GMT"
},
{
"version": "v3",
"created": "Sun, 5 Jun 2022 22:15:16 GMT"
}
] | 1,654,560,000,000 | [
[
"Sutton",
"Richard S.",
""
]
] |
2202.13406 | Hiroyuki Kido | Hiroyuki Kido | Towards Unifying Logical Entailment and Statistical Estimation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper gives a generative model of the interpretation of formal logic for
data-driven logical reasoning. The key idea is to represent the interpretation
as likelihood of a formula being true given a model of formal logic. Using the
likelihood, Bayes' theorem gives the posterior of the model being the case
given the formula. The posterior represents an inverse interpretation of formal
logic that seeks models making the formula true. The likelihood and posterior
cause Bayesian learning that gives the probability of the conclusion being true
in the models where all the premises are true. This paper looks at statistical
and logical properties of the Bayesian learning. It is shown that the
generative model is a unified theory of several different types of reasoning in
logic and statistics.
| [
{
"version": "v1",
"created": "Sun, 27 Feb 2022 17:51:35 GMT"
}
] | 1,646,092,800,000 | [
[
"Kido",
"Hiroyuki",
""
]
] |
2202.13686 | Yile Chen | Yile Chen, Xiucheng Li, Gao Cong, Cheng Long, Zhifeng Bao, Shang Liu,
Wanli Gu, Fuzheng Zhang | Points-of-Interest Relationship Inference with Spatial-enriched Graph
Neural Networks | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | As a fundamental component in location-based services, inferring the
relationship between points-of-interests (POIs) is very critical for service
providers to offer good user experience to business owners and customers. Most
of the existing methods for relationship inference are not targeted at POI,
thus failing to capture unique spatial characteristics that have huge effects
on POI relationships. In this work we propose PRIM to tackle POI relationship
inference for multiple relation types. PRIM features four novel components,
including a weighted relational graph neural network, category taxonomy
integration, a self-attentive spatial context extractor, and a
distance-specific scoring function. Extensive experiments on two real-world
datasets show that PRIM achieves the best results compared to state-of-the-art
baselines and it is robust against data sparsity and is applicable to unseen
cases in practice.
| [
{
"version": "v1",
"created": "Mon, 28 Feb 2022 11:09:54 GMT"
}
] | 1,646,092,800,000 | [
[
"Chen",
"Yile",
""
],
[
"Li",
"Xiucheng",
""
],
[
"Cong",
"Gao",
""
],
[
"Long",
"Cheng",
""
],
[
"Bao",
"Zhifeng",
""
],
[
"Liu",
"Shang",
""
],
[
"Gu",
"Wanli",
""
],
[
"Zhang",
"Fuzheng",
""
]
] |
2202.13746 | Gyanateet Dutta | Gyanateet Dutta | Solving The Travelling Salesmen Problem using HNN and HNN-SA algorithms | null | Demonstratio Mathematica 29(1):219-231, January 1996 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this case study, the renowned Travelling Salesmen problem has been
studied. Travelling Salesman problem is a most demanding computational problem
in Computer Science. The Travelling Salesmen problem has been solved by two
different ways using Hopfield Network. The main theory of the problem is to
find distance and connectedness between nodes in a graph having edges between
the nodes. The basic algorithm used for this problem is Djikstra's Algorithm.
But till now , a number of such algorithms have evolved. Among them(some other
algorithms) , are distinct and have been proved to solve the travelling
salesmen problem by graph theory.
| [
{
"version": "v1",
"created": "Tue, 8 Feb 2022 13:44:54 GMT"
}
] | 1,646,092,800,000 | [
[
"Dutta",
"Gyanateet",
""
]
] |
2202.13750 | Umberto Straccia | Umberto Straccia and Giovanni Casini | A Minimal Deductive System for RDFS with Negative Statements | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The triple language RDFS is designed to represent and reason with
\emph{positive} statements only (e.g."antipyretics are drugs").
In this paper we show how to extend RDFS to express and reason with various
forms of negative statements under the Open World Assumption (OWA). To do so,
we start from $\rho df$, a minimal, but significant RDFS fragment that covers
all essential features of RDFS, and then extend it to $\rho df_\bot^\neg$,
allowing express also statements such as "radio therapies are non drug
treatments", "Ebola has no treatment", or "opioids and antipyretics are
disjoint classes". The main and, to the best of our knowledge, unique features
of our proposal are: (i) $\rho df_\bot^\neg$ remains syntactically a triple
language by extending $\rho df$ with new symbols with specific semantics and
there is no need to revert to the reification method to represent negative
triples; (ii) the logic is defined in such a way that any RDFS reasoner/store
may handle the new predicates as ordinary terms if it does not want to take
account of the extra capabilities; (iii) despite negated statements, every
$\rho df_\bot^\neg$ knowledge base is satisfiable; (iv) the $\rho df_\bot^\neg$
entailment decision procedure is obtained from $\rho df$ via additional
inference rules favouring a potential implementation; and (v) deciding
entailment in $\rho df_\bot^\neg$ ranges from P to NP.
| [
{
"version": "v1",
"created": "Fri, 11 Feb 2022 13:56:21 GMT"
}
] | 1,646,092,800,000 | [
[
"Straccia",
"Umberto",
""
],
[
"Casini",
"Giovanni",
""
]
] |
2202.13794 | Andrii Maksai | Andrii Maksai, Henry Rowley, Jesse Berent and Claudiu Musat | Inkorrect: Online Handwriting Spelling Correction | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We introduce Inkorrect, a data- and label-efficient approach for online
handwriting (Digital Ink) spelling correction - DISC. Unlike previous work, the
proposed method does not require multiple samples from the same writer, or
access to character level segmentation. We show that existing automatic
evaluation metrics do not fully capture and are not correlated with the human
perception of the quality of the spelling correction, and propose new ones that
correlate with human perception. We additionally surface an interesting
phenomenon: a trade-off between the similarity and recognizability of the
spell-corrected inks. We further create a family of models corresponding to
different points on the Pareto frontier between those two axes. We show that
Inkorrect's Pareto frontier dominates the points that correspond to prior work.
| [
{
"version": "v1",
"created": "Mon, 28 Feb 2022 13:39:26 GMT"
}
] | 1,646,092,800,000 | [
[
"Maksai",
"Andrii",
""
],
[
"Rowley",
"Henry",
""
],
[
"Berent",
"Jesse",
""
],
[
"Musat",
"Claudiu",
""
]
] |
2202.13985 | Stuart Armstrong | Rebecca Gorman, Stuart Armstrong | The dangers in algorithms learning humans' values and irrationalities | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | For an artificial intelligence (AI) to be aligned with human values (or human
preferences), it must first learn those values. AI systems that are trained on
human behavior, risk miscategorising human irrationalities as human values --
and then optimising for these irrationalities. Simply learning human values
still carries risks: AI learning them will inevitably also gain information on
human irrationalities and human behaviour/policy. Both of these can be
dangerous: knowing human policy allows an AI to become generically more
powerful (whether it is partially aligned or not aligned at all), while
learning human irrationalities allows it to exploit humans without needing to
provide value in return. This paper analyses the danger in developing
artificial intelligence that learns about human irrationalities and human
policy, and constructs a model recommendation system with various levels of
information about human biases, human policy, and human values. It concludes
that, whatever the power and knowledge of the AI, it is more dangerous for it
to know human irrationalities than human values. Thus it is better for the AI
to learn human values directly, rather than learning human biases and then
deducing values from behaviour.
| [
{
"version": "v1",
"created": "Mon, 28 Feb 2022 17:41:39 GMT"
},
{
"version": "v2",
"created": "Tue, 1 Mar 2022 11:23:04 GMT"
}
] | 1,646,179,200,000 | [
[
"Gorman",
"Rebecca",
""
],
[
"Armstrong",
"Stuart",
""
]
] |
2202.14018 | Xi Peng | Xi Peng, Zhenwei Tang, Maxat Kulmanov, Kexin Niu, Robert Hoehndorf | Description Logic EL++ Embeddings with Intersectional Closure | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Many ontologies, in particular in the biomedical domain, are based on the
Description Logic EL++. Several efforts have been made to interpret and exploit
EL++ ontologies by distributed representation learning. Specifically, concepts
within EL++ theories have been represented as n-balls within an n-dimensional
embedding space. However, the intersectional closure is not satisfied when
using n-balls to represent concepts because the intersection of two n-balls is
not an n-ball. This leads to challenges when measuring the distance between
concepts and inferring equivalence between concepts. To this end, we developed
EL Box Embedding (ELBE) to learn Description Logic EL++ embeddings using
axis-parallel boxes. We generate specially designed box-based geometric
constraints from EL++ axioms for model training. Since the intersection of
boxes remains as a box, the intersectional closure is satisfied. We report
extensive experimental results on three datasets and present a case study to
demonstrate the effectiveness of the proposed method.
| [
{
"version": "v1",
"created": "Mon, 28 Feb 2022 18:37:14 GMT"
}
] | 1,646,092,800,000 | [
[
"Peng",
"Xi",
""
],
[
"Tang",
"Zhenwei",
""
],
[
"Kulmanov",
"Maxat",
""
],
[
"Niu",
"Kexin",
""
],
[
"Hoehndorf",
"Robert",
""
]
] |
2203.00083 | Debajyoti Kar | Palash Dey, Debajyoti Kar, Swagato Sanyal | Sampling-Based Winner Prediction in District-Based Elections | 27 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In a district-based election, we apply a voting rule $r$ to decide the
winners in each district, and a candidate who wins in a maximum number of
districts is the winner of the election. We present efficient sampling-based
algorithms to predict the winner of such district-based election systems in
this paper. When $r$ is plurality and the margin of victory is known to be at
least $\varepsilon$ fraction of the total population, we present an algorithm
to predict the winner. The sample complexity of our algorithm is
$\mathcal{O}\left(\frac{1}{\varepsilon^4}\log
\frac{1}{\varepsilon}\log\frac{1}{\delta}\right)$. We complement this result by
proving that any algorithm, from a natural class of algorithms, for predicting
the winner in a district-based election when $r$ is plurality, must sample at
least $\Omega\left(\frac{1}{\varepsilon^4}\log\frac{1}{\delta}\right)$ votes.
We then extend this result to any voting rule $r$. Loosely speaking, we show
that we can predict the winner of a district-based election with an extra
overhead of
$\mathcal{O}\left(\frac{1}{\varepsilon^2}\log\frac{1}{\delta}\right)$ over the
sample complexity of predicting the single-district winner under $r$. We
further extend our algorithm for the case when the margin of victory is
unknown, but we have only two candidates. We then consider the median voting
rule when the set of preferences in each district is single-peaked. We show
that the winner of a district-based election can be predicted with
$\mathcal{O}\left(\frac{1}{\varepsilon^4}\log\frac{1}{\varepsilon}\log\frac{1}{\delta}\right)$
samples even when the harmonious order in different districts can be different
and even unknown. Finally, we also show some results for estimating the margin
of victory of a district-based election within both additive and multiplicative
error bounds.
| [
{
"version": "v1",
"created": "Mon, 28 Feb 2022 20:32:48 GMT"
}
] | 1,646,179,200,000 | [
[
"Dey",
"Palash",
""
],
[
"Kar",
"Debajyoti",
""
],
[
"Sanyal",
"Swagato",
""
]
] |
2203.00183 | Zheng Yuan | Zheng Yuan, Tianhao Wu, Qinwen Wang, Yiying Yang, Lei Li, Lin Zhang | $ \text{T}^3 $OMVP: A Transformer-based Time and Team Reinforcement
Learning Scheme for Observation-constrained Multi-Vehicle Pursuit in Urban
Area | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI)
will contribute to vehicle decision-making in the Intelligent Transportation
System (ITS). Multi-Vehicle Pursuit games (MVP), a multi-vehicle cooperative
ability to capture mobile targets, is becoming a hot research topic gradually.
Although there are some achievements in the field of MVP in the open space
environment, the urban area brings complicated road structures and restricted
moving spaces as challenges to the resolution of MVP games. We define an
Observation-constrained MVP (OMVP) problem in this paper and propose a
Transformer-based Time and Team Reinforcement Learning scheme ($ \text{T}^3
$OMVP) to address the problem. First, a new multi-vehicle pursuit model is
constructed based on decentralized partially observed Markov decision processes
(Dec-POMDP) to instantiate this problem. Second, by introducing and modifying
the transformer-based observation sequence, QMIX is redefined to adapt to the
complicated road structure, restricted moving spaces and constrained
observations, so as to control vehicles to pursue the target combining the
vehicle's observations. Third, a multi-intersection urban environment is built
to verify the proposed scheme. Extensive experimental results demonstrate that
the proposed $ \text{T}^3 $OMVP scheme achieves significant improvements
relative to state-of-the-art QMIX approaches by 9.66%~106.25%. Code is
available at https://github.com/pipihaiziguai/T3OMVP.
| [
{
"version": "v1",
"created": "Tue, 1 Mar 2022 02:19:26 GMT"
},
{
"version": "v2",
"created": "Fri, 4 Mar 2022 02:52:39 GMT"
}
] | 1,646,611,200,000 | [
[
"Yuan",
"Zheng",
""
],
[
"Wu",
"Tianhao",
""
],
[
"Wang",
"Qinwen",
""
],
[
"Yang",
"Yiying",
""
],
[
"Li",
"Lei",
""
],
[
"Zhang",
"Lin",
""
]
] |
2203.00467 | Tim Ritmeester | Tim Ritmeester and Hildegard Meyer-Ortmanns | Belief propagation for supply networks: Efficient clustering of their
factor graphs | 19 pages, 9 figures | Eur. Phys. J. B 95, 89 (2022) | 10.1140/epjb/s10051-022-00336-7 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We consider belief propagation (BP) as an efficient and scalable tool for
state estimation and optimization problems in supply networks such as power
grids. BP algorithms make use of factor graph representations, whose assignment
to the problem of interest is not unique. It depends on the state variables and
their mutual interdependencies. Many short loops in factor graphs may impede
the accuracy of BP. We propose a systematic way to cluster loops of naively
assigned factor graphs such that the resulting transformed factor graphs have
no additional loops as compared to the original network. They guarantee an
accurate performance of BP with only slightly increased computational effort,
as we demonstrate by a concrete and realistic implementation for power grids.
The method outperforms existing alternatives to handle the loops. We point to
other applications to supply networks such as gas-pipeline or other flow
networks that share the structure of constraints in the form of analogues to
Kirchhoff's laws. Whenever small and abundant loops in factor graphs are
systematically generated by constraints between variables in the original
network, our factor-graph assignment in BP complements other approaches. It
provides a fast and reliable algorithm to perform marginalization in tasks like
state determination, estimation, or optimization issues in supply networks.
| [
{
"version": "v1",
"created": "Tue, 1 Mar 2022 14:01:35 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Apr 2022 10:10:19 GMT"
}
] | 1,654,732,800,000 | [
[
"Ritmeester",
"Tim",
""
],
[
"Meyer-Ortmanns",
"Hildegard",
""
]
] |
2203.00669 | Junkyu Lee | Junkyu Lee, Michael Katz, Don Joven Agravante, Miao Liu, Geraud Nangue
Tasse, Tim Klinger, Shirin Sohrabi | Hierarchical Reinforcement Learning with AI Planning Models | 30 pages, 15 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Two common approaches to sequential decision-making are AI planning (AIP) and
reinforcement learning (RL). Each has strengths and weaknesses. AIP is
interpretable, easy to integrate with symbolic knowledge, and often efficient,
but requires an up-front logical domain specification and is sensitive to
noise; RL only requires specification of rewards and is robust to noise but is
sample inefficient and not easily supplied with external knowledge. We propose
an integrative approach that combines high-level planning with RL, retaining
interpretability, transfer, and efficiency, while allowing for robust learning
of the lower-level plan actions. Our approach defines options in hierarchical
reinforcement learning (HRL) from AIP operators by establishing a
correspondence between the state transition model of AI planning problem and
the abstract state transition system of a Markov Decision Process (MDP).
Options are learned by adding intrinsic rewards to encourage consistency
between the MDP and AIP transition models. We demonstrate the benefit of our
integrated approach by comparing the performance of RL and HRL algorithms in
both MiniGrid and N-rooms environments, showing the advantage of our method
over the existing ones.
| [
{
"version": "v1",
"created": "Tue, 1 Mar 2022 18:38:41 GMT"
},
{
"version": "v2",
"created": "Wed, 28 Sep 2022 22:02:13 GMT"
}
] | 1,664,496,000,000 | [
[
"Lee",
"Junkyu",
""
],
[
"Katz",
"Michael",
""
],
[
"Agravante",
"Don Joven",
""
],
[
"Liu",
"Miao",
""
],
[
"Tasse",
"Geraud Nangue",
""
],
[
"Klinger",
"Tim",
""
],
[
"Sohrabi",
"Shirin",
""
]
] |
2203.00815 | Ola Alkhatib Ms. | Ayman Alahmar and Ola Alkhatib | Computerization of Clinical Pathways: A Literature Review and Directions
for Future Research | 12 pages, 4 figures, 3 tables | 2nd. International Symposium of Scientific Research and Innovative
Studies (ISSRIS'22), March 2-5, 2022 | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Clinical Pathways (CP) are medical management plans developed to standardize
patient treatment activities, optimize resource usage, reduce expenses, and
improve the quality of healthcare services. Most CPs currently in use are
paper-based documents (i.e., not computerized). CP computerization has been an
active research topic since the inception of CP use in hospitals. This
literature review research aims to examine studies that focused on CP
computerization and offers recommendations for future research in this
important research area. Some critical research suggestions include
centralizing computerized CPs in Healthcare Information Systems (HIS), CP term
standardization using international medical terminology systems, developing a
global CP-specific digital coding system, creating a unified CP meta-ontology,
developing independent Clinical Pathway Management Systems (CPMS), and
supporting CPMSs with machine learning sub-systems.
| [
{
"version": "v1",
"created": "Wed, 2 Mar 2022 01:38:40 GMT"
}
] | 1,646,265,600,000 | [
[
"Alahmar",
"Ayman",
""
],
[
"Alkhatib",
"Ola",
""
]
] |
2203.00905 | Qinghua Lu | Qinghua Lu, Liming Zhu, Xiwei Xu, Jon Whittle | Responsible-AI-by-Design: a Pattern Collection for Designing Responsible
AI Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although AI has significant potential to transform society, there are serious
concerns about its ability to behave and make decisions responsibly. Many
ethical regulations, principles, and guidelines for responsible AI have been
issued recently. However, these principles are high-level and difficult to put
into practice. In the meantime much effort has been put into responsible AI
from the algorithm perspective, but they are limited to a small subset of
ethical principles amenable to mathematical analysis. Responsible AI issues go
beyond data and algorithms and are often at the system-level crosscutting many
system components and the entire software engineering lifecycle. Based on the
result of a systematic literature review, this paper identifies one missing
element as the system-level guidance - how to design the architecture of
responsible AI systems. We present a summary of design patterns that can be
embedded into the AI systems as product features to contribute to
responsible-AI-by-design.
| [
{
"version": "v1",
"created": "Wed, 2 Mar 2022 07:30:03 GMT"
},
{
"version": "v2",
"created": "Tue, 20 Sep 2022 07:10:45 GMT"
}
] | 1,663,718,400,000 | [
[
"Lu",
"Qinghua",
""
],
[
"Zhu",
"Liming",
""
],
[
"Xu",
"Xiwei",
""
],
[
"Whittle",
"Jon",
""
]
] |
2203.00964 | Wen Zhang | Wen Zhang, Chi-Man Wong, Ganqinag Ye, Bo Wen, Hongting Zhou, Wei
Zhang, Huajun Chen | PKGM: A Pre-trained Knowledge Graph Model for E-commerce Application | This is an extension of work "Billion-scale Pre-trained E-commerce
Product Knowledge Graph Model" published at ICDE2021. We test PKGM on two
additional tasks, scene detection and sequential recommendation, and add
serving with item embeddings as one of the baseline. The extensive
experiments show the effectiveness of PKGM, pre-trained knowledge graph
model. arXiv admin note: text overlap with arXiv:2105.00388 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, knowledge graphs have been widely applied as a uniform way
to organize data and have enhanced many tasks requiring knowledge. In online
shopping platform Taobao, we built a billion-scale e-commerce product knowledge
graph. It organizes data uniformly and provides item knowledge services for
various tasks such as item recommendation. Usually, such knowledge services are
provided through triple data, while this implementation includes (1) tedious
data selection works on product knowledge graph and (2) task model designing
works to infuse those triples knowledge. More importantly, product knowledge
graph is far from complete, resulting error propagation to knowledge enhanced
tasks. To avoid these problems, we propose a Pre-trained Knowledge Graph Model
(PKGM) for the billion-scale product knowledge graph. On the one hand, it could
provide item knowledge services in a uniform way with service vectors for
embedding-based and item-knowledge-related task models without accessing triple
data. On the other hand, it's service is provided based on implicitly completed
product knowledge graph, overcoming the common the incomplete issue. We also
propose two general ways to integrate the service vectors from PKGM into
downstream task models. We test PKGM in five knowledge-related tasks, item
classification, item resolution, item recommendation, scene detection and
sequential recommendation. Experimental results show that PKGM introduces
significant performance gains on these tasks, illustrating the useful of
service vectors from PKGM.
| [
{
"version": "v1",
"created": "Wed, 2 Mar 2022 09:17:20 GMT"
}
] | 1,646,265,600,000 | [
[
"Zhang",
"Wen",
""
],
[
"Wong",
"Chi-Man",
""
],
[
"Ye",
"Ganqinag",
""
],
[
"Wen",
"Bo",
""
],
[
"Zhou",
"Hongting",
""
],
[
"Zhang",
"Wei",
""
],
[
"Chen",
"Huajun",
""
]
] |
2203.01024 | Carmine Dodaro | Carmine Dodaro, Marco Maratea, Mauro Vallati | On the Configuration of More and Less Expressive Logic Programs | Under consideration in Theory and Practice of Logic Programming
(TPLP) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The decoupling between the representation of a certain problem, i.e., its
knowledge model, and the reasoning side is one of main strong points of
model-based Artificial Intelligence (AI). This allows, e.g. to focus on
improving the reasoning side by having advantages on the whole solving process.
Further, it is also well-known that many solvers are very sensitive to even
syntactic changes in the input. In this paper, we focus on improving the
reasoning side by taking advantages of such sensitivity. We consider two
well-known model-based AI methodologies, SAT and ASP, define a number of
syntactic features that may characterise their inputs, and use automated
configuration tools to reformulate the input formula or program. Results of a
wide experimental analysis involving SAT and ASP domains, taken from respective
competitions, show the different advantages that can be obtained by using input
reformulation and configuration. Under consideration in Theory and Practice of
Logic Programming (TPLP).
| [
{
"version": "v1",
"created": "Wed, 2 Mar 2022 10:55:35 GMT"
}
] | 1,646,265,600,000 | [
[
"Dodaro",
"Carmine",
""
],
[
"Maratea",
"Marco",
""
],
[
"Vallati",
"Mauro",
""
]
] |
2203.01146 | Tianxing He | Jiabao Ji, Yoon Kim, James Glass, Tianxing He | Controlling the Focus of Pretrained Language Generation Models | null | ACL Findings 2022 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The finetuning of pretrained transformer-based language generation models are
typically conducted in an end-to-end manner, where the model learns to attend
to relevant parts of the input by itself. However, there does not exist a
mechanism to directly control the model's focus. This work aims to develop a
control mechanism by which a user can select spans of context as "highlights"
for the model to focus on, and generate relevant output. To achieve this goal,
we augment a pretrained model with trainable "focus vectors" that are directly
applied to the model's embeddings, while the model itself is kept fixed. These
vectors, trained on automatic annotations derived from attribution methods, act
as indicators for context importance. We test our approach on two core
generation tasks: dialogue response generation and abstractive summarization.
We also collect evaluation data where the highlight-generation pairs are
annotated by humans. Our experiments show that the trained focus vectors are
effective in steering the model to generate outputs that are relevant to
user-selected highlights.
| [
{
"version": "v1",
"created": "Wed, 2 Mar 2022 14:46:14 GMT"
}
] | 1,646,265,600,000 | [
[
"Ji",
"Jiabao",
""
],
[
"Kim",
"Yoon",
""
],
[
"Glass",
"James",
""
],
[
"He",
"Tianxing",
""
]
] |
2203.01201 | Bruno Yun | Nir Oren, Bruno Yun, Assaf Libman, Murilo S. Baptista | Analytical Solutions for the Inverse Problem within Gradual Semantics | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gradual semantics within abstract argumentation associate a numeric score
with every argument in a system, which represents the level of acceptability of
this argument, and from which a preference ordering over arguments can be
derived. While some semantics operate over standard argumentation frameworks,
many utilise a weighted framework, where a numeric initial weight is associated
with each argument. Recent work has examined the inverse problem within gradual
semantics. Rather than determining a preference ordering given an argumentation
framework and a semantics, the inverse problem takes an argumentation
framework, a gradual semantics, and a preference ordering as inputs, and
identifies what weights are needed to over arguments in the framework to obtain
the desired preference ordering. Existing work has attacked the inverse problem
numerically, using a root finding algorithm (the bisection method) to identify
appropriate initial weights. In this paper we demonstrate that for a class of
gradual semantics, an analytical approach can be used to solve the inverse
problem. Unlike the current state-of-the-art, such an analytic approach can
rapidly find a solution, and is guaranteed to do so. In obtaining this result,
we are able to prove several important properties which previous work had posed
as conjectures.
| [
{
"version": "v1",
"created": "Wed, 2 Mar 2022 15:55:10 GMT"
}
] | 1,646,265,600,000 | [
[
"Oren",
"Nir",
""
],
[
"Yun",
"Bruno",
""
],
[
"Libman",
"Assaf",
""
],
[
"Baptista",
"Murilo S.",
""
]
] |
2203.01310 | Yuanshun Yao | Yuanshun Yao and Chong Wang and Hang Li | Counterfactually Evaluating Explanations in Recommender Systems | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Modern recommender systems face an increasing need to explain their
recommendations. Despite considerable progress in this area, evaluating the
quality of explanations remains a significant challenge for researchers and
practitioners. Prior work mainly conducts human study to evaluate explanation
quality, which is usually expensive, time-consuming, and prone to human bias.
In this paper, we propose an offline evaluation method that can be computed
without human involvement. To evaluate an explanation, our method quantifies
its counterfactual impact on the recommendation. To validate the effectiveness
of our method, we carry out an online user study. We show that, compared to
conventional methods, our method can produce evaluation scores more correlated
with the real human judgments, and therefore can serve as a better proxy for
human evaluation. In addition, we show that explanations with high evaluation
scores are considered better by humans. Our findings highlight the promising
direction of using the counterfactual approach as one possible way to evaluate
recommendation explanations.
| [
{
"version": "v1",
"created": "Wed, 2 Mar 2022 18:55:29 GMT"
},
{
"version": "v2",
"created": "Thu, 17 Nov 2022 17:57:33 GMT"
}
] | 1,668,729,600,000 | [
[
"Yao",
"Yuanshun",
""
],
[
"Wang",
"Chong",
""
],
[
"Li",
"Hang",
""
]
] |
2203.01654 | Manu Lahariya | Manu Lahariya, Nasrin Sadeghianpourhamami and Chris Develder | Optimized cost function for demand response coordination of multiple EV
charging stations using reinforcement learning | null | Proceedings of the 6th ACM International Conference on Systems for
Energy-Efficient Buildings, Cities, and Transportation (BuildSys 19),
November 2019 Pages 344 345 | 10.1145/3360322.3360992 | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Electric vehicle (EV) charging stations represent a substantial load with
significant flexibility. The exploitation of that flexibility in demand
response (DR) algorithms becomes increasingly important to manage and balance
demand and supply in power grids. Model-free DR based on reinforcement learning
(RL) is an attractive approach to balance such EV charging load. We build on
previous research on RL, based on a Markov decision process (MDP) to
simultaneously coordinate multiple charging stations. However, we note that the
computationally expensive cost function adopted in the previous research leads
to large training times, which limits the feasibility and practicality of the
approach. We, therefore, propose an improved cost function that essentially
forces the learned control policy to always fulfill any charging demand that
does not offer any flexibility. We rigorously compare the newly proposed batch
RL fitted Q-iteration implementation with the original (costly) one, using
real-world data. Specifically, for the case of load flattening, we compare the
two approaches in terms of (i) the processing time to learn the RL-based
charging policy, as well as (ii) the overall performance of the policy
decisions in terms of meeting the target load for unseen test data. The
performance is analyzed for different training periods and varying training
sample sizes. In addition to both RL policies performance results, we provide
performance bounds in terms of both (i) an optimal all-knowing strategy, and
(ii) a simple heuristic spreading individual EV charging uniformly over time
| [
{
"version": "v1",
"created": "Thu, 3 Mar 2022 11:22:27 GMT"
}
] | 1,646,352,000,000 | [
[
"Lahariya",
"Manu",
""
],
[
"Sadeghianpourhamami",
"Nasrin",
""
],
[
"Develder",
"Chris",
""
]
] |
2203.01657 | Isabelle Hupont | Isabelle Hupont, Emilia Gomez, Songul Tolan, Lorenzo Porcaro, Ana
Freire | Monitoring Diversity of AI Conferences: Lessons Learnt and Future
Challenges in the DivinAI Project | 5 pages, 3 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | DivinAI is an open and collaborative initiative promoted by the European
Commission's Joint Research Centre to measure and monitor diversity indicators
related to AI conferences, with special focus on gender balance, geographical
representation, and presence of academia vs companies. This paper summarizes
the main achievements and lessons learnt during the first year of life of the
DivinAI project, and proposes a set of recommendations for its further
development and maintenance by the AI community.
| [
{
"version": "v1",
"created": "Thu, 3 Mar 2022 11:24:35 GMT"
}
] | 1,646,352,000,000 | [
[
"Hupont",
"Isabelle",
""
],
[
"Gomez",
"Emilia",
""
],
[
"Tolan",
"Songul",
""
],
[
"Porcaro",
"Lorenzo",
""
],
[
"Freire",
"Ana",
""
]
] |
2203.01895 | Pervaiz Khan | Pervaiz Iqbal Khan, Shoaib Ahmed Siddiqui, Imran Razzak, Andreas
Dengel, and Sheraz Ahmed | Improving Health Mentioning Classification of Tweets using Contrastive
Adversarial Training | null | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Health mentioning classification (HMC) classifies an input text as health
mention or not. Figurative and non-health mention of disease words makes the
classification task challenging. Learning the context of the input text is the
key to this problem. The idea is to learn word representation by its
surrounding words and utilize emojis in the text to help improve the
classification results. In this paper, we improve the word representation of
the input text using adversarial training that acts as a regularizer during
fine-tuning of the model. We generate adversarial examples by perturbing the
embeddings of the model and then train the model on a pair of clean and
adversarial examples. Additionally, we utilize contrastive loss that pushes a
pair of clean and perturbed examples close to each other and other examples
away in the representation space. We train and evaluate the method on an
extended version of the publicly available PHM2017 dataset. Experiments show an
improvement of 1.0% over BERT-Large baseline and 0.6% over RoBERTa-Large
baseline, whereas 5.8% over the state-of-the-art in terms of F1 score.
Furthermore, we provide a brief analysis of the results by utilizing the power
of explainable AI.
| [
{
"version": "v1",
"created": "Thu, 3 Mar 2022 18:20:51 GMT"
}
] | 1,646,352,000,000 | [
[
"Khan",
"Pervaiz Iqbal",
""
],
[
"Siddiqui",
"Shoaib Ahmed",
""
],
[
"Razzak",
"Imran",
""
],
[
"Dengel",
"Andreas",
""
],
[
"Ahmed",
"Sheraz",
""
]
] |
2203.02150 | Chengjin Xu | Chengjin Xu, Fenglong Su, Jens Lehmann | Time-aware Graph Neural Networks for Entity Alignment between Temporal
Knowledge Graphs | Accepted at EMNLP2021 | null | 10.18653/v1/2021.emnlp-main.709 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Entity alignment aims to identify equivalent entity pairs between different
knowledge graphs (KGs). Recently, the availability of temporal KGs (TKGs) that
contain time information created the need for reasoning over time in such TKGs.
Existing embedding-based entity alignment approaches disregard time information
that commonly exists in many large-scale KGs, leaving much room for
improvement. In this paper, we focus on the task of aligning entity pairs
between TKGs and propose a novel Time-aware Entity Alignment approach based on
Graph Neural Networks (TEA-GNN). We embed entities, relations and timestamps of
different KGs into a vector space and use GNNs to learn entity representations.
To incorporate both relation and time information into the GNN structure of our
model, we use a time-aware attention mechanism which assigns different weights
to different nodes with orthogonal transformation matrices computed from
embeddings of the relevant relations and timestamps in a neighborhood.
Experimental results on multiple real-world TKG datasets show that our method
significantly outperforms the state-of-the-art methods due to the inclusion of
time information.
| [
{
"version": "v1",
"created": "Fri, 4 Mar 2022 06:41:51 GMT"
},
{
"version": "v2",
"created": "Sun, 13 Mar 2022 14:57:43 GMT"
}
] | 1,647,302,400,000 | [
[
"Xu",
"Chengjin",
""
],
[
"Su",
"Fenglong",
""
],
[
"Lehmann",
"Jens",
""
]
] |
2203.02217 | Vyacheslav Yukalov | V.I. Yukalov | Quantification of emotions in decision making | Latex file, 33 pages | Soft Comput. 26 (2022) 2419-2436 | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The problem of quantification of emotions in the choice between alternatives
is considered. The alternatives are evaluated in a dual manner. From one side,
they are characterized by rational features defining the utility of each
alternative. From the other side, the choice is affected by emotions labeling
the alternatives as attractive or repulsive, pleasant or unpleasant. A decision
maker needs to make a choice taking into account both these features, the
utility of alternatives and their attractiveness. The notion of utility is
based on rational grounds, while the notion of attractiveness is vague and
rather is based on irrational feelings. A general method, allowing for the
quantification of the choice combining rational and emotional features is
described. Despite that emotions seem to avoid precise quantification, their
quantitative evaluation is possible at the aggregate level. The analysis of a
series of empirical data demonstrates the efficiency of the approach, including
the realistic behavioral problems that cannot be treated by the standard
expected utility theory.
| [
{
"version": "v1",
"created": "Fri, 4 Mar 2022 09:56:39 GMT"
}
] | 1,646,611,200,000 | [
[
"Yukalov",
"V. I.",
""
]
] |
2203.02696 | Nadjib Lazaar Dr | Nassim Belmecheri and Noureddine Aribi and Nadjib Lazaar and Yahia
Lebbah and Samir Loudni | Boosting the Learning for Ranking Patterns | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Discovering relevant patterns for a particular user remains a challenging
tasks in data mining. Several approaches have been proposed to learn
user-specific pattern ranking functions. These approaches generalize well, but
at the expense of the running time. On the other hand, several measures are
often used to evaluate the interestingness of patterns, with the hope to reveal
a ranking that is as close as possible to the user-specific ranking. In this
paper, we formulate the problem of learning pattern ranking functions as a
multicriteria decision making problem. Our approach aggregates different
interestingness measures into a single weighted linear ranking function, using
an interactive learning procedure that operates in either passive or active
modes. A fast learning step is used for eliciting the weights of all the
measures by mean of pairwise comparisons.
This approach is based on Analytic Hierarchy Process (AHP), and a set of
user-ranked patterns to build a preference matrix, which compares the
importance of measures according to the user-specific interestingness. A
sensitivity based heuristic is proposed for the active learning mode, in order
to insure high quality results with few user ranking queries. Experiments
conducted on well-known datasets show that our approach significantly reduces
the running time and returns precise pattern ranking, while being robust to
user-error compared with state-of-the-art approaches.
| [
{
"version": "v1",
"created": "Sat, 5 Mar 2022 10:22:44 GMT"
}
] | 1,646,697,600,000 | [
[
"Belmecheri",
"Nassim",
""
],
[
"Aribi",
"Noureddine",
""
],
[
"Lazaar",
"Nadjib",
""
],
[
"Lebbah",
"Yahia",
""
],
[
"Loudni",
"Samir",
""
]
] |
2203.02878 | Jiayi Zhang | Jiayi Zhang and Chang Liu and Junchi Yan and Xijun Li and Hui-Ling
Zhen and Mingxuan Yuan | A Survey for Solving Mixed Integer Programming via Machine Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper surveys the trend of leveraging machine learning to solve mixed
integer programming (MIP) problems. Theoretically, MIP is an NP-hard problem,
and most of the combinatorial optimization (CO) problems can be formulated as
the MIP. Like other CO problems, the human-designed heuristic algorithms for
MIP rely on good initial solutions and cost a lot of computational resources.
Therefore, we consider applying machine learning methods to solve MIP, since
ML-enhanced approaches can provide the solution based on the typical patterns
from the historical data. In this paper, we first introduce the formulation and
preliminaries of MIP and several traditional algorithms to solve MIP. Then, we
advocate further promoting the different integration of machine learning and
MIP and introducing related learning-based methods, which can be classified
into exact algorithms and heuristic algorithms. Finally, we propose the outlook
for learning-based MIP solvers, direction towards more combinatorial
optimization problems beyond MIP, and also the mutual embrace of traditional
solvers and machine learning components.
| [
{
"version": "v1",
"created": "Sun, 6 Mar 2022 05:03:37 GMT"
}
] | 1,646,697,600,000 | [
[
"Zhang",
"Jiayi",
""
],
[
"Liu",
"Chang",
""
],
[
"Yan",
"Junchi",
""
],
[
"Li",
"Xijun",
""
],
[
"Zhen",
"Hui-Ling",
""
],
[
"Yuan",
"Mingxuan",
""
]
] |
2203.03153 | Haoze Wu | Haoze Wu, Clark Barrett, Mahmood Sharif, Nina Narodytska, Gagandeep
Singh | Scalable Verification of GNN-based Job Schedulers | Condensed version published at OOPSLA'22 | null | 10.1145/3563325 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recently, Graph Neural Networks (GNNs) have been applied for scheduling jobs
over clusters, achieving better performance than hand-crafted heuristics.
Despite their impressive performance, concerns remain over whether these
GNN-based job schedulers meet users' expectations about other important
properties, such as strategy-proofness, sharing incentive, and stability. In
this work, we consider formal verification of GNN-based job schedulers. We
address several domain-specific challenges such as networks that are deeper and
specifications that are richer than those encountered when verifying image and
NLP classifiers. We develop vegas, the first general framework for verifying
both single-step and multi-step properties of these schedulers based on
carefully designed algorithms that combine abstractions, refinements, solvers,
and proof transfer. Our experimental results show that vegas achieves
significant speed-up when verifying important properties of a state-of-the-art
GNN-based scheduler compared to previous methods.
| [
{
"version": "v1",
"created": "Mon, 7 Mar 2022 06:13:04 GMT"
},
{
"version": "v2",
"created": "Tue, 19 Apr 2022 00:49:10 GMT"
},
{
"version": "v3",
"created": "Tue, 7 Jun 2022 23:45:32 GMT"
},
{
"version": "v4",
"created": "Thu, 15 Sep 2022 18:16:40 GMT"
}
] | 1,663,545,600,000 | [
[
"Wu",
"Haoze",
""
],
[
"Barrett",
"Clark",
""
],
[
"Sharif",
"Mahmood",
""
],
[
"Narodytska",
"Nina",
""
],
[
"Singh",
"Gagandeep",
""
]
] |
2203.03183 | Zehao Wang | Zehao Wang, Mingxiao Li, Minye Wu, Marie-Francine Moens, Tinne
Tuytelaars | Find a Way Forward: a Language-Guided Semantic Map Navigator | content revised | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this paper, we introduce the map-language navigation task where an agent
executes natural language instructions and moves to the target position based
only on a given 3D semantic map. To tackle the task, we design the
instruction-aware Path Proposal and Discrimination model (iPPD). Our approach
leverages map information to provide instruction-aware path proposals, i.e., it
selects all potential instruction-aligned candidate paths to reduce the
solution space. Next, to represent the map observations along a path for a
better modality alignment, a novel Path Feature Encoding scheme tailored for
semantic maps is proposed. An attention-based Language Driven Discriminator is
designed to evaluate path candidates and determine the best path as the final
result. Our method can naturally avoid error accumulation compared with
single-step greedy decision methods. Comparing to a single-step imitation
learning approach, iPPD has performance gains above 17% on navigation success
and 0.18 on path matching measurement nDTW in challenging unseen environments.
| [
{
"version": "v1",
"created": "Mon, 7 Mar 2022 07:40:33 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Sep 2022 06:31:47 GMT"
}
] | 1,664,236,800,000 | [
[
"Wang",
"Zehao",
""
],
[
"Li",
"Mingxiao",
""
],
[
"Wu",
"Minye",
""
],
[
"Moens",
"Marie-Francine",
""
],
[
"Tuytelaars",
"Tinne",
""
]
] |
2203.03315 | Lingbing Guo | Lingbing Guo and Yuqiang Han and Qiang Zhang and Huajun Chen | Deep Reinforcement Learning for Entity Alignment | Findings of ACL | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Embedding-based methods have attracted increasing attention in recent entity
alignment (EA) studies. Although great promise they can offer, there are still
several limitations. The most notable is that they identify the aligned
entities based on cosine similarity, ignoring the semantics underlying the
embeddings themselves. Furthermore, these methods are shortsighted,
heuristically selecting the closest entity as the target and allowing multiple
entities to match the same candidate. To address these limitations, we model
entity alignment as a sequential decision-making task, in which an agent
sequentially decides whether two entities are matched or mismatched based on
their representation vectors. The proposed reinforcement learning (RL)-based
entity alignment framework can be flexibly adapted to most embedding-based EA
methods. The experimental results demonstrate that it consistently advances the
performance of several state-of-the-art methods, with a maximum improvement of
31.1% on Hits@1.
| [
{
"version": "v1",
"created": "Mon, 7 Mar 2022 11:49:40 GMT"
}
] | 1,646,697,600,000 | [
[
"Guo",
"Lingbing",
""
],
[
"Han",
"Yuqiang",
""
],
[
"Zhang",
"Qiang",
""
],
[
"Chen",
"Huajun",
""
]
] |
2203.03344 | Yat Long Lo | Yat Long Lo and Biswa Sengupta | Learning to Ground Decentralized Multi-Agent Communication with
Contrastive Learning | null | EmeCom at ICLR 2022 | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | For communication to happen successfully, a common language is required
between agents to understand information communicated by one another. Inducing
the emergence of a common language has been a difficult challenge to
multi-agent learning systems. In this work, we introduce an alternative
perspective to the communicative messages sent between agents, considering them
as different incomplete views of the environment state. Based on this
perspective, we propose a simple approach to induce the emergence of a common
language by maximizing the mutual information between messages of a given
trajectory in a self-supervised manner. By evaluating our method in
communication-essential environments, we empirically show how our method leads
to better learning performance and speed, and learns a more consistent common
language than existing methods, without introducing additional learning
parameters.
| [
{
"version": "v1",
"created": "Mon, 7 Mar 2022 12:41:32 GMT"
}
] | 1,651,536,000,000 | [
[
"Lo",
"Yat Long",
""
],
[
"Sengupta",
"Biswa",
""
]
] |
2203.03485 | Dustin Dannenhauer | Dustin Dannenhauer, Matthew Molineaux, Michael W. Floyd, Noah
Reifsnyder, David W. Aha | Self-directed Learning of Action Models using Exploratory Planning | Presented at The Ninth Advances in Cognitive Systems (ACS) Conference
2021 (arXiv:2201.06134) | null | null | ACS2021/29 | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Complex, real-world domains may not be fully modeled for an agent, especially
if the agent has never operated in the domain before. The agent's ability to
effectively plan and act in such a domain is influenced by its knowledge of
when it can perform specific actions and the effects of those actions. We
describe a novel exploratory planning agent that is capable of learning action
preconditions and effects without expert traces or a given goal. The agent's
architecture allows it to perform both exploratory actions as well as
goal-directed actions, which opens up important considerations for how
exploratory planning and goal planning should be controlled, as well as how the
agent's behavior should be explained to any teammates it may have. The
contributions of this work include a new representation for contexts called
Lifted Linked Clauses, a novel exploration action selection approach using
these clauses, an exploration planner that uses lifted linked clauses as goals
in order to reach new states, and an empirical evaluation in a scenario from an
exploration-focused video game demonstrating that lifted linked clauses improve
exploration and action model learning against non-planning baseline agents.
| [
{
"version": "v1",
"created": "Mon, 7 Mar 2022 15:57:10 GMT"
}
] | 1,646,697,600,000 | [
[
"Dannenhauer",
"Dustin",
""
],
[
"Molineaux",
"Matthew",
""
],
[
"Floyd",
"Michael W.",
""
],
[
"Reifsnyder",
"Noah",
""
],
[
"Aha",
"David W.",
""
]
] |
2203.04363 | Mohamed El Yafrani | Mohamed El Yafrani, Marcella Scoczynski, Myriam Delgado, Ricardo
L\"uders, Peter Nielsen, Markus Wagner | On the Fitness Landscapes of Interdependency Models in the Travelling
Thief Problem | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Since its inception in 2013, the Travelling Thief Problem (TTP) has been
widely studied as an example of problems with multiple interconnected
sub-problems. The dependency in this model arises when tying the travelling
time of the "thief" to the weight of the knapsack. However, other forms of
dependency as well as combinations of dependencies should be considered for
investigation, as they are often found in complex real-world problems. Our goal
is to study the impact of different forms of dependency in the TTP using a
simple local search algorithm. To achieve this, we use Local Optima Networks, a
technique for analysing the fitness landscape.
| [
{
"version": "v1",
"created": "Mon, 28 Feb 2022 13:26:42 GMT"
}
] | 1,646,870,400,000 | [
[
"Yafrani",
"Mohamed El",
""
],
[
"Scoczynski",
"Marcella",
""
],
[
"Delgado",
"Myriam",
""
],
[
"Lüders",
"Ricardo",
""
],
[
"Nielsen",
"Peter",
""
],
[
"Wagner",
"Markus",
""
]
] |
2203.04699 | Boris Shminke | Boris Shminke | Gym-saturation: an OpenAI Gym environment for saturation provers | 6 pages, 1 figure | Journal of Open Source Software, 7(71), 3849, 2022 | 10.21105/joss.03849 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | `gym-saturation` is an OpenAI Gym environment for reinforcement learning (RL)
agents capable of proving theorems. Currently, only theorems written in a
formal language of the Thousands of Problems for Theorem Provers (TPTP) library
in clausal normal form (CNF) are supported. `gym-saturation` implements the
'given clause' algorithm (similar to the one used in Vampire and E Prover).
Being written in Python, `gym-saturation` was inspired by PyRes. In contrast to
the monolithic architecture of a typical Automated Theorem Prover (ATP),
`gym-saturation` gives different agents opportunities to select clauses
themselves and train from their experience. Combined with a particular agent,
`gym-saturation` can work as an ATP. Even with a non trained agent based on
heuristics, `gym-saturation` can find refutations for 688 (of 8257) CNF
problems from TPTP v7.5.0.
| [
{
"version": "v1",
"created": "Wed, 9 Mar 2022 13:22:15 GMT"
}
] | 1,646,870,400,000 | [
[
"Shminke",
"Boris",
""
]
] |
2203.04702 | Jingxuan Chai | Jingxuan Chai and Guangming Shi | ModulE: Module Embedding for Knowledge Graphs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge graph embedding (KGE) has been shown to be a powerful tool for
predicting missing links of a knowledge graph. However, existing methods mainly
focus on modeling relation patterns, while simply embed entities to vector
spaces, such as real field, complex field and quaternion space. To model the
embedding space from a more rigorous and theoretical perspective, we propose a
novel general group theory-based embedding framework for rotation-based models,
in which both entities and relations are embedded as group elements.
Furthermore, in order to explore more available KGE models, we utilize a more
generic group structure, module, a generalization notion of vector space.
Specifically, under our framework, we introduce a more generic embedding
method, ModulE, which projects entities to a module. Following the method of
ModulE, we build three instantiating models: ModulE$_{\mathbb{R},\mathbb{C}}$,
ModulE$_{\mathbb{R},\mathbb{H}}$ and ModulE$_{\mathbb{H},\mathbb{H}}$, by
adopting different module structures. Experimental results show that
ModulE$_{\mathbb{H},\mathbb{H}}$ which embeds entities to a module over
non-commutative ring, achieves state-of-the-art performance on multiple
benchmark datasets.
| [
{
"version": "v1",
"created": "Wed, 9 Mar 2022 13:27:46 GMT"
}
] | 1,646,870,400,000 | [
[
"Chai",
"Jingxuan",
""
],
[
"Shi",
"Guangming",
""
]
] |
2203.05057 | Colan Biemer | Colan Biemer and Seth Cooper | On Linking Level Segments | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An increasingly common area of study in procedural content generation is the
creation of level segments: short pieces that can be used to form larger
levels. Previous work has used basic concatenation to form these larger levels.
However, even if the segments themselves are completable and well-formed,
concatenation can fail to produce levels that are completable and can cause
broken in-game structures (e.g. malformed pipes in Mario). We show this with
three tile-based games: a side-scrolling platformer, a vertical platformer, and
a top-down roguelike. Additionally, we present a Markov chain and a tree search
algorithm that finds a link between two level segments, which uses filters to
ensure completability and unbroken in-game structures in the linked segments.
We further show that these links work well for multi-segment levels. We find
that this method reliably finds links between segments and is customizable to
meet a designer's needs.
| [
{
"version": "v1",
"created": "Wed, 9 Mar 2022 21:32:41 GMT"
},
{
"version": "v2",
"created": "Mon, 22 Aug 2022 13:33:55 GMT"
}
] | 1,661,212,800,000 | [
[
"Biemer",
"Colan",
""
],
[
"Cooper",
"Seth",
""
]
] |
2203.07302 | Valerio Biscione | Valerio Biscione, Jeffrey S. Bowers | Mixed Evidence for Gestalt Grouping in Deep Neural Networks | Accepted in Computational Brain & Behaviour | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Gestalt psychologists have identified a range of conditions in which humans
organize elements of a scene into a group or whole, and perceptual grouping
principles play an essential role in scene perception and object
identification. Recently, Deep Neural Networks (DNNs) trained on natural images
(ImageNet) have been proposed as compelling models of human vision based on
reports that they perform well on various brain and behavioral benchmarks. Here
we test a total of 16 networks covering a variety of architectures and learning
paradigms (convolutional, attention-based, supervised and self-supervised,
feed-forward and recurrent) on dots (Experiment 1) and more complex shapes
(Experiment 2) stimuli that produce strong Gestalts effects in humans. In
Experiment 1 we found that convolutional networks were indeed sensitive in a
human-like fashion to the principles of proximity, linearity, and orientation,
but only at the output layer. In Experiment 2, we found that most networks
exhibited Gestalt effects only for a few sets, and again only at the latest
stage of processing. Overall, self-supervised and Vision-Transformer appeared
to perform worse than convolutional networks in terms of human similarity.
Remarkably, no model presented a grouping effect at the early or intermediate
stages of processing. This is at odds with the widespread assumption that
Gestalts occur prior to object recognition, and indeed, serve to organize the
visual scene for the sake of object recognition. Our overall conclusion is
that, albeit noteworthy that networks trained on simple 2D images support a
form of Gestalt grouping for some stimuli at the output layer, this ability
does not seem to transfer to more complex features. Additionally, the fact that
this grouping only occurs at the last layer suggests that networks learn
fundamentally different perceptual properties than humans.
| [
{
"version": "v1",
"created": "Mon, 14 Mar 2022 17:06:11 GMT"
},
{
"version": "v2",
"created": "Wed, 6 Apr 2022 07:38:50 GMT"
},
{
"version": "v3",
"created": "Mon, 20 Feb 2023 10:57:46 GMT"
}
] | 1,676,937,600,000 | [
[
"Biscione",
"Valerio",
""
],
[
"Bowers",
"Jeffrey S.",
""
]
] |
2203.07507 | Izack Cohen | Eli Bogdanov, Izack Cohen, Avigdor Gal | Conformance Checking Over Stochastically Known Logs | null | In International Conference on Business Process Management (pp.
105-119). Cham: Springer International Publishing (2022) | 10.1007/978-3-031-16171-1_7 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the growing number of devices, sensors and digital systems, data logs
may become uncertain due to, e.g., sensor reading inaccuracies or incorrect
interpretation of readings by processing programs. At times, such uncertainties
can be captured stochastically, especially when using probabilistic data
classification models. In this work we focus on conformance checking, which
compares a process model with an event log, when event logs are stochastically
known. Building on existing alignment-based conformance checking fundamentals,
we mathematically define a stochastic trace model, a stochastic synchronous
product, and a cost function that reflects the uncertainty of events in a log.
Then, we search for an optimal alignment over the reachability graph of the
stochastic synchronous product for finding an optimal alignment between a model
and a stochastic process observation. Via structured experiments with two
well-known process mining benchmarks, we explore the behavior of the suggested
stochastic conformance checking approach and compare it to a standard
alignment-based approach as well as to an approach that creates a lower bound
on performance. We envision the proposed stochastic conformance checking
approach as a viable process mining component for future analysis of stochastic
event logs.
| [
{
"version": "v1",
"created": "Mon, 14 Mar 2022 21:33:06 GMT"
}
] | 1,700,611,200,000 | [
[
"Bogdanov",
"Eli",
""
],
[
"Cohen",
"Izack",
""
],
[
"Gal",
"Avigdor",
""
]
] |
2203.07782 | Zixuan Li | Zixuan Li, Saiping Guan, Xiaolong Jin, Weihua Peng, Yajuan Lyu, Yong
Zhu, Long Bai, Wei Li, Jiafeng Guo and Xueqi Cheng | Complex Evolutional Pattern Learning for Temporal Knowledge Graph
Reasoning | ACL 2022 main conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A Temporal Knowledge Graph (TKG) is a sequence of KGs corresponding to
different timestamps. TKG reasoning aims to predict potential facts in the
future given the historical KG sequences. One key of this task is to mine and
understand evolutional patterns of facts from these sequences. The evolutional
patterns are complex in two aspects, length-diversity and time-variability.
Existing models for TKG reasoning focus on modeling fact sequences of a fixed
length, which cannot discover complex evolutional patterns that vary in length.
Furthermore, these models are all trained offline, which cannot well adapt to
the changes of evolutional patterns from then on. Thus, we propose a new model,
called Complex Evolutional Network (CEN), which uses a length-aware
Convolutional Neural Network (CNN) to handle evolutional patterns of different
lengths via an easy-to-difficult curriculum learning strategy. Besides, we
propose to learn the model under the online setting so that it can adapt to the
changes of evolutional patterns over time. Extensive experiments demonstrate
that CEN obtains substantial performance improvement under both the traditional
offline and the proposed online settings.
| [
{
"version": "v1",
"created": "Tue, 15 Mar 2022 11:02:55 GMT"
},
{
"version": "v2",
"created": "Sun, 20 Mar 2022 11:39:19 GMT"
}
] | 1,647,907,200,000 | [
[
"Li",
"Zixuan",
""
],
[
"Guan",
"Saiping",
""
],
[
"Jin",
"Xiaolong",
""
],
[
"Peng",
"Weihua",
""
],
[
"Lyu",
"Yajuan",
""
],
[
"Zhu",
"Yong",
""
],
[
"Bai",
"Long",
""
],
[
"Li",
"Wei",
""
],
[
"Guo",
"Jiafeng",
""
],
[
"Cheng",
"Xueqi",
""
]
] |
2203.07993 | Kai Chen | Kai Chen, Ye Wang, Yitong Li and Aiping Li | RotateQVS: Representing Temporal Information as Rotations in Quaternion
Vector Space for Temporal Knowledge Graph Completion | To appear in ACL 2022 main conference | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Temporal factors are tied to the growth of facts in realistic applications,
such as the progress of diseases and the development of political situation,
therefore, research on Temporal Knowledge Graph (TKG) attracks much attention.
In TKG, relation patterns inherent with temporality are required to be studied
for representation learning and reasoning across temporal facts. However,
existing methods can hardly model temporal relation patterns, nor can capture
the intrinsic connections between relations when evolving over time, lacking of
interpretability. In this paper, we propose a novel temporal modeling method
which represents temporal entities as Rotations in Quaternion Vector Space
(RotateQVS) and relations as complex vectors in Hamilton's quaternion space. We
demonstrate our method can model key patterns of relations in TKG, such as
symmetry, asymmetry, inverse, and can further capture time-evolved relations by
theory. Empirically, we show that our method can boost the performance of link
prediction tasks over four temporal knowledge graph benchmarks.
| [
{
"version": "v1",
"created": "Tue, 15 Mar 2022 15:27:23 GMT"
},
{
"version": "v2",
"created": "Thu, 17 Mar 2022 03:31:46 GMT"
}
] | 1,647,561,600,000 | [
[
"Chen",
"Kai",
""
],
[
"Wang",
"Ye",
""
],
[
"Li",
"Yitong",
""
],
[
"Li",
"Aiping",
""
]
] |
2203.08146 | Jin Xie | Jin Xie, Teng Zhang, Jose Blanchet, Peter Glynn, Matthew Randolph,
David Scheinker | The Design and Implementation of a Broadly Applicable Algorithm for
Optimizing Intra-Day Surgical Scheduling | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Surgical scheduling optimization is an active area of research. However, few
algorithms to optimize surgical scheduling are implemented and see sustained
use. An algorithm is more likely to be implemented, if it allows for surgeon
autonomy, i.e., requires only limited scheduling centralization, and functions
in the limited technical infrastructure of widely used electronic medical
records (EMRs). In order for an algorithm to see sustained use, it must be
compatible with changes to hospital capacity, patient volumes, and scheduling
practices. To meet these objectives, we developed the BEDS (better elective day
of surgery) algorithm, a greedy heuristic for smoothing unit-specific surgical
admissions across days. We implemented BEDS in the EMR of a large pediatric
academic medical center.
The use of BEDS was associated with a reduction in the variability in the
number of admissions. BEDS is freely available as a dashboard in Tableau, a
commercial software used by numerous hospitals. BEDS is readily implementable
with the limited tools available to most hospitals, does not require reductions
to surgeon autonomy or centralized scheduling, and is compatible with changes
to hospital capacity or patient volumes. We present a general algorithmic
framework from which BEDS is derived based on a particular choice of objectives
and constraints. We argue that algorithms generated by this framework retain
many of the desirable characteristics of BEDS while being compatible with a
wide range of objectives and constraints.
| [
{
"version": "v1",
"created": "Mon, 14 Mar 2022 04:19:25 GMT"
}
] | 1,647,475,200,000 | [
[
"Xie",
"Jin",
""
],
[
"Zhang",
"Teng",
""
],
[
"Blanchet",
"Jose",
""
],
[
"Glynn",
"Peter",
""
],
[
"Randolph",
"Matthew",
""
],
[
"Scheinker",
"David",
""
]
] |
2203.08895 | Parisa Zehtabi | Alberto Pozanco, Francesca Mosca, Parisa Zehtabi, Daniele Magazzeni,
Sarit Kraus | Explaining Preference-driven Schedules: the EXPRES Framework | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Scheduling is the task of assigning a set of scarce resources distributed
over time to a set of agents, who typically have preferences about the
assignments they would like to get. Due to the constrained nature of these
problems, satisfying all agents' preferences is often infeasible, which might
lead to some agents not being happy with the resulting schedule. Providing
explanations has been shown to increase satisfaction and trust in solutions
produced by AI tools. However, it is particularly challenging to explain
solutions that are influenced by and impact on multiple agents. In this paper
we introduce the EXPRES framework, which can explain why a given preference was
unsatisfied in a given optimal schedule. The EXPRES framework consists of: (i)
an explanation generator that, based on a Mixed-Integer Linear Programming
model, finds the best set of reasons that can explain an unsatisfied
preference; and (ii) an explanation parser, which translates the generated
explanations into human interpretable ones. Through simulations, we show that
the explanation generator can efficiently scale to large instances. Finally,
through a set of user studies within J.P. Morgan, we show that employees
preferred the explanations generated by EXPRES over human-generated ones when
considering workforce scheduling scenarios.
| [
{
"version": "v1",
"created": "Wed, 16 Mar 2022 19:15:21 GMT"
}
] | 1,647,561,600,000 | [
[
"Pozanco",
"Alberto",
""
],
[
"Mosca",
"Francesca",
""
],
[
"Zehtabi",
"Parisa",
""
],
[
"Magazzeni",
"Daniele",
""
],
[
"Kraus",
"Sarit",
""
]
] |
2203.09926 | Yunuo Cen | Yunuo Cen, Debasis Das, Xuanyao Fong | CITS: Coherent Ising Tree Search Algorithm Towards Solving Combinatorial
Optimization Problems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Simulated annealing (SA) attracts more attention among classical heuristic
algorithms because the solution of the combinatorial optimization problem can
be naturally mapped to the ground state of the Ising Hamiltonian. However, in
practical implementation, the annealing process cannot be arbitrarily slow and
hence, it may deviate from the expected stationary Boltzmann distribution and
become trapped in a local energy minimum. To overcome this problem, this paper
proposes a heuristic search algorithm by expanding search space from a Markov
chain to a recursive depth limited tree based on SA, where the parent and child
nodes represent the current and future spin states. At each iteration, the
algorithm will select the best near-optimal solution within the feasible search
space by exploring along the tree in the sense of `look ahead'. Furthermore,
motivated by coherent Ising machine (CIM), we relax the discrete representation
of spin states to continuous representation with a regularization term and
utilize the reduced dynamics of the oscillators to explore the surrounding
neighborhood of the selected tree nodes. We tested our algorithm on a
representative NP-hard problem (MAX-CUT) to illustrate the effectiveness of
this algorithm compared to semi-definite programming (SDP), SA, and simulated
CIM. Our results show that above the primal heuristics SA and CIM, our
high-level tree search strategy is able to provide solutions within fewer
epochs for Ising formulated NP-optimization problems.
| [
{
"version": "v1",
"created": "Wed, 9 Mar 2022 10:07:26 GMT"
}
] | 1,648,425,600,000 | [
[
"Cen",
"Yunuo",
""
],
[
"Das",
"Debasis",
""
],
[
"Fong",
"Xuanyao",
""
]
] |
2203.09952 | Kefan Jin | Kefan Jin, Xingyao Han | Conquering Ghosts: Relation Learning for Information Reliability
Representation and End-to-End Robust Navigation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Environmental disturbances, such as sensor data noises, various lighting
conditions, challenging weathers and external adversarial perturbations, are
inevitable in real self-driving applications. Existing researches and testings
have shown that they can severely influence the vehicles perception ability and
performance, one of the main issue is the false positive detection, i.e., the
ghost object which is not real existed or occurs in the wrong position (such as
a non-existent vehicle). Traditional navigation methods tend to avoid every
detected objects for safety, however, avoiding a ghost object may lead the
vehicle into a even more dangerous situation, such as a sudden break on the
highway. Considering the various disturbance types, it is difficult to address
this issue at the perceptual aspect. A potential solution is to detect the
ghost through relation learning among the whole scenario and develop an
integrated end-to-end navigation system. Our underlying logic is that the
behavior of all vehicles in the scene is influenced by their neighbors, and
normal vehicles behave in a logical way, while ghost vehicles do not. By
learning the spatio-temporal relation among surrounding vehicles, an
information reliability representation is learned for each detected vehicle and
then a robot navigation network is developed. In contrast to existing works, we
encourage the network to learn how to represent the reliability and how to
aggregate all the information with uncertainties by itself, thus increasing the
efficiency and generalizability. To the best of the authors knowledge, this
paper provides the first work on using graph relation learning to achieve
end-to-end robust navigation in the presence of ghost vehicles. Simulation
results in the CARLA platform demonstrate the feasibility and effectiveness of
the proposed method in various scenarios.
| [
{
"version": "v1",
"created": "Mon, 14 Mar 2022 14:11:12 GMT"
},
{
"version": "v2",
"created": "Fri, 30 Dec 2022 12:10:56 GMT"
},
{
"version": "v3",
"created": "Mon, 20 Feb 2023 12:29:42 GMT"
}
] | 1,676,937,600,000 | [
[
"Jin",
"Kefan",
""
],
[
"Han",
"Xingyao",
""
]
] |
2203.10145 | Maryam Tavakoli-Zaniani | Maryam Tavakoli-Zaniani, Mohammad Reza Gholamian and S. Alireza
Hashemi Golpayegani | Improving Heuristic-based Process Discovery Methods by Detecting Optimal
Dependency Graphs | Prepared to sumit to Fundamenta Informaticae journal | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Heuristic-based methods are among the most popular methods in the process
discovery area. This category of methods is composed of two main steps: 1)
discovering a dependency graph 2) determining the split/join patterns of the
dependency graph. The current dependency graph discovery techniques of
heuristic-based methods select the initial set of graph arcs according to
dependency measures and then modify the set regarding some criteria. This can
lead to selecting the non-optimal set of arcs. Also, the modifications can
result in modeling rare behaviors and, consequently, low precision and
non-simple process models. Thus, constructing dependency graphs through
selecting the optimal set of arcs has a high potential for improving graphs
quality. Hence, this paper proposes a new integer linear programming model that
determines the optimal set of graph arcs regarding dependency measures.
Simultaneously, the proposed method can eliminate some other issues that the
existing methods cannot handle completely; i.e., even in the presence of loops,
it guarantees that all tasks are on a path from the initial to the final tasks.
This approach also allows utilizing domain knowledge by introducing appropriate
constraints, which can be a practical advantage in real-world problems. To
assess the results, we modified two existing methods of evaluating process
models to make them capable of measuring the quality of dependency graphs.
According to assessments, the outputs of the proposed method are superior to
the outputs of the most prominent dependency graph discovery methods in terms
of fitness, precision, and especially simplicity.
| [
{
"version": "v1",
"created": "Fri, 18 Mar 2022 20:00:23 GMT"
}
] | 1,647,907,200,000 | [
[
"Tavakoli-Zaniani",
"Maryam",
""
],
[
"Gholamian",
"Mohammad Reza",
""
],
[
"Golpayegani",
"S. Alireza Hashemi",
""
]
] |
2203.10540 | David Vainshtein | David Vainshtein, Kiril Solovey, Oren Salzman | Multi-Agent Terraforming: Efficient Multi-Agent Path Finding via
Environment Manipulation | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Multi-agent pathfinding (MAPF) is concerned with planning collision-free
paths for a team of agents from their start to goal locations in an environment
cluttered with obstacles. Typical approaches for MAPF consider the locations of
obstacles as being fixed, which limits their effectiveness in automated
warehouses, where obstacles (representing pods or shelves) can be moved out of
the way by agents (representing robots) to relieve bottlenecks and introduce
shorter routes. In this work we initiate the study of MAPF with movable
obstacles. In particular, we introduce a new extension of MAPF, which we call
Terraforming MAPF (tMAPF), where some agents are responsible for moving
obstacles to clear the way for other agents. Solving tMAPF is extremely
challenging as it requires reasoning not only about collisions between agents,
but also where and when obstacles should be moved. We present extensions of two
state-of-the-art algorithms, CBS and PBS, in order to tackle tMAPF, and
demonstrate that they can consistently outperform the best solution possible
under a static-obstacle setting.
| [
{
"version": "v1",
"created": "Sun, 20 Mar 2022 12:18:35 GMT"
}
] | 1,647,907,200,000 | [
[
"Vainshtein",
"David",
""
],
[
"Solovey",
"Kiril",
""
],
[
"Salzman",
"Oren",
""
]
] |
2203.10794 | Jo\v{z}e Ro\v{z}anec | Jo\v{z}e M. Ro\v{z}anec, Inna Novalija, Patrik Zajec, Klemen Kenda,
Hooman Tavakoli, Sungho Suh, Entso Veliou, Dimitrios Papamartzivanos,
Thanassis Giannetsos, Sofia Anna Menesidou, Ruben Alonso, Nino Cauli,
Antonello Meloni, Diego Reforgiato Recupero, Dimosthenis Kyriazis, Georgios
Sofianidis, Spyros Theodoropoulos, Bla\v{z} Fortuna, Dunja Mladeni\'c, John
Soldatos | Human-Centric Artificial Intelligence Architecture for Industry 5.0
Applications | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human-centricity is the core value behind the evolution of manufacturing
towards Industry 5.0. Nevertheless, there is a lack of architecture that
considers safety, trustworthiness, and human-centricity at its core. Therefore,
we propose an architecture that integrates Artificial Intelligence (Active
Learning, Forecasting, Explainable Artificial Intelligence), simulated reality,
decision-making, and users' feedback, focusing on synergies between humans and
machines. Furthermore, we align the proposed architecture with the Big Data
Value Association Reference Architecture Model. Finally, we validate it on
three use cases from real-world case studies.
| [
{
"version": "v1",
"created": "Mon, 21 Mar 2022 08:16:46 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Oct 2022 09:53:08 GMT"
}
] | 1,666,224,000,000 | [
[
"Rožanec",
"Jože M.",
""
],
[
"Novalija",
"Inna",
""
],
[
"Zajec",
"Patrik",
""
],
[
"Kenda",
"Klemen",
""
],
[
"Tavakoli",
"Hooman",
""
],
[
"Suh",
"Sungho",
""
],
[
"Veliou",
"Entso",
""
],
[
"Papamartzivanos",
"Dimitrios",
""
],
[
"Giannetsos",
"Thanassis",
""
],
[
"Menesidou",
"Sofia Anna",
""
],
[
"Alonso",
"Ruben",
""
],
[
"Cauli",
"Nino",
""
],
[
"Meloni",
"Antonello",
""
],
[
"Recupero",
"Diego Reforgiato",
""
],
[
"Kyriazis",
"Dimosthenis",
""
],
[
"Sofianidis",
"Georgios",
""
],
[
"Theodoropoulos",
"Spyros",
""
],
[
"Fortuna",
"Blaž",
""
],
[
"Mladenić",
"Dunja",
""
],
[
"Soldatos",
"John",
""
]
] |
2203.10944 | Ezana Beyenne | Ezana N. Beyenne | Spreadsheet computing with Finite Domain Constraint Enhancements | 2008 Master's thesis | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spreadsheet computing is one of the more popular computing methodologies in
today's modern society. The spreadsheet application's ease of use and
usefulness has enabled non-programmers to perform programming-like tasks in a
familiar setting modeled after the tabular "pen and paper" approach. However,
spreadsheet applications are limited to bookkeeping-like tasks due to their
single-direction data flow. This thesis demonstrates an extension of the
spreadsheet computing paradigm in overcoming this limitation to solve
constraint satisfaction problems. We present a framework seamlessly
incorporating a finite constraint solver with the spreadsheet computing
paradigm. This framework allows the individual cells in the spreadsheet to be
attached to either a finite domain or a constraint specifying the relationship
among the cells. The framework provides an interface for constraint solving and
further enhances the spreadsheet computing paradigm by providing a set of
spreadsheet-specific constraints that will aid in controlling the scalability
of large spreadsheet applications implementations. Finally, we provide examples
to demonstrate the usability and usefulness of the extended spreadsheet
paradigm.
Keywords: Spreadsheet computing, Constraint Logic Programming, Constraint
satisfaction, Domain-Specific language, Excel, SWI Prolog, C#
| [
{
"version": "v1",
"created": "Tue, 22 Feb 2022 17:50:48 GMT"
}
] | 1,647,907,200,000 | [
[
"Beyenne",
"Ezana N.",
""
]
] |
2203.11743 | Joshua Andle | Joshua Andle, Nicholas Soucy, Simon Socolow, Salimeh Yasaei Sekeh | The Stanford Drone Dataset is More Complex than We Think: An Analysis of
Key Characteristics | 12 pages, 10 figures, 5 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Several datasets exist which contain annotated information of individuals'
trajectories. Such datasets are vital for many real-world applications,
including trajectory prediction and autonomous navigation. One prominent
dataset currently in use is the Stanford Drone Dataset (SDD). Despite its
prominence, discussion surrounding the characteristics of this dataset is
insufficient. We demonstrate how this insufficiency reduces the information
available to users and can impact performance. Our contributions include the
outlining of key characteristics in the SDD, employment of an
information-theoretic measure and custom metric to clearly visualize those
characteristics, the implementation of the PECNet and Y-Net trajectory
prediction models to demonstrate the outlined characteristics' impact on
predictive performance, and lastly we provide a comparison between the SDD and
Intersection Drone (inD) Dataset. Our analysis of the SDD's key characteristics
is important because without adequate information about available datasets a
user's ability to select the most suitable dataset for their methods, to
reproduce one another's results, and to interpret their own results are
hindered. The observations we make through this analysis provide a readily
accessible and interpretable source of information for those planning to use
the SDD. Our intention is to increase the performance and reproducibility of
methods applied to this dataset going forward, while also clearly detailing
less obvious features of the dataset for new users.
| [
{
"version": "v1",
"created": "Tue, 22 Mar 2022 13:58:14 GMT"
}
] | 1,647,993,600,000 | [
[
"Andle",
"Joshua",
""
],
[
"Soucy",
"Nicholas",
""
],
[
"Socolow",
"Simon",
""
],
[
"Sekeh",
"Salimeh Yasaei",
""
]
] |
2203.11912 | Levi Lelis | Leandro C. Medeiros, David S. Aleixo, and Levi H. S. Lelis | What can we Learn Even From the Weakest? Learning Sketches for
Programmatic Strategies | Published at AAAI'22 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we show that behavioral cloning can be used to learn effective
sketches of programmatic strategies. We show that even the sketches learned by
cloning the behavior of weak players can help the synthesis of programmatic
strategies. This is because even weak players can provide helpful information,
e.g., that a player must choose an action in their turn of the game. If
behavioral cloning is not employed, the synthesizer needs to learn even the
most basic information by playing the game, which can be computationally
expensive. We demonstrate empirically the advantages of our sketch-learning
approach with simulated annealing and UCT synthesizers. We evaluate our
synthesizers in the games of Can't Stop and MicroRTS. The sketch-based
synthesizers are able to learn stronger programmatic strategies than their
original counterparts. Our synthesizers generate strategies of Can't Stop that
defeat a traditional programmatic strategy for the game. They also synthesize
strategies that defeat the best performing method from the latest MicroRTS
competition.
| [
{
"version": "v1",
"created": "Tue, 22 Mar 2022 17:33:01 GMT"
}
] | 1,647,993,600,000 | [
[
"Medeiros",
"Leandro C.",
""
],
[
"Aleixo",
"David S.",
""
],
[
"Lelis",
"Levi H. S.",
""
]
] |
2203.12111 | Alexander Neuwirth | Alex Moran, Bart Gebka, Joshua Goldshteyn, Autumn Beyer, Nathan
Johnson, and Alexander Neuwirth | Muscle Vision: Real Time Keypoint Based Pose Classification of Physical
Exercises | Published in MICS 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recent advances in machine learning technology have enabled highly portable
and performant models for many common tasks, especially in image recognition.
One emerging field, 3D human pose recognition extrapolated from video, has now
advanced to the point of enabling real-time software applications with robust
enough output to support downstream machine learning tasks. In this work we
propose a new machine learning pipeline and web interface that performs human
pose recognition on a live video feed to detect when common exercises are
performed and classify them accordingly. We present a model interface capable
of webcam input with live display of classification results. Our main
contributions include a keypoint and time series based lightweight approach for
classifying a selected set of fitness exercises and a web-based software
application for obtaining and visualizing the results in real time.
| [
{
"version": "v1",
"created": "Wed, 23 Mar 2022 00:55:07 GMT"
}
] | 1,648,080,000,000 | [
[
"Moran",
"Alex",
""
],
[
"Gebka",
"Bart",
""
],
[
"Goldshteyn",
"Joshua",
""
],
[
"Beyer",
"Autumn",
""
],
[
"Johnson",
"Nathan",
""
],
[
"Neuwirth",
"Alexander",
""
]
] |
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