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2010.13583 | Linlin Hou | Linlin Hou, Ji Zhang, Ou Wu, Ting Yu, Zhen Wang, Zhao Li, Jianliang
Gao, Yingchun Ye, Rujing Yao | Method and Dataset Entity Mining in Scientific Literature: A CNN +
Bi-LSTM Model with Self-attention | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Literature analysis facilitates researchers to acquire a good understanding
of the development of science and technology. The traditional literature
analysis focuses largely on the literature metadata such as topics, authors,
abstracts, keywords, references, etc., and little attention was paid to the
main content of papers. In many scientific domains such as science, computing,
engineering, etc., the methods and datasets involved in the scientific papers
published in those domains carry important information and are quite useful for
domain analysis as well as algorithm and dataset recommendation. In this paper,
we propose a novel entity recognition model, called MDER, which is able to
effectively extract the method and dataset entities from the main textual
content of scientific papers. The model utilizes rule embedding and adopts a
parallel structure of CNN and Bi-LSTM with the self-attention mechanism. We
evaluate the proposed model on datasets which are constructed from the
published papers of four research areas in computer science, i.e., NLP, CV,
Data Mining and AI. The experimental results demonstrate that our model
performs well in all the four areas and it features a good learning capacity
for cross-area learning and recognition. We also conduct experiments to
evaluate the effectiveness of different building modules within our model which
indicate that the importance of different building modules in collectively
contributing to the good entity recognition performance as a whole. The data
augmentation experiments on our model demonstrated that data augmentation
positively contributes to model training, making our model much more robust in
dealing with the scenarios where only small number of training samples are
available. We finally apply our model on PAKDD papers published from 2009-2019
to mine insightful results from scientific papers published in a longer time
span.
| [
{
"version": "v1",
"created": "Mon, 26 Oct 2020 13:38:43 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Jan 2021 02:33:37 GMT"
}
] | 1,611,878,400,000 | [
[
"Hou",
"Linlin",
""
],
[
"Zhang",
"Ji",
""
],
[
"Wu",
"Ou",
""
],
[
"Yu",
"Ting",
""
],
[
"Wang",
"Zhen",
""
],
[
"Li",
"Zhao",
""
],
[
"Gao",
"Jianliang",
""
],
[
"Ye",
"Yingchun",
""
],
[
"Yao",
"Rujing",
""
]
] |
2010.14108 | Mingjun Zhao | Mingjun Zhao, Shengli Yan, Bang Liu, Xinwang Zhong, Qian Hao, Haolan
Chen, Di Niu, Bowei Long and Weidong Guo | QBSUM: a Large-Scale Query-Based Document Summarization Dataset from
Real-world Applications | accepted by Computer Speech & Language | null | 10.1016/j.csl.2020.101166 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Query-based document summarization aims to extract or generate a summary of a
document which directly answers or is relevant to the search query. It is an
important technique that can be beneficial to a variety of applications such as
search engines, document-level machine reading comprehension, and chatbots.
Currently, datasets designed for query-based summarization are short in numbers
and existing datasets are also limited in both scale and quality. Moreover, to
the best of our knowledge, there is no publicly available dataset for Chinese
query-based document summarization. In this paper, we present QBSUM, a
high-quality large-scale dataset consisting of 49,000+ data samples for the
task of Chinese query-based document summarization. We also propose multiple
unsupervised and supervised solutions to the task and demonstrate their
high-speed inference and superior performance via both offline experiments and
online A/B tests. The QBSUM dataset is released in order to facilitate future
advancement of this research field.
| [
{
"version": "v1",
"created": "Tue, 27 Oct 2020 07:30:04 GMT"
},
{
"version": "v2",
"created": "Wed, 28 Oct 2020 08:39:51 GMT"
}
] | 1,603,929,600,000 | [
[
"Zhao",
"Mingjun",
""
],
[
"Yan",
"Shengli",
""
],
[
"Liu",
"Bang",
""
],
[
"Zhong",
"Xinwang",
""
],
[
"Hao",
"Qian",
""
],
[
"Chen",
"Haolan",
""
],
[
"Niu",
"Di",
""
],
[
"Long",
"Bowei",
""
],
[
"Guo",
"Weidong",
""
]
] |
2010.14194 | Ahmad Asadi | Mehran Taghian, Ahmad Asadi, Reza Safabakhsh | Learning Financial Asset-Specific Trading Rules via Deep Reinforcement
Learning | 41 pages, 6 figures, submitted to the journal of Expert Systems with
Applications | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generating asset-specific trading signals based on the financial conditions
of the assets is one of the challenging problems in automated trading. Various
asset trading rules are proposed experimentally based on different technical
analysis techniques. However, these kind of trading strategies are profitable,
extracting new asset-specific trading rules from vast historical data to
increase total return and decrease the risk of portfolios is difficult for
human experts. Recently, various deep reinforcement learning (DRL) methods are
employed to learn the new trading rules for each asset. In this paper, a novel
DRL model with various feature extraction modules is proposed. The effect of
different input representations on the performance of the models is
investigated and the performance of DRL-based models in different markets and
asset situations is studied. The proposed model in this work outperformed the
other state-of-the-art models in learning single asset-specific trading rules
and obtained a total return of almost 262% in two years on a specific asset
while the best state-of-the-art model get 78% on the same asset in the same
time period.
| [
{
"version": "v1",
"created": "Tue, 27 Oct 2020 10:59:53 GMT"
}
] | 1,603,843,200,000 | [
[
"Taghian",
"Mehran",
""
],
[
"Asadi",
"Ahmad",
""
],
[
"Safabakhsh",
"Reza",
""
]
] |
2010.14202 | Wenjie Ou | Wenjie Ou, Yue Lin | A Clarifying Question Selection System from NTES_ALONG in Convai3
Challenge | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents the participation of NetEase Game AI Lab team for the
ClariQ challenge at Search-oriented Conversational AI (SCAI) EMNLP workshop in
2020. The challenge asks for a complete conversational information retrieval
system that can understanding and generating clarification questions. We
propose a clarifying question selection system which consists of response
understanding, candidate question recalling and clarifying question ranking. We
fine-tune a RoBERTa model to understand user's responses and use an enhanced
BM25 model to recall the candidate questions. In clarifying question ranking
stage, we reconstruct the training dataset and propose two models based on
ELECTRA. Finally we ensemble the models by summing up their output
probabilities and choose the question with the highest probability as the
clarification question. Experiments show that our ensemble ranking model
outperforms in the document relevance task and achieves the best recall@[20,30]
metrics in question relevance task. And in multi-turn conversation evaluation
in stage2, our system achieve the top score of all document relevance metrics.
| [
{
"version": "v1",
"created": "Tue, 27 Oct 2020 11:22:53 GMT"
},
{
"version": "v2",
"created": "Wed, 28 Oct 2020 11:11:55 GMT"
},
{
"version": "v3",
"created": "Fri, 20 Nov 2020 04:19:33 GMT"
}
] | 1,606,089,600,000 | [
[
"Ou",
"Wenjie",
""
],
[
"Lin",
"Yue",
""
]
] |
2010.14252 | Ziyi Chen | Ziyi Chen and Patrick De Causmaecker and Yajie Dou | Neural Networked Assisted Tree Search for the Personnel Rostering
Problem | 16 pages, 10 figures, 4 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The personnel rostering problem is the problem of finding an optimal way to
assign employees to shifts, subject to a set of hard constraints which all
valid solutions must follow, and a set of soft constraints which define the
relative quality of valid solutions. The problem has received significant
attention in the literature and is addressed by a large number of exact and
metaheuristic methods. In order to make the complex and costly design of
heuristics for the personnel rostering problem automatic, we propose a new
method combined Deep Neural Network and Tree Search. By treating schedules as
matrices, the neural network can predict the distance between the current
solution and the optimal solution. It can select solution strategies by
analyzing existing (near-)optimal solutions to personnel rostering problem
instances. Combined with branch and bound, the network can give every node a
probability which indicates the distance between it and the optimal one, so
that a well-informed choice can be made on which branch to choose next and to
prune the search tree.
| [
{
"version": "v1",
"created": "Sat, 24 Oct 2020 22:23:20 GMT"
}
] | 1,603,843,200,000 | [
[
"Chen",
"Ziyi",
""
],
[
"De Causmaecker",
"Patrick",
""
],
[
"Dou",
"Yajie",
""
]
] |
2010.14289 | Daniel Graves PhD | Daniel Graves, Johannes G\"unther, Jun Luo | Affordance as general value function: A computational model | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | General value functions (GVFs) in the reinforcement learning (RL) literature
are long-term predictive summaries of the outcomes of agents following specific
policies in the environment. Affordances as perceived action possibilities with
specific valence may be cast into predicted policy-relative goodness and
modelled as GVFs. A systematic explication of this connection shows that GVFs
and especially their deep learning embodiments (1) realize affordance
prediction as a form of direct perception, (2) illuminate the fundamental
connection between action and perception in affordance, and (3) offer a
scalable way to learn affordances using RL methods. Through an extensive review
of existing literature on GVF applications and representative affordance
research in robotics, we demonstrate that GVFs provide the right framework for
learning affordances in real-world applications. In addition, we highlight a
few new avenues of research opened up by the perspective of "affordance as
GVF", including using GVFs for orchestrating complex behaviors.
| [
{
"version": "v1",
"created": "Tue, 27 Oct 2020 13:42:58 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Jan 2021 01:54:32 GMT"
},
{
"version": "v3",
"created": "Sat, 8 May 2021 00:15:11 GMT"
}
] | 1,620,691,200,000 | [
[
"Graves",
"Daniel",
""
],
[
"Günther",
"Johannes",
""
],
[
"Luo",
"Jun",
""
]
] |
2010.14376 | Oliver Niggemann | Oliver Niggemann and Alexander Diedrich and Christian Kuehnert and
Erik Pfannstiel and Joshua Schraven | The DigitalTwin from an Artificial Intelligence Perspective | 10 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Services for Cyber-Physical Systems based on Artificial Intelligence and
Machine Learning require a virtual representation of the physical. To reduce
modeling efforts and to synchronize results, for each system, a common and
unique virtual representation used by all services during the whole system
life-cycle is needed, i.e. a DigitalTwin. In this paper such a DigitalTwin,
namely the AI reference model AITwin, is defined. This reference model is
verified by using a running example from process industry and by analyzing the
work done in recent projects.
| [
{
"version": "v1",
"created": "Tue, 27 Oct 2020 15:40:36 GMT"
}
] | 1,603,843,200,000 | [
[
"Niggemann",
"Oliver",
""
],
[
"Diedrich",
"Alexander",
""
],
[
"Kuehnert",
"Christian",
""
],
[
"Pfannstiel",
"Erik",
""
],
[
"Schraven",
"Joshua",
""
]
] |
2010.14388 | Alun Preece | Katie Barrett-Powell, Jack Furby, Liam Hiley, Marc Roig Vilamala,
Harrison Taylor, Federico Cerutti, Alun Preece, Tianwei Xing, Luis Garcia,
Mani Srivastava, Dave Braines | An Experimentation Platform for Explainable Coalition Situational
Understanding | Presented at AAAI FSS-20: Artificial Intelligence in Government and
Public Sector, Washington, DC, USA | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an experimentation platform for coalition situational
understanding research that highlights capabilities in explainable artificial
intelligence/machine learning (AI/ML) and integration of symbolic and
subsymbolic AI/ML approaches for event processing. The Situational
Understanding Explorer (SUE) platform is designed to be lightweight, to easily
facilitate experiments and demonstrations, and open. We discuss our
requirements to support coalition multi-domain operations with emphasis on
asset interoperability and ad hoc human-machine teaming in a dense urban
terrain setting. We describe the interface functionality and give examples of
SUE applied to coalition situational understanding tasks.
| [
{
"version": "v1",
"created": "Tue, 27 Oct 2020 15:51:27 GMT"
},
{
"version": "v2",
"created": "Mon, 9 Nov 2020 16:01:15 GMT"
}
] | 1,604,966,400,000 | [
[
"Barrett-Powell",
"Katie",
""
],
[
"Furby",
"Jack",
""
],
[
"Hiley",
"Liam",
""
],
[
"Vilamala",
"Marc Roig",
""
],
[
"Taylor",
"Harrison",
""
],
[
"Cerutti",
"Federico",
""
],
[
"Preece",
"Alun",
""
],
[
"Xing",
"Tianwei",
""
],
[
"Garcia",
"Luis",
""
],
[
"Srivastava",
"Mani",
""
],
[
"Braines",
"Dave",
""
]
] |
2010.14654 | Luis Duarte | Luis Duarte, Jonathan Torres, Vitor Ribeiro, In\^es Moreira | Artificial Intelligence Systems applied to tourism: A Survey | bad content | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Artificial Intelligence (AI) has been improving the performance of systems
for a diverse set of tasks and introduced a more interactive generation of
personal agents. Despite the current trend of applying AI for a great amount of
areas, we have not seen the same quantity of work being developed for the
tourism sector. This paper reports on the main applications of AI systems
developed for tourism and the current state of the art for this sector. The
paper also provides an up-to-date survey of this field regarding several key
works and systems that are applied to tourism, like Personal Agents, for
providing a more interactive experience. We also carried out an in-depth
research on systems for predicting traffic human flow, more accurate
recommendation systems and even how geospatial is trying to display tourism
data in a more informative way and prevent problems before they arise.
| [
{
"version": "v1",
"created": "Tue, 27 Oct 2020 22:41:12 GMT"
},
{
"version": "v2",
"created": "Mon, 1 Mar 2021 15:44:03 GMT"
}
] | 1,614,643,200,000 | [
[
"Duarte",
"Luis",
""
],
[
"Torres",
"Jonathan",
""
],
[
"Ribeiro",
"Vitor",
""
],
[
"Moreira",
"Inês",
""
]
] |
2010.15255 | Sriram Gopalakrishnan | Sriram Gopalakrishnan, Subbarao Kambhampati | Minimizing Robot Navigation-Graph For Position-Based Predictability By
Humans | 8 pages, 6 pages supplemental material. Accepted as an extended
abstract in the 21st International Conference on Autonomous Agents and
Multiagent Systems(AAMAS2022 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In situations where humans and robots are moving in the same space whilst
performing their own tasks, predictable paths taken by mobile robots can not
only make the environment feel safer, but humans can also help with the
navigation in the space by avoiding path conflicts or not blocking the way. So
predictable paths become vital. The cognitive effort for the human to predict
the robot's path becomes untenable as the number of robots increases. As the
number of humans increase, it also makes it harder for the robots to move while
considering the motion of multiple humans. Additionally, if new people are
entering the space -- like in restaurants, banks, and hospitals -- they would
have less familiarity with the trajectories typically taken by the robots; this
further increases the needs for predictable robot motion along paths.
With this in mind, we propose to minimize the navigation-graph of the robot
for position-based predictability, which is predictability from just the
current position of the robot. This is important since the human cannot be
expected to keep track of the goals and prior actions of the robot in addition
to doing their own tasks. In this paper, we define measures for position-based
predictability, then present and evaluate a hill-climbing algorithm to minimize
the navigation-graph (directed graph) of robot motion. This is followed by the
results of our human-subject experiments which support our proposed
methodology.
| [
{
"version": "v1",
"created": "Wed, 28 Oct 2020 22:09:10 GMT"
},
{
"version": "v2",
"created": "Tue, 11 Jan 2022 23:28:57 GMT"
}
] | 1,642,032,000,000 | [
[
"Gopalakrishnan",
"Sriram",
""
],
[
"Kambhampati",
"Subbarao",
""
]
] |
2010.15296 | Niall Walsh | Stefan Kennedy and Niall Walsh, Kirils Sloka, Jennifer Foster, Andrew
McCarren | Fact or Factitious? Contextualized Opinion Spam Detection | 6 pages, 3 figures, presented at the 2019 ACL Conference in Florence,
Italy | null | 10.18653/v1/P19-2048 | P19-2048 P19-2048 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we perform an analytic comparison of a number of techniques
used to detect fake and deceptive online reviews. We apply a number machine
learning approaches found to be effective, and introduce our own approach by
fine-tuning state of the art contextualised embeddings. The results we obtain
show the potential of contextualised embeddings for fake review detection, and
lay the groundwork for future research in this area.
| [
{
"version": "v1",
"created": "Thu, 29 Oct 2020 00:59:06 GMT"
}
] | 1,604,016,000,000 | [
[
"Kennedy",
"Stefan",
""
],
[
"Walsh",
"Niall",
""
],
[
"Sloka",
"Kirils",
""
],
[
"Foster",
"Jennifer",
""
],
[
"McCarren",
"Andrew",
""
]
] |
2010.15832 | EPTCS | Pedro Quaresma (University of Coimbra, Portugal), Walther Neuper (JKU
Johannes Kepler University, Linz, Austria), Jo\~ao Marcos (UFRN, Brazil) | Proceedings 9th International Workshop on Theorem Proving Components for
Educational Software | null | EPTCS 328, 2020 | 10.4204/EPTCS.328 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The 9th International Workshop on Theorem-Proving Components for Educational
Software (ThEdu'20) was scheduled to happen on June 29 as a satellite of the
IJCAR-FSCD 2020 joint meeting, in Paris. The COVID-19 pandemic came by
surprise, though, and the main conference was virtualised. Fearing that an
online meeting would not allow our community to fully reproduce the usual
face-to-face networking opportunities of the ThEdu initiative, the Steering
Committee of ThEdu decided to cancel our workshop. Given that many of us had
already planned and worked for that moment, we decided that ThEdu'20 could
still live in the form of an EPTCS volume. The EPTCS concurred with us,
recognising this very singular situation, and accepted our proposal of
organising a special issue with papers submitted to ThEdu'20. An open call for
papers was then issued, and attracted five submissions, all of which have been
accepted by our reviewers, who produced three careful reports on each of the
contributions. The resulting revised papers are collected in the present
volume. We, the volume editors, hope that this collection of papers will help
further promoting the development of theorem-proving-based software, and that
it will collaborate to improve the mutual understanding between computer
mathematicians and stakeholders in education. With some luck, we would actually
expect that the very special circumstances set up by the worst sanitary crisis
in a century will happen to reinforce the need for the application of certified
components and of verification methods for the production of educational
software that would be available even when the traditional on-site learning
experiences turn out not to be recommendable.
| [
{
"version": "v1",
"created": "Wed, 28 Oct 2020 00:36:08 GMT"
}
] | 1,604,016,000,000 | [
[
"Quaresma",
"Pedro",
"",
"University of Coimbra, Portugal"
],
[
"Neuper",
"Walther",
"",
"JKU\n Johannes Kepler University, Linz, Austria"
],
[
"Marcos",
"João",
"",
"UFRN, Brazil"
]
] |
2010.16244 | Aditya Gulati | Aditya Gulati, Sarthak Soni, Shrisha Rao | Interleaving Fast and Slow Decision Making | 7 pages, 11 figures; typos corrected, references added | null | 10.1109/ICRA48506.2021.9561562 | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | The "Thinking, Fast and Slow" paradigm of Kahneman proposes that we use two
different styles of thinking -- a fast and intuitive System 1 for certain
tasks, along with a slower but more analytical System 2 for others. While the
idea of using this two-system style of thinking is gaining popularity in AI and
robotics, our work considers how to interleave the two styles of
decision-making, i.e., how System 1 and System 2 should be used together. For
this, we propose a novel and general framework which includes a new System 0 to
oversee Systems 1 and 2. At every point when a decision needs to be made,
System 0 evaluates the situation and quickly hands over the decision-making
process to either System 1 or System 2. We evaluate such a framework on a
modified version of the classic Pac-Man game, with an already-trained RL
algorithm for System 1, a Monte-Carlo tree search for System 2, and several
different possible strategies for System 0. As expected, arbitrary switches
between Systems 1 and 2 do not work, but certain strategies do well. With
System 0, an agent is able to perform better than one that uses only System 1
or System 2.
| [
{
"version": "v1",
"created": "Fri, 30 Oct 2020 13:16:10 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Mar 2021 16:49:24 GMT"
}
] | 1,635,206,400,000 | [
[
"Gulati",
"Aditya",
""
],
[
"Soni",
"Sarthak",
""
],
[
"Rao",
"Shrisha",
""
]
] |
2011.00215 | Shuyin Xia | Shuyin Xia, Wenhua Li, Guoyin Wang, Xinbo Gao, Changqing Zhang,
Elisabeth Giem | LRA: an accelerated rough set framework based on local redundancy of
attribute for feature selection | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose and prove the theorem regarding the stability of
attributes in a decision system. Based on the theorem, we propose the LRA
framework for accelerating rough set algorithms. It is a general-purpose
framework which can be applied to almost all rough set methods significantly .
Theoretical analysis guarantees high efficiency. Note that the enhancement of
efficiency will not lead to any decrease of the classification accuracy.
Besides, we provide a simpler prove for the positive approximation acceleration
framework.
| [
{
"version": "v1",
"created": "Sat, 31 Oct 2020 08:50:28 GMT"
}
] | 1,604,361,600,000 | [
[
"Xia",
"Shuyin",
""
],
[
"Li",
"Wenhua",
""
],
[
"Wang",
"Guoyin",
""
],
[
"Gao",
"Xinbo",
""
],
[
"Zhang",
"Changqing",
""
],
[
"Giem",
"Elisabeth",
""
]
] |
2011.00775 | Ruo Ando | Ruo Ando, Yoshiyasu Takefuji | A Curious New Result of Resolution Strategies in Negation-Limited
Inverters Problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generally, negation-limited inverters problem is known as a puzzle of
constructing an inverter with AND gates and OR gates and a few inverters. In
this paper, we introduce a curious new result about the effectiveness of two
powerful ATP (Automated Theorem Proving) strategies on tackling negation
limited inverter problem. Two resolution strategies are UR (Unit Resulting)
resolution and hyper-resolution. In experiment, we come two kinds of automated
circuit construction: 3 input/output inverters and 4 input/output BCD Counter
Circuit. Both circuits are constructed with a few limited inverters. Curiously,
it has been turned out that UR resolution is drastically faster than
hyper-resolution in the measurement of the size of SOS (Set of Support).
Besides, we discuss the syntactic and semantic criteria which might causes
considerable difference of computation cost between UR resolution and
hyper-resolution.
| [
{
"version": "v1",
"created": "Mon, 2 Nov 2020 06:52:35 GMT"
}
] | 1,604,361,600,000 | [
[
"Ando",
"Ruo",
""
],
[
"Takefuji",
"Yoshiyasu",
""
]
] |
2011.00781 | Abhinav Sharma | Abhinav Sharma, Advait Deshpande, Yanming Wang, Xinyi Xu, Prashan
Madumal, Anbin Hou | Searching k-Optimal Goals for an Orienteering Problem on a Specialized
Graph with Budget Constraints | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We propose a novel non-randomized anytime orienteering algorithm for finding
k-optimal goals that maximize reward on a specialized graph with budget
constraints. This specialized graph represents a real-world scenario which is
analogous to an orienteering problem of finding k-most optimal goal states.
| [
{
"version": "v1",
"created": "Mon, 2 Nov 2020 07:15:41 GMT"
}
] | 1,604,361,600,000 | [
[
"Sharma",
"Abhinav",
""
],
[
"Deshpande",
"Advait",
""
],
[
"Wang",
"Yanming",
""
],
[
"Xu",
"Xinyi",
""
],
[
"Madumal",
"Prashan",
""
],
[
"Hou",
"Anbin",
""
]
] |
2011.01306 | Nicholas Quek | Nicholas Quek Wei Kiat, Duo Wang, Mateja Jamnik | Pairwise Relations Discriminator for Unsupervised Raven's Progressive
Matrices | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ability to hypothesise, develop abstract concepts based on concrete
observations and apply these hypotheses to justify future actions has been
paramount in human development. An existing line of research in outfitting
intelligent machines with abstract reasoning capabilities revolves around the
Raven's Progressive Matrices (RPM). There have been many breakthroughs in
supervised approaches to solving RPM in recent years. However, this process
requires external assistance, and thus it cannot be claimed that machines have
achieved reasoning ability comparable to humans. Namely, humans can solve RPM
problems without supervision or prior experience once the RPM rule that
relations can only exist row/column-wise is properly introduced. In this paper,
we introduce a pairwise relations discriminator (PRD), a technique to develop
unsupervised models with sufficient reasoning abilities to tackle an RPM
problem. PRD reframes the RPM problem into a relation comparison task, which we
can solve without requiring the labelling of the RPM problem. We can identify
the optimal candidate by adapting the application of PRD to the RPM problem.
Our approach, the PRD, establishes a new state-of-the-art unsupervised learning
benchmark with an accuracy of 55.9% on the I-RAVEN, presenting a significant
improvement and a step forward in equipping machines with abstract reasoning.
| [
{
"version": "v1",
"created": "Mon, 2 Nov 2020 20:49:46 GMT"
},
{
"version": "v2",
"created": "Thu, 5 Aug 2021 09:11:31 GMT"
}
] | 1,628,208,000,000 | [
[
"Kiat",
"Nicholas Quek Wei",
""
],
[
"Wang",
"Duo",
""
],
[
"Jamnik",
"Mateja",
""
]
] |
2011.01542 | Syrine Belakaria | Syrine Belakaria, Aryan Deshwal and Janardhan Rao Doppa | Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space
Entropy Search Approach | corrected typos. arXiv admin note: text overlap with arXiv:2009.05700 | The Thirty-Fourth AAAI Conference on Artificial Intelligence AAAI,
2020 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the novel problem of blackbox optimization of multiple objectives
via multi-fidelity function evaluations that vary in the amount of resources
consumed and their accuracy. The overall goal is to approximate the true Pareto
set of solutions by minimizing the resources consumed for function evaluations.
For example, in power system design optimization, we need to find designs that
trade-off cost, size, efficiency, and thermal tolerance using multi-fidelity
simulators for design evaluations. In this paper, we propose a novel approach
referred as Multi-Fidelity Output Space Entropy Search for Multi-objective
Optimization (MF-OSEMO) to solve this problem. The key idea is to select the
sequence of candidate input and fidelity-vector pairs that maximize the
information gained about the true Pareto front per unit resource cost. Our
experiments on several synthetic and real-world benchmark problems show that
MF-OSEMO, with both approximations, significantly improves over the
state-of-the-art single-fidelity algorithms for multi-objective optimization.
| [
{
"version": "v1",
"created": "Mon, 2 Nov 2020 06:59:04 GMT"
}
] | 1,604,448,000,000 | [
[
"Belakaria",
"Syrine",
""
],
[
"Deshwal",
"Aryan",
""
],
[
"Doppa",
"Janardhan Rao",
""
]
] |
2011.01788 | Elena Congeduti | Elena Congeduti, Alexander Mey, Frans A. Oliehoek | Loss Bounds for Approximate Influence-Based Abstraction | 13 pages, 9 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sequential decision making techniques hold great promise to improve the
performance of many real-world systems, but computational complexity hampers
their principled application. Influence-based abstraction aims to gain leverage
by modeling local subproblems together with the 'influence' that the rest of
the system exerts on them. While computing exact representations of such
influence might be intractable, learning approximate representations offers a
promising approach to enable scalable solutions. This paper investigates the
performance of such approaches from a theoretical perspective. The primary
contribution is the derivation of sufficient conditions on approximate
influence representations that can guarantee solutions with small value loss.
In particular we show that neural networks trained with cross entropy are well
suited to learn approximate influence representations. Moreover, we provide a
sample based formulation of the bounds, which reduces the gap to applications.
Finally, driven by our theoretical insights, we propose approximation error
estimators, which empirically reveal to correlate well with the value loss.
| [
{
"version": "v1",
"created": "Tue, 3 Nov 2020 15:33:10 GMT"
},
{
"version": "v2",
"created": "Thu, 18 Feb 2021 10:25:38 GMT"
},
{
"version": "v3",
"created": "Tue, 23 Feb 2021 15:31:22 GMT"
}
] | 1,614,124,800,000 | [
[
"Congeduti",
"Elena",
""
],
[
"Mey",
"Alexander",
""
],
[
"Oliehoek",
"Frans A.",
""
]
] |
2011.01826 | Daniel Borrajo | Daniel Borrajo, Manuela Veloso, Sameena Shah | Simulating and classifying behavior in adversarial environments based on
action-state traces: an application to money laundering | A version appeared in the Proceedings of the 2020 ACM International
Conference on AI in Finance (ICAIF'20) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many business applications involve adversarial relationships in which both
sides adapt their strategies to optimize their opposing benefits. One of the
key characteristics of these applications is the wide range of strategies that
an adversary may choose as they adapt their strategy dynamically to sustain
benefits and evade authorities. In this paper, we present a novel way of
approaching these types of applications, in particular in the context of
Anti-Money Laundering. We provide a mechanism through which diverse, realistic
and new unobserved behavior may be generated to discover potential unobserved
adversarial actions to enable organizations to preemptively mitigate these
risks. In this regard, we make three main contributions. (a) Propose a novel
behavior-based model as opposed to individual transactions-based models
currently used by financial institutions. We introduce behavior traces as
enriched relational representation to represent observed human behavior. (b) A
modelling approach that observes these traces and is able to accurately infer
the goals of actors by classifying the behavior into money laundering or
standard behavior despite significant unobserved activity. And (c) a synthetic
behavior simulator that can generate new previously unseen traces. The
simulator incorporates a high level of flexibility in the behavioral parameters
so that we can challenge the detection algorithm. Finally, we provide
experimental results that show that the learning module (automated
investigator) that has only partial observability can still successfully infer
the type of behavior, and thus the simulated goals, followed by customers based
on traces - a key aspiration for many applications today.
| [
{
"version": "v1",
"created": "Tue, 3 Nov 2020 16:30:53 GMT"
}
] | 1,604,448,000,000 | [
[
"Borrajo",
"Daniel",
""
],
[
"Veloso",
"Manuela",
""
],
[
"Shah",
"Sameena",
""
]
] |
2011.01832 | Daniel Borrajo | Daniel Borrajo, Sriram Gopalakrishnan, Vamsi K. Potluru | Goal recognition via model-based and model-free techniques | A version of this paper appeared in the Pre-prints of the Workshop in
Planning for Financial Services (FinPlan) at ICAPS'20 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Goal recognition aims at predicting human intentions from a trace of
observations. This ability allows people or organizations to anticipate future
actions and intervene in a positive (collaborative) or negative (adversarial)
way. Goal recognition has been successfully used in many domains, but it has
been seldom been used by financial institutions. We claim the techniques are
ripe for its wide use in finance-related tasks. The main two approaches to
perform goal recognition are model-based (planning-based) and model-free
(learning-based). In this paper, we adapt state-of-the-art learning techniques
to goal recognition, and compare model-based and model-free approaches in
different domains. We analyze the experimental data to understand the
trade-offs of using both types of methods. The experiments show that
planning-based approaches are ready for some goal-recognition finance tasks.
| [
{
"version": "v1",
"created": "Tue, 3 Nov 2020 16:44:28 GMT"
}
] | 1,604,448,000,000 | [
[
"Borrajo",
"Daniel",
""
],
[
"Gopalakrishnan",
"Sriram",
""
],
[
"Potluru",
"Vamsi K.",
""
]
] |
2011.02223 | Kieran Greer Dr | Kieran Greer | New Ideas for Brain Modelling 7 | null | International Journal of Computational and Applied Mathematics &
Computer Science, Volume 1, pp.34-45, 2021 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper updates the cognitive model, firstly by creating two systems and
then unifying them over the same structure. It represents information at the
semantic level only, where labelled patterns are aggregated into a
'type-set-match' form. It is described that the aggregations can be used to
match across regions with potentially different functionality and therefore
give the structure a required amount of flexibility. The theory is that if the
model stores information which can be transposed in consistent ways, then that
will result in knowledge and some level of intelligence. As part of the design,
patterns have to become distinct and that is realised by unique paths through
shared aggregated structures. An ensemble-hierarchy relation also helps to
define uniqueness through local feedback that may even be an action potential.
The earlier models are still consistent in terms of their proposed
functionality, but some of the architecture boundaries have been moved to match
them up more closely. After pattern optimisation and tree-like aggregations,
the two main models differ only in their upper, more intelligent level. One
provides a propositional logic for mutually inclusive or exclusive pattern
groups and sequences, while the other provides a behaviour script that is
constructed from node types. It can be seen that these two views are
complimentary and would allow some control over behaviours, as well as
memories, that might get selected.
| [
{
"version": "v1",
"created": "Wed, 4 Nov 2020 10:59:01 GMT"
},
{
"version": "v2",
"created": "Sat, 15 May 2021 23:43:49 GMT"
}
] | 1,630,022,400,000 | [
[
"Greer",
"Kieran",
""
]
] |
2011.02912 | Alessandro Antonucci | Marco Zaffalon and Alessandro Antonucci and Rafael Caba\~nas | Causal Expectation-Maximisation | WHY-21 workshop (NeurIPS 2021) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Structural causal models are the basic modelling unit in Pearl's causal
theory; in principle they allow us to solve counterfactuals, which are at the
top rung of the ladder of causation. But they often contain latent variables
that limit their application to special settings. This appears to be a
consequence of the fact, proven in this paper, that causal inference is NP-hard
even in models characterised by polytree-shaped graphs. To deal with such a
hardness, we introduce the causal EM algorithm. Its primary aim is to
reconstruct the uncertainty about the latent variables from data about
categorical manifest variables. Counterfactual inference is then addressed via
standard algorithms for Bayesian networks. The result is a general method to
approximately compute counterfactuals, be they identifiable or not (in which
case we deliver bounds). We show empirically, as well as by deriving credible
intervals, that the approximation we provide becomes accurate in a fair number
of EM runs. These results lead us finally to argue that there appears to be an
unnoticed limitation to the trending idea that counterfactual bounds can often
be computed without knowledge of the structural equations.
| [
{
"version": "v1",
"created": "Wed, 4 Nov 2020 10:25:13 GMT"
},
{
"version": "v2",
"created": "Fri, 20 Aug 2021 07:16:42 GMT"
},
{
"version": "v3",
"created": "Mon, 22 Nov 2021 11:16:46 GMT"
}
] | 1,637,625,600,000 | [
[
"Zaffalon",
"Marco",
""
],
[
"Antonucci",
"Alessandro",
""
],
[
"Cabañas",
"Rafael",
""
]
] |
2011.02918 | Daniel Borrajo | Daniel Borrajo, Manuela Veloso | Domain-independent generation and classification of behavior traces | A version of this paper appears in the Pre-prints of the Workshop in
Planning for Financial Services (FinPlan) at ICAPS'20. arXiv admin note: text
overlap with arXiv:2011.01826 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Financial institutions mostly deal with people. Therefore, characterizing
different kinds of human behavior can greatly help institutions for improving
their relation with customers and with regulatory offices. In many of such
interactions, humans have some internal goals, and execute some actions within
the financial system that lead them to achieve their goals. In this paper, we
tackle these tasks as a behavior-traces classification task. An observer agent
tries to learn characterizing other agents by observing their behavior when
taking actions in a given environment. The other agents can be of several types
and the goal of the observer is to identify the type of the other agent given a
trace of observations. We present CABBOT, a learning technique that allows the
agent to perform on-line classification of the type of planning agent whose
behavior is observing. In this work, the observer agent has partial and noisy
observability of the environment (state and actions of the other agents). In
order to evaluate the performance of the learning technique, we have generated
a domain-independent goal-based simulator of agents. We present experiments in
several (both financial and non-financial) domains with promising results.
| [
{
"version": "v1",
"created": "Tue, 3 Nov 2020 16:58:54 GMT"
}
] | 1,604,620,800,000 | [
[
"Borrajo",
"Daniel",
""
],
[
"Veloso",
"Manuela",
""
]
] |
2011.03359 | Hao Nie | Hao Nie and Qin Zhang | A New Inference algorithm of Dynamic Uncertain Causality Graph based on
Conditional Sampling Method for Complex Cases | null | null | 10.1109/ACCESS.2021.3093205 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dynamic Uncertain Causality Graph(DUCG) is a recently proposed model for
diagnoses of complex systems. It performs well for industry system such as
nuclear power plants, chemical system and spacecrafts. However, the variable
state combination explosion in some cases is still a problem that may result in
inefficiency or even disability in DUCG inference. In the situation of clinical
diagnoses, when a lot of intermediate causes are unknown while the downstream
results are known in a DUCG graph, the combination explosion may appear during
the inference computation. Monte Carlo sampling is a typical algorithm to solve
this problem. However, we are facing the case that the occurrence rate of the
case is very small, e.g. $10^{-20}$, which means a huge number of samplings are
needed. This paper proposes a new scheme based on conditional stochastic
simulation which obtains the final result from the expectation of the
conditional probability in sampling loops instead of counting the sampling
frequency, and thus overcomes the problem. As a result, the proposed algorithm
requires much less time than the DUCG recursive inference algorithm presented
earlier. Moreover, a simple analysis of convergence rate based on a designed
example is given to show the advantage of the proposed method. % In addition,
supports for logic gate, logic cycles, and parallelization, which exist in
DUCG, are also addressed in this paper. The new algorithm reduces the time
consumption a lot and performs 3 times faster than old one with 2.7% error
ratio in a practical graph for Viral Hepatitis B.
| [
{
"version": "v1",
"created": "Fri, 6 Nov 2020 13:55:12 GMT"
},
{
"version": "v2",
"created": "Wed, 24 Feb 2021 02:29:29 GMT"
}
] | 1,624,924,800,000 | [
[
"Nie",
"Hao",
""
],
[
"Zhang",
"Qin",
""
]
] |
2011.03835 | Thibaud De Souza | Thibaud de Souza | Implementing Behavior Trees using Three-Valued Logic | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With consideration to behavior trees and their relevance to planning and
control, within and without game development, the distinction between stateful
and stateless models is discussed; a three-valued logic bridging traditional
control flow with behavior trees is introduced, and a C# implementation is
presented.
| [
{
"version": "v1",
"created": "Sat, 7 Nov 2020 19:12:01 GMT"
}
] | 1,604,966,400,000 | [
[
"de Souza",
"Thibaud",
""
]
] |
2011.03836 | Ning Li | Ning Li, Bethany Keller, Mark Butler, Daniel Cer | SeqGenSQL -- A Robust Sequence Generation Model for Structured Query
Language | 6 pages, 7 figures, 2 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore using T5 (Raffel et al. (2019)) to directly translate natural
language questions into SQL statements. General purpose natural language that
interfaces to information stored within databases requires flexibly translating
natural language questions into database queries. The best performing
text-to-SQL systems approach this task by first converting questions into an
intermediate logical form (LF) (Lyu et al. (2020)). While LFs provide a
convenient intermediate representation and simplify query generation, they
introduce an additional layer of complexity and annotation requirements.
However, weakly supervised modeling that directly converts questions to SQL
statements has proven more difficult without the scaffolding provided by LFs
(Min et al. (2019)). We approach direct conversion of questions to SQL
statements using T5 (Raffel et al. (2019)), a pre-trained textto-text
generation model, modified to support pointer-generator style decoding (See et
al. (2017)). We explore using question augmentation with table schema
information and the use of automatically generated silver training data. The
resulting model achieves 90.5% execution accuracy on the WikiSQL (Zhong et al.
(2017)) test data set, a new state-of-the-art on weakly supervised SQL
generation. The performance improvement is 6.6% absolute over the prior
state-of-the-art (Min et al. (2019)) and approaches the performance of
state-ofthe-art systems making use of LFs.
| [
{
"version": "v1",
"created": "Sat, 7 Nov 2020 19:22:59 GMT"
}
] | 1,604,966,400,000 | [
[
"Li",
"Ning",
""
],
[
"Keller",
"Bethany",
""
],
[
"Butler",
"Mark",
""
],
[
"Cer",
"Daniel",
""
]
] |
2011.03901 | Shreya Khare Ms | Alex Mathai, Shreya Khare, Srikanth Tamilselvam, Senthil Mani | Adversarial Black-Box Attacks On Text Classifiers Using Multi-Objective
Genetic Optimization Guided By Deep Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a novel genetic-algorithm technique that generates black-box
adversarial examples which successfully fool neural network based text
classifiers. We perform a genetic search with multi-objective optimization
guided by deep learning based inferences and Seq2Seq mutation to generate
semantically similar but imperceptible adversaries. We compare our approach
with DeepWordBug (DWB) on SST and IMDB sentiment datasets by attacking three
trained models viz. char-LSTM, word-LSTM and elmo-LSTM. On an average, we
achieve an attack success rate of 65.67% for SST and 36.45% for IMDB across the
three models showing an improvement of 49.48% and 101% respectively.
Furthermore, our qualitative study indicates that 94% of the time, the users
were not able to distinguish between an original and adversarial sample.
| [
{
"version": "v1",
"created": "Sun, 8 Nov 2020 04:30:14 GMT"
},
{
"version": "v2",
"created": "Tue, 10 Nov 2020 04:40:01 GMT"
}
] | 1,605,052,800,000 | [
[
"Mathai",
"Alex",
""
],
[
"Khare",
"Shreya",
""
],
[
"Tamilselvam",
"Srikanth",
""
],
[
"Mani",
"Senthil",
""
]
] |
2011.03909 | Filipp Skomorokhov | Filipp Skomorokhov (1 and 2) and George Ovchinnikov (2) ((1) Moscow
Institute of Physics and Technology, (2) Skolkovo Institute of Science and
Technology) | Reinforcement Learning for Assignment problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper is dedicated to the application of reinforcement learning combined
with neural networks to the general formulation of user scheduling problem. Our
simulator resembles real world problems by means of stochastic changes in
environment. We applied Q-learning based method to the number of dynamic
simulations and outperformed analytical greedy-based solution in terms of total
reward, the aim of which is to get the lowest possible penalty throughout
simulation.
| [
{
"version": "v1",
"created": "Sun, 8 Nov 2020 06:25:50 GMT"
}
] | 1,604,966,400,000 | [
[
"Skomorokhov",
"Filipp",
"",
"1 and 2"
],
[
"Ovchinnikov",
"George",
""
]
] |
2011.03974 | Kai Chen | Kai Chen, Twan van Laarhoven, Elena Marchiori | Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for
Long-term Forecasting | 19 pages, 34 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Long-term forecasting involves predicting a horizon that is far ahead of the
last observation. It is a problem of high practical relevance, for instance for
companies in order to decide upon expensive long-term investments. Despite the
recent progress and success of Gaussian processes (GPs) based on spectral
mixture kernels, long-term forecasting remains a challenging problem for these
kernels because they decay exponentially at large horizons. This is mainly due
to their use of a mixture of Gaussians to model spectral densities.
Characteristics of the signal important for long-term forecasting can be
unravelled by investigating the distribution of the Fourier coefficients of
(the training part of) the signal, which is non-smooth, heavy-tailed, sparse,
and skewed. The heavy tail and skewness characteristics of such distributions
in the spectral domain allow to capture long-range covariance of the signal in
the time domain. Motivated by these observations, we propose to model spectral
densities using a skewed Laplace spectral mixture (SLSM) due to the skewness of
its peaks, sparsity, non-smoothness, and heavy tail characteristics. By
applying the inverse Fourier Transform to this spectral density we obtain a new
GP kernel for long-term forecasting. In addition, we adapt the lottery ticket
method, originally developed to prune weights of a neural network, to GPs in
order to automatically select the number of kernel components. Results of
extensive experiments, including a multivariate time series, show the
beneficial effect of the proposed SLSM kernel for long-term extrapolation and
robustness to the choice of the number of mixture components.
| [
{
"version": "v1",
"created": "Sun, 8 Nov 2020 13:03:59 GMT"
},
{
"version": "v2",
"created": "Tue, 10 Aug 2021 03:24:11 GMT"
},
{
"version": "v3",
"created": "Sat, 2 Oct 2021 04:33:28 GMT"
}
] | 1,633,392,000,000 | [
[
"Chen",
"Kai",
""
],
[
"van Laarhoven",
"Twan",
""
],
[
"Marchiori",
"Elena",
""
]
] |
2011.04085 | Henrique Santos | H. Santos, A. Mulvehill, J. S. Erickson, J. P. McCusker, M. Gordon, O.
Xie, S. Stouffer, G. Capraro, A. Pidwerbetsky, J. Burgess, A. Berlinsky, K.
Turck, J. Ashdown, D. L. McGuinness | A Semantic Framework for Enabling Radio Spectrum Policy Management and
Evaluation | null | The Semantic Web - ISWC 2020. ISWC 2020. Lecture Notes in Computer
Science, vol 12507 | 10.1007/978-3-030-62466-8_30 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Because radio spectrum is a finite resource, its usage and sharing is
regulated by government agencies. These agencies define policies to manage
spectrum allocation and assignment across multiple organizations, systems, and
devices. With more portions of the radio spectrum being licensed for commercial
use, the importance of providing an increased level of automation when
evaluating such policies becomes crucial for the efficiency and efficacy of
spectrum management. We introduce our Dynamic Spectrum Access Policy Framework
for supporting the United States government's mission to enable both federal
and non-federal entities to compatibly utilize available spectrum. The DSA
Policy Framework acts as a machine-readable policy repository providing policy
management features and spectrum access request evaluation. The framework
utilizes a novel policy representation using OWL and PROV-O along with a
domain-specific reasoning implementation that mixes GeoSPARQL, OWL reasoning,
and knowledge graph traversal to evaluate incoming spectrum access requests and
explain how applicable policies were used. The framework is currently being
used to support live, over-the-air field exercises involving a diverse set of
federal and commercial radios, as a component of a prototype spectrum
management system.
| [
{
"version": "v1",
"created": "Sun, 8 Nov 2020 21:29:10 GMT"
}
] | 1,604,966,400,000 | [
[
"Santos",
"H.",
""
],
[
"Mulvehill",
"A.",
""
],
[
"Erickson",
"J. S.",
""
],
[
"McCusker",
"J. P.",
""
],
[
"Gordon",
"M.",
""
],
[
"Xie",
"O.",
""
],
[
"Stouffer",
"S.",
""
],
[
"Capraro",
"G.",
""
],
[
"Pidwerbetsky",
"A.",
""
],
[
"Burgess",
"J.",
""
],
[
"Berlinsky",
"A.",
""
],
[
"Turck",
"K.",
""
],
[
"Ashdown",
"J.",
""
],
[
"McGuinness",
"D. L.",
""
]
] |
2011.04166 | Pengcheng Zou | Zhao Li, Donghui Ding, Pengcheng Zou, Yu Gong, Xi Chen, Ji Zhang,
Jianliang Gao, Youxi Wu and Yucong Duan | Distant Supervision for E-commerce Query Segmentation via Attention
Network | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The booming online e-commerce platforms demand highly accurate approaches to
segment queries that carry the product requirements of consumers. Recent works
have shown that the supervised methods, especially those based on deep
learning, are attractive for achieving better performance on the problem of
query segmentation. However, the lack of labeled data is still a big challenge
for training a deep segmentation network, and the problem of Out-of-Vocabulary
(OOV) also adversely impacts the performance of query segmentation. Different
from query segmentation task in an open domain, e-commerce scenario can provide
external documents that are closely related to these queries. Thus, to deal
with the two challenges, we employ the idea of distant supervision and design a
novel method to find contexts in external documents and extract features from
these contexts. In this work, we propose a BiLSTM-CRF based model with an
attention module to encode external features, such that external contexts
information, which can be utilized naturally and effectively to help query
segmentation. Experiments on two datasets show the effectiveness of our
approach compared with several kinds of baselines.
| [
{
"version": "v1",
"created": "Mon, 9 Nov 2020 03:00:52 GMT"
}
] | 1,604,966,400,000 | [
[
"Li",
"Zhao",
""
],
[
"Ding",
"Donghui",
""
],
[
"Zou",
"Pengcheng",
""
],
[
"Gong",
"Yu",
""
],
[
"Chen",
"Xi",
""
],
[
"Zhang",
"Ji",
""
],
[
"Gao",
"Jianliang",
""
],
[
"Wu",
"Youxi",
""
],
[
"Duan",
"Yucong",
""
]
] |
2011.04333 | Nathan Grinsztajn | Nathan Grinsztajn, Olivier Beaumont, Emmanuel Jeannot, Philippe Preux | Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In practice, it is quite common to face combinatorial optimization problems
which contain uncertainty along with non-determinism and dynamicity. These
three properties call for appropriate algorithms; reinforcement learning (RL)
is dealing with them in a very natural way. Today, despite some efforts, most
real-life combinatorial optimization problems remain out of the reach of
reinforcement learning algorithms.
In this paper, we propose a reinforcement learning approach to solve a
realistic scheduling problem, and apply it to an algorithm commonly executed in
the high performance computing community, the Cholesky factorization. On the
contrary to static scheduling, where tasks are assigned to processors in a
predetermined ordering before the beginning of the parallel execution, our
method is dynamic: task allocations and their execution ordering are decided at
runtime, based on the system state and unexpected events, which allows much
more flexibility. To do so, our algorithm uses graph neural networks in
combination with an actor-critic algorithm (A2C) to build an adaptive
representation of the problem on the fly.
We show that this approach is competitive with state-of-the-art heuristics
used in high-performance computing runtime systems. Moreover, our algorithm
does not require an explicit model of the environment, but we demonstrate that
extra knowledge can easily be incorporated and improves performance. We also
exhibit key properties provided by this RL approach, and study its transfer
abilities to other instances.
| [
{
"version": "v1",
"created": "Mon, 9 Nov 2020 10:57:21 GMT"
}
] | 1,604,966,400,000 | [
[
"Grinsztajn",
"Nathan",
""
],
[
"Beaumont",
"Olivier",
""
],
[
"Jeannot",
"Emmanuel",
""
],
[
"Preux",
"Philippe",
""
]
] |
2011.04405 | Kushagra Chandak | Jiajing Ling, Kushagra Chandak, Akshat Kumar | Combining Propositional Logic Based Decision Diagrams with Decision
Making in Urban Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Solving multiagent problems can be an uphill task due to uncertainty in the
environment, partial observability, and scalability of the problem at hand.
Especially in an urban setting, there are more challenges since we also need to
maintain safety for all users while minimizing congestion of the agents as well
as their travel times. To this end, we tackle the problem of multiagent
pathfinding under uncertainty and partial observability where the agents are
tasked to move from their starting points to ending points while also
satisfying some constraints, e.g., low congestion, and model it as a multiagent
reinforcement learning problem. We compile the domain constraints using
propositional logic and integrate them with the RL algorithms to enable fast
simulation for RL.
| [
{
"version": "v1",
"created": "Mon, 9 Nov 2020 13:13:54 GMT"
},
{
"version": "v2",
"created": "Tue, 10 Nov 2020 05:46:56 GMT"
}
] | 1,605,052,800,000 | [
[
"Ling",
"Jiajing",
""
],
[
"Chandak",
"Kushagra",
""
],
[
"Kumar",
"Akshat",
""
]
] |
2011.04428 | Sofia Maria Nikolakaki | Sofia Maria Nikolakaki, Mingxiang Cai, Evimaria Terzi | Finding teams that balance expert load and task coverage | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rise of online labor markets (e.g., Freelancer, Guru and Upwork) has
ignited a lot of research on team formation, where experts acquiring different
skills form teams to complete tasks. The core idea in this line of work has
been the strict requirement that the team of experts assigned to complete a
given task should contain a superset of the skills required by the task.
However, in many applications the required skills are often a wishlist of the
entity that posts the task and not all of the skills are absolutely necessary.
Thus, in our setting we relax the complete coverage requirement and we allow
for tasks to be partially covered by the formed teams, assuming that the
quality of task completion is proportional to the fraction of covered skills
per task. At the same time, we assume that when multiple tasks need to be
performed, the less the load of an expert the better the performance. We
combine these two high-level objectives into one and define the BalancedTA
problem. We also consider a generalization of this problem where each task
consists of required and optional skills. In this setting, our objective is the
same under the constraint that all required skills should be covered. From the
technical point of view, we show that the BalancedTA problem (and its variant)
is NP-hard and design efficient heuristics for solving it in practice. Using
real datasets from three online market places, Freelancer, Guru and Upwork we
demonstrate the efficiency of our methods and the practical utility of our
framework.
| [
{
"version": "v1",
"created": "Tue, 3 Nov 2020 18:04:15 GMT"
}
] | 1,604,966,400,000 | [
[
"Nikolakaki",
"Sofia Maria",
""
],
[
"Cai",
"Mingxiang",
""
],
[
"Terzi",
"Evimaria",
""
]
] |
2011.04527 | Alun Preece | Frank Stein, Alun Preece | AAAI FSS-20: Artificial Intelligence in Government and Public Sector
Proceedings | Post-symposium proceedings including 13 papers | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Proceedings of the AAAI Fall Symposium on Artificial Intelligence in
Government and Public Sector, Washington, DC, USA, November 13-14, 2020
| [
{
"version": "v1",
"created": "Mon, 9 Nov 2020 16:08:42 GMT"
},
{
"version": "v2",
"created": "Fri, 8 Oct 2021 15:35:11 GMT"
}
] | 1,633,910,400,000 | [
[
"Stein",
"Frank",
""
],
[
"Preece",
"Alun",
""
]
] |
2011.04590 | Banafsheh Rafiee | Banafsheh Rafiee, Zaheer Abbas, Sina Ghiassian, Raksha Kumaraswamy,
Richard Sutton, Elliot Ludvig, Adam White | From Eye-blinks to State Construction: Diagnostic Benchmarks for Online
Representation Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present three new diagnostic prediction problems inspired by
classical-conditioning experiments to facilitate research in online prediction
learning. Experiments in classical conditioning show that animals such as
rabbits, pigeons, and dogs can make long temporal associations that enable
multi-step prediction. To replicate this remarkable ability, an agent must
construct an internal state representation that summarizes its interaction
history. Recurrent neural networks can automatically construct state and learn
temporal associations. However, the current training methods are prohibitively
expensive for online prediction -- continual learning on every time step --
which is the focus of this paper. Our proposed problems test the learning
capabilities that animals readily exhibit and highlight the limitations of the
current recurrent learning methods. While the proposed problems are nontrivial,
they are still amenable to extensive testing and analysis in the small-compute
regime, thereby enabling researchers to study issues in isolation, ultimately
accelerating progress towards scalable online representation learning methods.
| [
{
"version": "v1",
"created": "Mon, 9 Nov 2020 17:41:13 GMT"
},
{
"version": "v2",
"created": "Fri, 13 Nov 2020 17:25:23 GMT"
},
{
"version": "v3",
"created": "Thu, 18 Feb 2021 01:52:57 GMT"
},
{
"version": "v4",
"created": "Mon, 10 Oct 2022 18:11:11 GMT"
}
] | 1,665,532,800,000 | [
[
"Rafiee",
"Banafsheh",
""
],
[
"Abbas",
"Zaheer",
""
],
[
"Ghiassian",
"Sina",
""
],
[
"Kumaraswamy",
"Raksha",
""
],
[
"Sutton",
"Richard",
""
],
[
"Ludvig",
"Elliot",
""
],
[
"White",
"Adam",
""
]
] |
2011.04797 | Dongsheng Luo | Dongsheng Luo, Yuchen Bian, Xiang Zhang, Jun Huan | Attentive Social Recommendation: Towards User And Item Diversities | 8 Pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Social recommendation system is to predict unobserved user-item rating values
by taking advantage of user-user social relation and user-item ratings.
However, user/item diversities in social recommendations are not well utilized
in the literature. Especially, inter-factor (social and rating factors)
relations and distinct rating values need taking into more consideration. In
this paper, we propose an attentive social recommendation system (ASR) to
address this issue from two aspects. First, in ASR, Rec-conv graph network
layers are proposed to extract the social factor, user-rating and item-rated
factors and then automatically assign contribution weights to aggregate these
factors into the user/item embedding vectors. Second, a disentangling strategy
is applied for diverse rating values. Extensive experiments on benchmarks
demonstrate the effectiveness and advantages of our ASR.
| [
{
"version": "v1",
"created": "Mon, 9 Nov 2020 21:57:45 GMT"
},
{
"version": "v2",
"created": "Sun, 15 Nov 2020 00:27:52 GMT"
}
] | 1,605,571,200,000 | [
[
"Luo",
"Dongsheng",
""
],
[
"Bian",
"Yuchen",
""
],
[
"Zhang",
"Xiang",
""
],
[
"Huan",
"Jun",
""
]
] |
2011.05174 | Claire Pagetti | Arthur Clavi\`ere, Eric Asselin, Christophe Garion (ISAE-SUPAERO),
Claire Pagetti (ANITI) | Safety Verification of Neural Network Controlled Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a system-level approach for verifying the safety of
neural network controlled systems, combining a continuous-time physical system
with a discrete-time neural network based controller. We assume a generic model
for the controller that can capture both simple and complex behaviours
involving neural networks. Based on this model, we perform a reachability
analysis that soundly approximates the reachable states of the overall system,
allowing to achieve a formal proof of safety. To this end, we leverage both
validated simulation to approximate the behaviour of the physical system and
abstract interpretation to approximate the behaviour of the controller. We
evaluate the applicability of our approach using a real-world use case.
Moreover, we show that our approach can provide valuable information when the
system cannot be proved totally safe.
| [
{
"version": "v1",
"created": "Tue, 10 Nov 2020 15:26:38 GMT"
}
] | 1,605,052,800,000 | [
[
"Clavière",
"Arthur",
"",
"ISAE-SUPAERO"
],
[
"Asselin",
"Eric",
"",
"ISAE-SUPAERO"
],
[
"Garion",
"Christophe",
"",
"ISAE-SUPAERO"
],
[
"Pagetti",
"Claire",
"",
"ANITI"
]
] |
2011.05622 | Chengpeng Hu | Chengpeng Hu, Ziqi Wang, Tianye Shu, Hao Tong, Julian Togelius, Xin
Yao and Jialin Liu | Reinforcement Learning with Dual-Observation for General Video Game
Playing | This work has been accepted by the IEEE Transactions on Games on
March 21, 2022 | null | 10.1109/TG.2022.3164242 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement learning algorithms have performed well in playing challenging
board and video games. More and more studies focus on improving the
generalisation ability of reinforcement learning algorithms. The General Video
Game AI Learning Competition aims to develop agents capable of learning to play
different game levels that were unseen during training. This paper summarises
the five years' General Video Game AI Learning Competition editions. At each
edition, three new games were designed. The training and test levels were
designed separately in the first three editions. Since 2020, three test levels
of each game were generated by perturbing or combining two training levels.
Then, we present a novel reinforcement learning technique with dual-observation
for general video game playing, assuming that it is more likely to observe
similar local information in different levels rather than global information.
Instead of directly inputting a single, raw pixel-based screenshot of the
current game screen, our proposed general technique takes the encoded,
transformed global and local observations of the game screen as two
simultaneous inputs, aiming at learning local information for playing new
levels. Our proposed technique is implemented with three state-of-the-art
reinforcement learning algorithms and tested on the game set of the 2020
General Video Game AI Learning Competition. Ablation studies show the
outstanding performance of using encoded, transformed global and local
observations as input.
| [
{
"version": "v1",
"created": "Wed, 11 Nov 2020 08:28:20 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Jan 2022 12:16:05 GMT"
},
{
"version": "v3",
"created": "Wed, 5 Jan 2022 02:08:06 GMT"
},
{
"version": "v4",
"created": "Thu, 31 Mar 2022 08:11:52 GMT"
}
] | 1,648,771,200,000 | [
[
"Hu",
"Chengpeng",
""
],
[
"Wang",
"Ziqi",
""
],
[
"Shu",
"Tianye",
""
],
[
"Tong",
"Hao",
""
],
[
"Togelius",
"Julian",
""
],
[
"Yao",
"Xin",
""
],
[
"Liu",
"Jialin",
""
]
] |
2011.06102 | Aya Abdelsalam Ismail | Aya Abdelsalam Ismail, Mahmudul Hasan, Faisal Ishtiaq | Improving Multimodal Accuracy Through Modality Pre-training and
Attention | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Training a multimodal network is challenging and it requires complex
architectures to achieve reasonable performance. We show that one reason for
this phenomena is the difference between the convergence rate of various
modalities. We address this by pre-training modality-specific sub-networks in
multimodal architectures independently before end-to-end training of the entire
network. Furthermore, we show that the addition of an attention mechanism
between sub-networks after pre-training helps identify the most important
modality during ambiguous scenarios boosting the performance. We demonstrate
that by performing these two tricks a simple network can achieve similar
performance to a complicated architecture that is significantly more expensive
to train on multiple tasks including sentiment analysis, emotion recognition,
and speaker trait recognition.
| [
{
"version": "v1",
"created": "Wed, 11 Nov 2020 22:31:27 GMT"
}
] | 1,605,225,600,000 | [
[
"Ismail",
"Aya Abdelsalam",
""
],
[
"Hasan",
"Mahmudul",
""
],
[
"Ishtiaq",
"Faisal",
""
]
] |
2011.06156 | Baogang Hu | Bao-Gang Hu and Han-Bing Qu | Generalized Constraints as A New Mathematical Problem in Artificial
Intelligence: A Review and Perspective | 20 pages, 16 figures, 3 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this comprehensive review, we describe a new mathematical problem in
artificial intelligence (AI) from a mathematical modeling perspective,
following the philosophy stated by Rudolf E. Kalman that "Once you get the
physics right, the rest is mathematics". The new problem is called "Generalized
Constraints (GCs)", and we adopt GCs as a general term to describe any type of
prior information in modelings. To understand better about GCs to be a general
problem, we compare them with the conventional constraints (CCs) and list their
extra challenges over CCs. In the construction of AI machines, we basically
encounter more often GCs for modeling, rather than CCs with well-defined forms.
Furthermore, we discuss the ultimate goals of AI and redefine transparent,
interpretable, and explainable AI in terms of comprehension levels about
machines. We review the studies in relation to the GC problems although most of
them do not take the notion of GCs. We demonstrate that if AI machines are
simplified by a coupling with both knowledge-driven submodel and data-driven
submodel, GCs will play a critical role in a knowledge-driven submodel as well
as in the coupling form between the two submodels. Examples are given to show
that the studies in view of a generalized constraint problem will help us
perceive and explore novel subjects in AI, or even in mathematics, such as
generalized constraint learning (GCL).
| [
{
"version": "v1",
"created": "Thu, 12 Nov 2020 01:47:31 GMT"
}
] | 1,605,225,600,000 | [
[
"Hu",
"Bao-Gang",
""
],
[
"Qu",
"Han-Bing",
""
]
] |
2011.06300 | Bahadorreza Ofoghi | Bahadorreza Ofoghi, Vicky Mak, John Yearwood | A Knowledge Representation Approach to Automated Mathematical Modelling | 10 pages, 2 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a new mixed-integer linear programming (MILP) model
ontology and a novel constraint typology of MILP formulations. MILP is a
commonly used mathematical programming technique for modelling and solving
real-life scheduling, routing, planning, resource allocation, and timetabling
optimization problems providing optimized business solutions for industry
sectors such as manufacturing, agriculture, defence, healthcare, medicine,
energy, finance, and transportation. Despite the numerous real-life
Combinatorial Optimization Problems found and solved and millions yet to be
discovered and formulated, the number of types of constraints (the building
blocks of a MILP) is relatively small. In the search for a suitable
machine-readable knowledge representation structure for MILPs, we propose an
optimization modelling tree built based upon an MILP model ontology that can be
used as a guide for automated systems to elicit an MILP model from end-users on
their combinatorial business optimization problems. Our ultimate aim is to
develop a machine-readable knowledge representation for MILP that allows us to
map an end-user's natural language description of the business optimization
problem to an MILP formal specification as a first step towards automated
mathematical modelling.
| [
{
"version": "v1",
"created": "Thu, 12 Nov 2020 10:29:57 GMT"
},
{
"version": "v2",
"created": "Sun, 28 Feb 2021 07:48:22 GMT"
}
] | 1,614,643,200,000 | [
[
"Ofoghi",
"Bahadorreza",
""
],
[
"Mak",
"Vicky",
""
],
[
"Yearwood",
"John",
""
]
] |
2011.06363 | Christopher Bamford | Chris Bamford, Shengyi Huang, Simon Lucas | Griddly: A platform for AI research in games | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In recent years, there have been immense breakthroughs in Game AI research,
particularly with Reinforcement Learning (RL). Despite their success, the
underlying games are usually implemented with their own preset environments and
game mechanics, thus making it difficult for researchers to prototype different
game environments. However, testing the RL agents against a variety of game
environments is critical for recent effort to study generalization in RL and
avoid the problem of overfitting that may otherwise occur. In this paper, we
present Griddly as a new platform for Game AI research that provides a unique
combination of highly configurable games, different observer types and an
efficient C++ core engine. Additionally, we present a series of baseline
experiments to study the effect of different observation configurations and
generalization ability of RL agents.
| [
{
"version": "v1",
"created": "Thu, 12 Nov 2020 13:23:31 GMT"
},
{
"version": "v2",
"created": "Sat, 21 Nov 2020 17:35:33 GMT"
},
{
"version": "v3",
"created": "Tue, 12 Jul 2022 18:40:29 GMT"
}
] | 1,657,756,800,000 | [
[
"Bamford",
"Chris",
""
],
[
"Huang",
"Shengyi",
""
],
[
"Lucas",
"Simon",
""
]
] |
2011.06665 | Ole Meyer | Jonas Andrulis, Ole Meyer, Gr\'egory Schott, Samuel Weinbach and
Volker Gruhn | Domain-Level Explainability -- A Challenge for Creating Trust in
Superhuman AI Strategies | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | For strategic problems, intelligent systems based on Deep Reinforcement
Learning (DRL) have demonstrated an impressive ability to learn advanced
solutions that can go far beyond human capabilities, especially when dealing
with complex scenarios. While this creates new opportunities for the
development of intelligent assistance systems with groundbreaking
functionalities, applying this technology to real-world problems carries
significant risks and therefore requires trust in their transparency and
reliability. With superhuman strategies being non-intuitive and complex by
definition and real-world scenarios prohibiting a reliable performance
evaluation, the key components for trust in these systems are difficult to
achieve. Explainable AI (XAI) has successfully increased transparency for
modern AI systems through a variety of measures, however, XAI research has not
yet provided approaches enabling domain level insights for expert users in
strategic situations. In this paper, we discuss the existence of superhuman
DRL-based strategies, their properties, the requirements and challenges for
transforming them into real-world environments, and the implications for trust
through explainability as a key technology.
| [
{
"version": "v1",
"created": "Thu, 12 Nov 2020 21:42:02 GMT"
}
] | 1,605,484,800,000 | [
[
"Andrulis",
"Jonas",
""
],
[
"Meyer",
"Ole",
""
],
[
"Schott",
"Grégory",
""
],
[
"Weinbach",
"Samuel",
""
],
[
"Gruhn",
"Volker",
""
]
] |
2011.06780 | Mingcheng Zuo | Mingcheng Zuo, Guangming Dai, Lei Peng, Zhe Tang | A differential evolution-based optimization tool for interplanetary
transfer trajectory design | The algorithm has been developed, and the results need a change | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The extremely sensitive and highly nonlinear search space of interplanetary
transfer trajectory design bring about big challenges on global optimization.
As a representative, the current known best solution of the global trajectory
optimization problem (GTOP) designed by the European space agency (ESA) is very
hard to be found. To deal with this difficulty, a powerful differential
evolution-based optimization tool named COoperative Differential Evolution
(CODE) is proposed in this paper. CODE employs a two-stage evolutionary
process, which concentrates on learning global structure in the earlier
process, and tends to self-adaptively learn the structures of different local
spaces. Besides, considering the spatial distribution of global optimum on
different problems and the gradient information on different variables, a
multiple boundary check technique has been employed. Also, Covariance Matrix
Adaptation Evolutionary Strategies (CMA-ES) is used as a local optimizer. The
previous studies have shown that a specific swarm intelligent optimization
algorithm usually can solve only one or two GTOP problems. However, the
experimental test results show that CODE can find the current known best
solutions of Cassini1 and Sagas directly, and the cooperation with CMA-ES can
solve Cassini2, GTOC1, Messenger (reduced) and Rosetta. For the most
complicated Messenger (full) problem, even though CODE cannot find the current
known best solution, the found best solution with objective function equaling
to 3.38 km/s is still a level that other swarm intelligent algorithms cannot
easily reach.
| [
{
"version": "v1",
"created": "Fri, 13 Nov 2020 06:35:17 GMT"
},
{
"version": "v2",
"created": "Tue, 17 Nov 2020 07:30:15 GMT"
},
{
"version": "v3",
"created": "Tue, 13 Apr 2021 13:55:31 GMT"
}
] | 1,618,358,400,000 | [
[
"Zuo",
"Mingcheng",
""
],
[
"Dai",
"Guangming",
""
],
[
"Peng",
"Lei",
""
],
[
"Tang",
"Zhe",
""
]
] |
2011.07027 | Joel Leibo | Charles Beattie, Thomas K\"oppe, Edgar A. Du\'e\~nez-Guzm\'an, Joel Z.
Leibo | DeepMind Lab2D | 7 pages, 2 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present DeepMind Lab2D, a scalable environment simulator for artificial
intelligence research that facilitates researcher-led experimentation with
environment design. DeepMind Lab2D was built with the specific needs of
multi-agent deep reinforcement learning researchers in mind, but it may also be
useful beyond that particular subfield.
| [
{
"version": "v1",
"created": "Fri, 13 Nov 2020 17:29:26 GMT"
},
{
"version": "v2",
"created": "Sat, 12 Dec 2020 20:56:32 GMT"
}
] | 1,607,990,400,000 | [
[
"Beattie",
"Charles",
""
],
[
"Köppe",
"Thomas",
""
],
[
"Duéñez-Guzmán",
"Edgar A.",
""
],
[
"Leibo",
"Joel Z.",
""
]
] |
2011.07035 | Blake Camp | Blake Camp, Jaya Krishna Mandivarapu, Rolando Estrada | Continual Learning with Deep Artificial Neurons | null | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Neurons in real brains are enormously complex computational units. Among
other things, they're responsible for transforming inbound electro-chemical
vectors into outbound action potentials, updating the strengths of intermediate
synapses, regulating their own internal states, and modulating the behavior of
other nearby neurons. One could argue that these cells are the only things
exhibiting any semblance of real intelligence. It is odd, therefore, that the
machine learning community has, for so long, relied upon the assumption that
this complexity can be reduced to a simple sum and fire operation. We ask,
might there be some benefit to substantially increasing the computational power
of individual neurons in artificial systems? To answer this question, we
introduce Deep Artificial Neurons (DANs), which are themselves realized as deep
neural networks. Conceptually, we embed DANs inside each node of a traditional
neural network, and we connect these neurons at multiple synaptic sites,
thereby vectorizing the connections between pairs of cells. We demonstrate that
it is possible to meta-learn a single parameter vector, which we dub a neuronal
phenotype, shared by all DANs in the network, which facilitates a
meta-objective during deployment. Here, we isolate continual learning as our
meta-objective, and we show that a suitable neuronal phenotype can endow a
single network with an innate ability to update its synapses with minimal
forgetting, using standard backpropagation, without experience replay, nor
separate wake/sleep phases. We demonstrate this ability on sequential
non-linear regression tasks.
| [
{
"version": "v1",
"created": "Fri, 13 Nov 2020 17:50:10 GMT"
}
] | 1,605,484,800,000 | [
[
"Camp",
"Blake",
""
],
[
"Mandivarapu",
"Jaya Krishna",
""
],
[
"Estrada",
"Rolando",
""
]
] |
2011.07507 | Md. Mushfiqur Rahman | Md. Mushfiqur Rahman, Sabah Binte Noor, Fazlul Hasan Siddiqui | Automated Large-scale Class Scheduling in MiniZinc | null | null | 10.1109/STI50764.2020.9350485 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Class Scheduling is a highly constrained task. Educational institutes spend a
lot of resources, in the form of time and manual computation, to find a
satisficing schedule that fulfills all the requirements. A satisficing class
schedule accommodates all the students to all their desired courses at
convenient timing. The scheduler also needs to take into account the
availability of course teachers on the given slots. With the added limitation
of available classrooms, the number of solutions satisfying all constraints in
this huge search-space, further decreases.
This paper proposes an efficient system to generate class schedules that can
fulfill every possible need of a typical university. Though it is primarily a
fixed-credit scheduler, it can be adjusted for open-credit systems as well. The
model is designed in MiniZinc and solved using various off-the-shelf solvers.
The proposed scheduling system can find a balanced schedule for a
moderate-sized educational institute in less than a minute.
| [
{
"version": "v1",
"created": "Sun, 15 Nov 2020 12:02:52 GMT"
}
] | 1,613,692,800,000 | [
[
"Rahman",
"Md. Mushfiqur",
""
],
[
"Noor",
"Sabah Binte",
""
],
[
"Siddiqui",
"Fazlul Hasan",
""
]
] |
2011.07509 | Md. Mushfiqur Rahman | Md. Mushfiqur Rahman, Nahian Muhtasim Zahin, Kazi Raiyan Mahmud, Md.
Azmaeen Bin Ansar | Automated Intersection Management with MiniZinc | null | null | 10.1109/STI50764.2020.9350408 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Ill-managed intersections are the primary reasons behind the increasing
traffic problem in urban areas, leading to nonoptimal traffic-flow and
unnecessary deadlocks. In this paper, we propose an automated intersection
management system that extracts data from a well-defined grid of sensors and
optimizes traffic flow by controlling traffic signals. The data extraction
mechanism is independent of the optimization algorithm and this paper primarily
emphasizes the later one. We have used MiniZinc modeling language to define our
system as a constraint satisfaction problem which can be solved using any
off-the-shelf solver. The proposed system performs much better than the systems
currently in use. Our system reduces the mean waiting time and standard
deviation of the waiting time of vehicles and avoids deadlocks.
| [
{
"version": "v1",
"created": "Sun, 15 Nov 2020 12:11:05 GMT"
}
] | 1,613,692,800,000 | [
[
"Rahman",
"Md. Mushfiqur",
""
],
[
"Zahin",
"Nahian Muhtasim",
""
],
[
"Mahmud",
"Kazi Raiyan",
""
],
[
"Ansar",
"Md. Azmaeen Bin",
""
]
] |
2011.07693 | Uwe Aickelin | J Navrro, C Wagner, Uwe Aickelin, L Green, R Ashford | Measuring agreement on linguistic expressions in medical treatment
scenarios | IEEE Symposium on Computational Intelligence, 6-9 Dec 2016, Athens,
Greece | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Quality of life assessment represents a key process of deciding treatment
success and viability. As such, patients' perceptions of their functional
status and well-being are important inputs for impairment assessment. Given
that patient completed questionnaires are often used to assess patient status
and determine future treatment options, it is important to know the level of
agreement of the words used by patients and different groups of medical
professionals. In this paper, we propose a measure called the Agreement Ratio
which provides a ratio of overall agreement when modelling words through Fuzzy
Sets (FSs). The measure has been specifically designed for assessing this
agreement in fuzzy sets which are generated from data such as patient
responses. The measure relies on using the Jaccard Similarity Measure for
comparing the different levels of agreement in the FSs generated.
| [
{
"version": "v1",
"created": "Mon, 16 Nov 2020 02:36:30 GMT"
}
] | 1,605,571,200,000 | [
[
"Navrro",
"J",
""
],
[
"Wagner",
"C",
""
],
[
"Aickelin",
"Uwe",
""
],
[
"Green",
"L",
""
],
[
"Ashford",
"R",
""
]
] |
2011.07751 | Pengpeng Shao | Pengpeng Shao, Guohua Yang, Dawei Zhang, Jianhua Tao, Feihu Che, Tong
Liu | Tucker decomposition-based Temporal Knowledge Graph Completion | null | null | null | 3467828 | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Knowledge graphs have been demonstrated to be an effective tool for numerous
intelligent applications. However, a large amount of valuable knowledge still
exists implicitly in the knowledge graphs. To enrich the existing knowledge
graphs, recent years witness that many algorithms for link prediction and
knowledge graphs embedding have been designed to infer new facts. But most of
these studies focus on the static knowledge graphs and ignore the temporal
information that reflects the validity of knowledge. Developing the model for
temporal knowledge graphs completion is an increasingly important task. In this
paper, we build a new tensor decomposition model for temporal knowledge graphs
completion inspired by the Tucker decomposition of order 4 tensor. We
demonstrate that the proposed model is fully expressive and report
state-of-the-art results for several public benchmarks. Additionally, we
present several regularization schemes to improve the strategy and study their
impact on the proposed model. Experimental studies on three temporal datasets
(i.e. ICEWS2014, ICEWS2005-15, GDELT) justify our design and demonstrate that
our model outperforms baselines with an explicit margin on link prediction
task.
| [
{
"version": "v1",
"created": "Mon, 16 Nov 2020 07:05:52 GMT"
}
] | 1,605,571,200,000 | [
[
"Shao",
"Pengpeng",
""
],
[
"Yang",
"Guohua",
""
],
[
"Zhang",
"Dawei",
""
],
[
"Tao",
"Jianhua",
""
],
[
"Che",
"Feihu",
""
],
[
"Liu",
"Tong",
""
]
] |
2011.08028 | Giuseppe Pirr\`o | Giuseppe Pirr\`o | Fact Checking via Path Embedding and Aggregation | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Knowledge graphs (KGs) are a useful source of background knowledge to
(dis)prove facts of the form (s, p, o). Finding paths between s and o is the
cornerstone of several fact-checking approaches. While paths are useful to
(visually) explain why a given fact is true or false, it is not completely
clear how to identify paths that are most relevant to a fact, encode them and
weigh their importance. The goal of this paper is to present the Fact Checking
via path Embedding and Aggregation (FEA) system. FEA starts by carefully
collecting the paths between s and o that are most semantically related to the
domain of p. However, instead of directly working with this subset of all
paths, it learns vectorized path representations, aggregates them according to
different strategies, and use them to finally (dis)prove a fact. We conducted a
large set of experiments on a variety of KGs and found that our hybrid solution
brings some benefits in terms of performance.
| [
{
"version": "v1",
"created": "Mon, 16 Nov 2020 15:27:38 GMT"
}
] | 1,605,571,200,000 | [
[
"Pirrò",
"Giuseppe",
""
]
] |
2011.08182 | Uwe Aickelin | Bahram Farhadinia, Uwe Aickelin, Hadi Akbarzadeh Khorshidi | Uncertainty measures for probabilistic hesitant fuzzy sets in multiple
criteria decision making | International Journal of Intelligent Systems | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This contribution reviews critically the existing entropy measures for
probabilistic hesitant fuzzy sets (PHFSs), and demonstrates that these entropy
measures fail to effectively distinguish a variety of different PHFSs in some
cases. In the sequel, we develop a new axiomatic framework of entropy measures
for probabilistic hesitant fuzzy elements (PHFEs) by considering two facets of
uncertainty associated with PHFEs which are known as fuzziness and
nonspecificity. Respect to each kind of uncertainty, a number of formulae are
derived to permit flexible selection of PHFE entropy measures. Moreover, based
on the proposed PHFE entropy measures, we introduce some entropy-based distance
measures which are used in the portion of comparative analysis.
| [
{
"version": "v1",
"created": "Mon, 16 Nov 2020 08:25:18 GMT"
}
] | 1,605,657,600,000 | [
[
"Farhadinia",
"Bahram",
""
],
[
"Aickelin",
"Uwe",
""
],
[
"Khorshidi",
"Hadi Akbarzadeh",
""
]
] |
2011.08183 | Uwe Aickelin | B Farhadinia, Uwe Aickelin, HA Khorshidi | Higher order hesitant fuzzy Choquet integral operator and its
application to multiple criteria decision making | Iranian Journal of Fuzzy Systems, Volume, 2002, Issue 5687 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Generally, the criteria involved in a decision making problem are interactive
or inter-dependent, and therefore aggregating them by the use of traditional
operators which are based on additive measures is not logical. This verifies
that we have to implement fuzzy measures for modelling the interaction
phenomena among the criteria.On the other hand, based on the recent extension
of hesitant fuzzy set, called higher order hesitant fuzzy set (HOHFS) which
allows the membership of a given element to be defined in forms of several
possible generalized types of fuzzy set, we encourage to propose the higher
order hesitant fuzzy (HOHF) Choquet integral operator. This concept not only
considers the importance of the higher order hesitant fuzzy arguments, but also
it can reflect the correlations among those arguments. Then,a detailed
discussion on the aggregation properties of the HOHF Choquet integral operator
will be presented.To enhance the application of HOHF Choquet integral operator
in decision making, we first assess the appropriate energy policy for the
socio-economic development. Then, the efficiency of the proposed HOHF Choquet
integral operator-based technique over a number of exiting techniques is
further verified by employing another decision making problem associated with
the technique of TODIM (an acronym in Portuguese of Interactive and
Multicriteria Decision Making).
| [
{
"version": "v1",
"created": "Mon, 16 Nov 2020 08:52:55 GMT"
}
] | 1,605,657,600,000 | [
[
"Farhadinia",
"B",
""
],
[
"Aickelin",
"Uwe",
""
],
[
"Khorshidi",
"HA",
""
]
] |
2011.08733 | Amruta Yelamanchili | Jagriti Agrawal and Amruta Yelamanchili and Steve Chien | Using Explainable Scheduling for the Mars 2020 Rover Mission | Submitted to the International Workshop of Explainable AI Planning
(XAIP) at the International Conference on Automated Planning and Scheduling
(ICAPS) 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the reasoning behind the behavior of an automated scheduling
system is essential to ensure that it will be trusted and consequently used to
its full capabilities in critical applications. In cases where a scheduler
schedules activities in an invalid location, it is usually easy for the user to
infer the missing constraint by inspecting the schedule with the invalid
activity to determine the missing constraint. If a scheduler fails to schedule
activities because constraints could not be satisfied, determining the cause
can be more challenging. In such cases it is important to understand which
constraints caused the activities to fail to be scheduled and how to alter
constraints to achieve the desired schedule. In this paper, we describe such a
scheduling system for NASA's Mars 2020 Perseverance Rover, as well as
Crosscheck, an explainable scheduling tool that explains the scheduler
behavior. The scheduling system and Crosscheck are the baseline for operational
use to schedule activities for the Mars 2020 rover. As we describe, the
scheduler generates a schedule given a set of activities and their constraints
and Crosscheck: (1) provides a visual representation of the generated schedule;
(2) analyzes and explains why activities failed to schedule given the
constraints provided; and (3) provides guidance on potential constraint
relaxations to enable the activities to schedule in future scheduler runs.
| [
{
"version": "v1",
"created": "Tue, 17 Nov 2020 16:10:49 GMT"
}
] | 1,605,657,600,000 | [
[
"Agrawal",
"Jagriti",
""
],
[
"Yelamanchili",
"Amruta",
""
],
[
"Chien",
"Steve",
""
]
] |
2011.09006 | Stylianos Loukas Vasileiou | Stylianos Loukas Vasileiou, William Yeoh, Tran Cao Son | On the Relationship Between KR Approaches for Explainable Planning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we build upon notions from knowledge representation and
reasoning (KR) to expand a preliminary logic-based framework that characterizes
the model reconciliation problem for explainable planning. We also provide a
detailed exposition on the relationship between similar KR techniques, such as
abductive explanations and belief change, and their applicability to
explainable planning.
| [
{
"version": "v1",
"created": "Tue, 17 Nov 2020 23:57:23 GMT"
},
{
"version": "v2",
"created": "Thu, 19 Nov 2020 01:37:43 GMT"
},
{
"version": "v3",
"created": "Wed, 16 Dec 2020 18:57:09 GMT"
}
] | 1,608,163,200,000 | [
[
"Vasileiou",
"Stylianos Loukas",
""
],
[
"Yeoh",
"William",
""
],
[
"Son",
"Tran Cao",
""
]
] |
2011.09020 | Rong Zhu | Ziniu Wu, Rong Zhu, Andreas Pfadler, Yuxing Han, Jiangneng Li,
Zhengping Qian, Kai Zeng, Jingren Zhou | FSPN: A New Class of Probabilistic Graphical Model | 16 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We introduce factorize sum split product networks (FSPNs), a new class of
probabilistic graphical models (PGMs). FSPNs are designed to overcome the
drawbacks of existing PGMs in terms of estimation accuracy and inference
efficiency. Specifically, Bayesian networks (BNs) have low inference speed and
performance of tree structured sum product networks(SPNs) significantly
degrades in presence of highly correlated variables. FSPNs absorb their
advantages by adaptively modeling the joint distribution of variables according
to their dependence degree, so that one can simultaneously attain the two
desirable goals: high estimation accuracy and fast inference speed. We present
efficient probability inference and structure learning algorithms for FSPNs,
along with a theoretical analysis and extensive evaluation evidence. Our
experimental results on synthetic and benchmark datasets indicate the
superiority of FSPN over other PGMs.
| [
{
"version": "v1",
"created": "Wed, 18 Nov 2020 01:11:55 GMT"
},
{
"version": "v2",
"created": "Fri, 20 Nov 2020 08:22:09 GMT"
}
] | 1,606,089,600,000 | [
[
"Wu",
"Ziniu",
""
],
[
"Zhu",
"Rong",
""
],
[
"Pfadler",
"Andreas",
""
],
[
"Han",
"Yuxing",
""
],
[
"Li",
"Jiangneng",
""
],
[
"Qian",
"Zhengping",
""
],
[
"Zeng",
"Kai",
""
],
[
"Zhou",
"Jingren",
""
]
] |
2011.09353 | Mihai Codescu | Bernd Krieg-Br\"uckner and Till Mossakowski and Mihai Codescu | Generic Ontology Design Patterns: Roles and Change over Time | To appear in Advances in Pattern-based Ontology Engineering. Studies
on the Semantic Web, IOS Press, Amsterdam | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | In this chapter we propose Generic Ontology Design Patterns, GODPs, as a
methodology for representing and instantiating ontology design patterns in a
way that is adaptable, and allows domain experts (and other users) to safely
use them without cluttering their ontologies.
| [
{
"version": "v1",
"created": "Wed, 18 Nov 2020 15:40:13 GMT"
}
] | 1,607,644,800,000 | [
[
"Krieg-Brückner",
"Bernd",
""
],
[
"Mossakowski",
"Till",
""
],
[
"Codescu",
"Mihai",
""
]
] |
2011.09533 | Christian Schroeder de Witt | Christian Schroeder de Witt, Tarun Gupta, Denys Makoviichuk, Viktor
Makoviychuk, Philip H.S. Torr, Mingfei Sun, Shimon Whiteson | Is Independent Learning All You Need in the StarCraft Multi-Agent
Challenge? | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Most recently developed approaches to cooperative multi-agent reinforcement
learning in the \emph{centralized training with decentralized execution}
setting involve estimating a centralized, joint value function. In this paper,
we demonstrate that, despite its various theoretical shortcomings, Independent
PPO (IPPO), a form of independent learning in which each agent simply estimates
its local value function, can perform just as well as or better than
state-of-the-art joint learning approaches on popular multi-agent benchmark
suite SMAC with little hyperparameter tuning. We also compare IPPO to several
variants; the results suggest that IPPO's strong performance may be due to its
robustness to some forms of environment non-stationarity.
| [
{
"version": "v1",
"created": "Wed, 18 Nov 2020 20:29:59 GMT"
}
] | 1,605,830,400,000 | [
[
"de Witt",
"Christian Schroeder",
""
],
[
"Gupta",
"Tarun",
""
],
[
"Makoviichuk",
"Denys",
""
],
[
"Makoviychuk",
"Viktor",
""
],
[
"Torr",
"Philip H. S.",
""
],
[
"Sun",
"Mingfei",
""
],
[
"Whiteson",
"Shimon",
""
]
] |
2011.09644 | Karthik Valmeekam | Karthik Valmeekam, Sarath Sreedharan, Sailik Sengupta, Subbarao
Kambhampati | RADAR-X: An Interactive Mixed Initiative Planning Interface Pairing
Contrastive Explanations and Revised Plan Suggestions | Accepted at ICAPS 2022 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decision support systems seek to enable informed decision-making. In the
recent years, automated planning techniques have been leveraged to empower such
systems to better aid the human-in-the-loop. The central idea for such decision
support systems is to augment the capabilities of the human-in-the-loop with
automated planning techniques and enhance the quality of decision-making. In
addition to providing planning support, effective decision support systems must
be able to provide intuitive explanations based on specific user queries for
proposed decisions to its end users. Using this as motivation, we present our
decision support system RADAR-X that showcases the ability to engage the user
in an interactive explanatory dialogue by first enabling them to specify an
alternative to a proposed decision (which we refer to as foils), and then
providing contrastive explanations to these user-specified foils which helps
the user understand why a specific plan was chosen over the alternative (or
foil). Furthermore, the system uses this dialogue to elicit the user's latent
preferences and provides revised plan suggestions through three different
interaction strategies.
| [
{
"version": "v1",
"created": "Thu, 19 Nov 2020 04:18:38 GMT"
},
{
"version": "v2",
"created": "Fri, 3 Jun 2022 22:36:02 GMT"
}
] | 1,654,560,000,000 | [
[
"Valmeekam",
"Karthik",
""
],
[
"Sreedharan",
"Sarath",
""
],
[
"Sengupta",
"Sailik",
""
],
[
"Kambhampati",
"Subbarao",
""
]
] |
2011.09671 | Qiang Shen | Qiang Shen and Stefano Teso and Wanyi Zhang and Hao Xu and Fausto
Giunchiglia | Multi-Modal Subjective Context Modelling and Recognition | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Applications like personal assistants need to be aware ofthe user's context,
e.g., where they are, what they are doing, and with whom. Context information
is usually inferred from sensor data, like GPS sensors and accelerometers on
the user's smartphone. This prediction task is known as context recognition. A
well-defined context model is fundamental for successful recognition. Existing
models, however, have two major limitations. First, they focus on few aspects,
like location or activity, meaning that recognition methods based onthem can
only compute and leverage few inter-aspect correlations. Second, existing
models typically assume that context is objective, whereas in most applications
context is best viewed from the user's perspective. Neglecting these factors
limits the usefulness of the context model and hinders recognition. We present
a novel ontological context model that captures five dimensions, namely time,
location, activity, social relations and object. Moreover, our model defines
three levels of description(objective context, machine context and subjective
context) that naturally support subjective annotations and reasoning.An initial
context recognition experiment on real-world data hints at the promise of our
model.
| [
{
"version": "v1",
"created": "Thu, 19 Nov 2020 05:42:03 GMT"
}
] | 1,605,830,400,000 | [
[
"Shen",
"Qiang",
""
],
[
"Teso",
"Stefano",
""
],
[
"Zhang",
"Wanyi",
""
],
[
"Xu",
"Hao",
""
],
[
"Giunchiglia",
"Fausto",
""
]
] |
2011.09705 | Rebecca Eifler | Rebecca Eifler and J\"org Hoffmann | Iterative Planning with Plan-Space Explanations: A Tool and User Study | Proceedings of the International Workshop of Explainable AI Planning
(XAIP'20), at ICAPS'20 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In a variety of application settings, the user preference for a planning task
- the precise optimization objective - is difficult to elicit. One possible
remedy is planning as an iterative process, allowing the user to iteratively
refine and modify example plans. A key step to support such a process are
explanations, answering user questions about the current plan. In particular, a
relevant kind of question is "Why does the plan you suggest not satisfy $p$?",
where p is a plan property desirable to the user. Note that such a question
pertains to plan space, i.e., the set of possible alternative plans. Adopting
the recent approach to answer such questions in terms of plan-property
dependencies, here we implement a tool and user interface for human-guided
iterative planning including plan-space explanations. The tool runs in standard
Web browsers, and provides simple user interfaces for both developers and
users. We conduct a first user study, whose outcome indicates the usefulness of
plan-property dependency explanations in iterative planning.
| [
{
"version": "v1",
"created": "Thu, 19 Nov 2020 08:15:13 GMT"
}
] | 1,605,830,400,000 | [
[
"Eifler",
"Rebecca",
""
],
[
"Hoffmann",
"Jörg",
""
]
] |
2011.09722 | Yuri Lavinas Mr | Felipe Vaz, Yuri Lavinas, Claus Aranha and Marcelo Ladeira | Exploring Constraint Handling Techniques in Real-world Problems on
MOEA/D with Limited Budget of Evaluations | Final version will be submitted to EMO-2021. This is only a preprint | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Finding good solutions for Multi-objective Optimization (MOPs) Problems is
considered a hard problem, especially when considering MOPs with constraints.
Thus, most of the works in the context of MOPs do not explore in-depth how
different constraints affect the performance of MOP solvers. Here, we focus on
exploring the effects of different Constraint Handling Techniques (CHTs) on
MOEA/D, a commonly used MOP solver when solving complex real-world MOPs.
Moreover, we introduce a simple and effective CHT focusing on the exploration
of the decision space, the Three Stage Penalty. We explore each of these CHTs
in MOEA/D on two simulated MOPs and six analytic MOPs (eight in total). The
results of this work indicate that while the best CHT is problem-dependent, our
new proposed Three Stage Penalty achieves competitive results and remarkable
performance in terms of hypervolume values in the hard simulated car design
MOP.
| [
{
"version": "v1",
"created": "Thu, 19 Nov 2020 08:51:53 GMT"
}
] | 1,605,830,400,000 | [
[
"Vaz",
"Felipe",
""
],
[
"Lavinas",
"Yuri",
""
],
[
"Aranha",
"Claus",
""
],
[
"Ladeira",
"Marcelo",
""
]
] |
2011.09776 | Yang Liu | Yang Liu, Anthony C. Constantinou, ZhiGao Guo | Improving Bayesian Network Structure Learning in the Presence of
Measurement Error | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Structure learning algorithms that learn the graph of a Bayesian network from
observational data often do so by assuming the data correctly reflect the true
distribution of the variables. However, this assumption does not hold in the
presence of measurement error, which can lead to spurious edges. This is one of
the reasons why the synthetic performance of these algorithms often
overestimates real-world performance. This paper describes an algorithm that
can be added as an additional learning phase at the end of any structure
learning algorithm, and serves as a correction learning phase that removes
potential false positive edges. The results show that the proposed correction
algorithm successfully improves the graphical score of four well-established
structure learning algorithms spanning different classes of learning in the
presence of measurement error.
| [
{
"version": "v1",
"created": "Thu, 19 Nov 2020 11:27:47 GMT"
}
] | 1,605,830,400,000 | [
[
"Liu",
"Yang",
""
],
[
"Constantinou",
"Anthony C.",
""
],
[
"Guo",
"ZhiGao",
""
]
] |
2011.09850 | Lenore Blum | Manuel Blum and Lenore Blum | A Theoretical Computer Science Perspective on Consciousness | 33 pages; 10 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The quest to understand consciousness, once the purview of philosophers and
theologians, is now actively pursued by scientists of many stripes. This paper
studies consciousness from the perspective of theoretical computer science. It
formalizes the Global Workspace Theory (GWT) originated by cognitive
neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene,
and others. Our major contribution lies in the precise formal definition of a
Conscious Turing Machine (CTM), also called a Conscious AI. We define the CTM
in the spirit of Alan Turing's simple yet powerful definition of a computer,
the Turing Machine (TM). We are not looking for a complex model of the brain
nor of cognition but for a simple model of (the admittedly complex concept of)
consciousness. After formally defining CTM, we give a formal definition of
consciousness in CTM. We then suggest why the CTM has the feeling of
consciousness. The reasonableness of the definitions and explanations can be
judged by how well they agree with commonly accepted intuitive concepts of
human consciousness, the breadth of related concepts that the model explains
easily and naturally, and the extent of its agreement with scientific evidence.
| [
{
"version": "v1",
"created": "Wed, 18 Nov 2020 11:28:37 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Jan 2021 16:48:42 GMT"
},
{
"version": "v3",
"created": "Thu, 21 Jan 2021 18:40:51 GMT"
},
{
"version": "v4",
"created": "Mon, 23 Aug 2021 18:40:52 GMT"
}
] | 1,629,849,600,000 | [
[
"Blum",
"Manuel",
""
],
[
"Blum",
"Lenore",
""
]
] |
2011.09858 | Carsten Lutz | Jean Christoph Jung, Carsten Lutz, Mauricio Martel, Thomas Schneider | Conservative Extensions in Horn Description Logics with Inverse Roles | null | Journal of Artificial Intelligence Ressearch 68: 365-411 (2020) | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We investigate the decidability and computational complexity of conservative
extensions and the related notions of inseparability and entailment in Horn
description logics (DLs) with inverse roles. We consider both query
conservative extensions, defined by requiring that the answers to all
conjunctive queries are left unchanged, and deductive conservative extensions,
which require that the entailed concept inclusions, role inclusions, and
functionality assertions do not change. Upper bounds for query conservative
extensions are particularly challenging because characterizations in terms of
unbounded homomorphisms between universal models, which are the foundation of
the standard approach to establishing decidability, fail in the presence of
inverse roles. We resort to a characterization that carefully mixes unbounded
and bounded homomorphisms and enables a decision procedure that combines tree
automata and a mosaic technique. Our main results are that query conservative
extensions are 2ExpTime-complete in all DLs between ELI and Horn-ALCHIF and
between Horn-ALC and Horn-ALCHIF, and that deductive conservative extensions
are 2ExpTime-complete in all DLs between ELI and ELHIF_\bot. The same results
hold for inseparability and entailment.
| [
{
"version": "v1",
"created": "Thu, 19 Nov 2020 14:41:02 GMT"
}
] | 1,605,830,400,000 | [
[
"Jung",
"Jean Christoph",
""
],
[
"Lutz",
"Carsten",
""
],
[
"Martel",
"Mauricio",
""
],
[
"Schneider",
"Thomas",
""
]
] |
2011.09890 | Uwe Aickelin | Xiaoping Jiang, Ruibin Bai, Dario Landa-Silva, Uwe Aickelin | Fuzzy C-means-based scenario bundling for stochastic service network
design | 2017 IEEE Symposium on Computational Intelligence (IEEE-SSCI 2017) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Stochastic service network designs with uncertain demand represented by a set
of scenarios can be modelled as a large-scale two-stage stochastic
mixed-integer program (SMIP). The progressive hedging algorithm (PHA) is a
decomposition method for solving the resulting SMIP. The computational
performance of the PHA can be greatly enhanced by decomposing according to
scenario bundles instead of individual scenarios. At the heart of bundle-based
decomposition is the method for grouping the scenarios into bundles. In this
paper, we present a fuzzy c-means-based scenario bundling method to address
this problem. Rather than full membership of a bundle, which is typically the
case in existing scenario bundling strategies such as k-means, a scenario has
partial membership in each of the bundles and can be assigned to more than one
bundle in our method.
| [
{
"version": "v1",
"created": "Mon, 16 Nov 2020 02:41:47 GMT"
}
] | 1,605,830,400,000 | [
[
"Jiang",
"Xiaoping",
""
],
[
"Bai",
"Ruibin",
""
],
[
"Landa-Silva",
"Dario",
""
],
[
"Aickelin",
"Uwe",
""
]
] |
2011.10307 | Margaux Nattaf | Margaux Nattaf (G-SCOP), Arnaud Malapert | Filtering Rules for Flow Time Minimization in a Parallel Machine
Scheduling Problem | null | CP 2020: Principles and Practice of Constraint Programming,
pp.462-477, 2020 | 10.1007/978-3-030-58475-7_27 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper studies the scheduling of jobs of different families on parallel
machines with qualification constraints. Originating from semiconductor
manufacturing, this constraint imposes a time threshold between the execution
of two jobs of the same family. Otherwise, the machine becomes disqualified for
this family. The goal is to minimize both the flow time and the number of
disqualifications. Recently, an efficient constraint programming model has been
proposed. However, when priority is given to the flow time objective, the
efficiency of the model can be improved. This paper uses a polynomial-time
algorithm which minimize the flow time for a single machine relaxation where
disqualifications are not considered. Using this algorithm one can derived
filtering rules on different variables of the model. Experimental results are
presented showing the effectiveness of these rules. They improve the
competitiveness with the mixed integer linear program of the literature.
| [
{
"version": "v1",
"created": "Fri, 20 Nov 2020 10:00:14 GMT"
}
] | 1,606,089,600,000 | [
[
"Nattaf",
"Margaux",
"",
"G-SCOP"
],
[
"Malapert",
"Arnaud",
""
]
] |
2011.10640 | Michael Gr. Voskoglou Prof. Dr. | Michael Voskoglou | Assessment and Linear Programming under Fuzzy Conditions | 19 pages, 3 figures | Journal of Fuzzy Extension and Applications, 1(3), 198-216, 2020 | 10.22105/jfea.2020.253436.1024 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | A new fuzzy method is developed using triangular/trapezoidal fuzzy numbers
for evaluating a group's mean performance, when qualitative grades instead of
numerical scores are used for assessing its members' individual performance.
Also, a new technique is developed for solving Linear Programming problems with
fuzzy coefficients and everyday life applications are presented to illustrate
our results.
| [
{
"version": "v1",
"created": "Fri, 20 Nov 2020 21:13:36 GMT"
}
] | 1,606,176,000,000 | [
[
"Voskoglou",
"Michael",
""
]
] |
2011.10672 | Johannes Schneider | Johannes Schneider and Rene Abraham and Christian Meske and Jan vom
Brocke | AI Governance for Businesses | null | Information Systems Management, 2022 | 10.1080/10580530.2022.2085825 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial Intelligence (AI) governance regulates the exercise of authority
and control over the management of AI. It aims at leveraging AI through
effective use of data and minimization of AI-related cost and risk. While
topics such as AI governance and AI ethics are thoroughly discussed on a
theoretical, philosophical, societal and regulatory level, there is limited
work on AI governance targeted to companies and corporations. This work views
AI products as systems, where key functionality is delivered by machine
learning (ML) models leveraging (training) data. We derive a conceptual
framework by synthesizing literature on AI and related fields such as ML. Our
framework decomposes AI governance into governance of data, (ML) models and
(AI) systems along four dimensions. It relates to existing IT and data
governance frameworks and practices. It can be adopted by practitioners and
academics alike. For practitioners the synthesis of mainly research papers, but
also practitioner publications and publications of regulatory bodies provides a
valuable starting point to implement AI governance, while for academics the
paper highlights a number of areas of AI governance that deserve more
attention.
| [
{
"version": "v1",
"created": "Fri, 20 Nov 2020 22:31:37 GMT"
},
{
"version": "v2",
"created": "Sun, 26 Jun 2022 20:52:22 GMT"
}
] | 1,656,374,400,000 | [
[
"Schneider",
"Johannes",
""
],
[
"Abraham",
"Rene",
""
],
[
"Meske",
"Christian",
""
],
[
"Brocke",
"Jan vom",
""
]
] |
2011.10707 | Sarath Sreedharan | Sarath Sreedharan, Tathagata Chakraborti, Yara Rizk and Yasaman
Khazaeni | Explainable Composition of Aggregated Assistants | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A new design of an AI assistant that has become increasingly popular is that
of an "aggregated assistant" -- realized as an orchestrated composition of
several individual skills or agents that can each perform atomic tasks. In this
paper, we will talk about the role of planning in the automated composition of
such assistants and explore how concepts in automated planning can help to
establish transparency of the inner workings of the assistant to the end-user.
| [
{
"version": "v1",
"created": "Sat, 21 Nov 2020 02:39:27 GMT"
}
] | 1,606,176,000,000 | [
[
"Sreedharan",
"Sarath",
""
],
[
"Chakraborti",
"Tathagata",
""
],
[
"Rizk",
"Yara",
""
],
[
"Khazaeni",
"Yasaman",
""
]
] |
2011.10794 | Nandish Chattopadhyay | Nandish Chattopadhyay, Lionell Yip En Zhi, Bryan Tan Bing Xing and
Anupam Chattopadhyay | Spatially Correlated Patterns in Adversarial Images | Submitted for review | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Adversarial attacks have proved to be the major impediment in the progress on
research towards reliable machine learning solutions. Carefully crafted
perturbations, imperceptible to human vision, can be added to images to force
misclassification by an otherwise high performing neural network. To have a
better understanding of the key contributors of such structured attacks, we
searched for and studied spatially co-located patterns in the distribution of
pixels in the input space. In this paper, we propose a framework for
segregating and isolating regions within an input image which are particularly
critical towards either classification (during inference), or adversarial
vulnerability or both. We assert that during inference, the trained model looks
at a specific region in the image, which we call Region of Importance (RoI);
and the attacker looks at a region to alter/modify, which we call Region of
Attack (RoA). The success of this approach could also be used to design a
post-hoc adversarial defence method, as illustrated by our observations. This
uses the notion of blocking out (we call neutralizing) that region of the image
which is highly vulnerable to adversarial attacks but is not important for the
task of classification. We establish the theoretical setup for formalising the
process of segregation, isolation and neutralization and substantiate it
through empirical analysis on standard benchmarking datasets. The findings
strongly indicate that mapping features into the input space preserves the
significant patterns typically observed in the feature-space while adding major
interpretability and therefore simplifies potential defensive mechanisms.
| [
{
"version": "v1",
"created": "Sat, 21 Nov 2020 14:06:59 GMT"
}
] | 1,606,176,000,000 | [
[
"Chattopadhyay",
"Nandish",
""
],
[
"Zhi",
"Lionell Yip En",
""
],
[
"Xing",
"Bryan Tan Bing",
""
],
[
"Chattopadhyay",
"Anupam",
""
]
] |
2011.10804 | Tianchen Zhao | Tianchen Zhao, Xuefei Ning, Xiangsheng Shi, Songyi Yang, Shuang Liang,
Peng Lei, Jianfei Chen, Huazhong Yang, Yu Wang | BARS: Joint Search of Cell Topology and Layout for Accurate and
Efficient Binary ARchitectures | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Binary Neural Networks (BNNs) have received significant attention due to
their promising efficiency. Currently, most BNN studies directly adopt
widely-used CNN architectures, which can be suboptimal for BNNs. This paper
proposes a novel Binary ARchitecture Search (BARS) flow to discover superior
binary architecture in a large design space. Specifically, we analyze the
information bottlenecks that are related to both the topology and layout
architecture design choices. And we propose to automatically search for the
optimal information flow. To achieve that, we design a two-level (Macro &
Micro) search space tailored for BNNs and apply a differentiable neural
architecture search (NAS) to explore this search space efficiently. The
macro-level search space includes width and depth decisions, which is required
for better balancing the model performance and complexity. We also design the
micro-level search space to strengthen the information flow for BNN. %A notable
challenge of BNN architecture search lies in that binary operations exacerbate
the "collapse" problem of differentiable NAS, for which we incorporate various
search and derive strategies to stabilize the search process. On CIFAR-10, BARS
achieves 1.5% higher accuracy with 2/3 binary operations and 1/10
floating-point operations comparing with existing BNN NAS studies. On ImageNet,
with similar resource consumption, BARS-discovered architecture achieves a 6%
accuracy gain than hand-crafted binary ResNet-18 architectures and outperforms
other binary architectures while fully binarizing the architecture backbone.
| [
{
"version": "v1",
"created": "Sat, 21 Nov 2020 14:38:44 GMT"
},
{
"version": "v2",
"created": "Mon, 21 Dec 2020 07:38:32 GMT"
},
{
"version": "v3",
"created": "Sat, 27 Mar 2021 05:54:26 GMT"
}
] | 1,617,062,400,000 | [
[
"Zhao",
"Tianchen",
""
],
[
"Ning",
"Xuefei",
""
],
[
"Shi",
"Xiangsheng",
""
],
[
"Yang",
"Songyi",
""
],
[
"Liang",
"Shuang",
""
],
[
"Lei",
"Peng",
""
],
[
"Chen",
"Jianfei",
""
],
[
"Yang",
"Huazhong",
""
],
[
"Wang",
"Yu",
""
]
] |
2011.10890 | Tianrong Chen | Tianrong Chen, Ziyi Wang, Ioannis Exarchos, Evangelos A. Theodorou | Large-Scale Multi-Agent Deep FBSDEs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present a scalable deep learning framework for finding
Markovian Nash Equilibria in multi-agent stochastic games using fictitious
play. The motivation is inspired by theoretical analysis of Forward Backward
Stochastic Differential Equations (FBSDE) and their implementation in a deep
learning setting, which is the source of our algorithm's sample efficiency
improvement. By taking advantage of the permutation-invariant property of
agents in symmetric games, the scalability and performance is further enhanced
significantly. We showcase superior performance of our framework over the
state-of-the-art deep fictitious play algorithm on an inter-bank
lending/borrowing problem in terms of multiple metrics. More importantly, our
approach scales up to 3000 agents in simulation, a scale which, to the best of
our knowledge, represents a new state-of-the-art. We also demonstrate the
applicability of our framework in robotics on a belief space autonomous racing
problem.
| [
{
"version": "v1",
"created": "Sat, 21 Nov 2020 23:00:50 GMT"
},
{
"version": "v2",
"created": "Sun, 16 May 2021 18:42:42 GMT"
},
{
"version": "v3",
"created": "Fri, 21 May 2021 04:46:01 GMT"
}
] | 1,621,814,400,000 | [
[
"Chen",
"Tianrong",
""
],
[
"Wang",
"Ziyi",
""
],
[
"Exarchos",
"Ioannis",
""
],
[
"Theodorou",
"Evangelos A.",
""
]
] |
2011.10920 | Sarath Sreedharan | Sarath Sreedharan, Anagha Kulkarni, Tathagata Chakraborti, David E.
Smith and Subbarao Kambhampati | A Bayesian Account of Measures of Interpretability in Human-AI
Interaction | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Existing approaches for the design of interpretable agent behavior consider
different measures of interpretability in isolation. In this paper we posit
that, in the design and deployment of human-aware agents in the real world,
notions of interpretability are just some among many considerations; and the
techniques developed in isolation lack two key properties to be useful when
considered together: they need to be able to 1) deal with their mutually
competing properties; and 2) an open world where the human is not just there to
interpret behavior in one specific form. To this end, we consider three
well-known instances of interpretable behavior studied in existing literature
-- namely, explicability, legibility, and predictability -- and propose a
revised model where all these behaviors can be meaningfully modeled together.
We will highlight interesting consequences of this unified model and motivate,
through results of a user study, why this revision is necessary.
| [
{
"version": "v1",
"created": "Sun, 22 Nov 2020 03:28:28 GMT"
}
] | 1,606,176,000,000 | [
[
"Sreedharan",
"Sarath",
""
],
[
"Kulkarni",
"Anagha",
""
],
[
"Chakraborti",
"Tathagata",
""
],
[
"Smith",
"David E.",
""
],
[
"Kambhampati",
"Subbarao",
""
]
] |
2011.10970 | El Moatez Billah Nagoudi | Muhammad Abdul-Mageed, Shady Elbassuoni, Jad Doughman, AbdelRahim
Elmadany, El Moatez Billah Nagoudi, Yorgo Zoughby, Ahmad Shaher, Iskander
Gaba, Ahmed Helal, Mohammed El-Razzaz | DiaLex: A Benchmark for Evaluating Multidialectal Arabic Word Embeddings | WANLP2021 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Word embeddings are a core component of modern natural language processing
systems, making the ability to thoroughly evaluate them a vital task. We
describe DiaLex, a benchmark for intrinsic evaluation of dialectal Arabic word
embedding. DiaLex covers five important Arabic dialects: Algerian, Egyptian,
Lebanese, Syrian, and Tunisian. Across these dialects, DiaLex provides a
testbank for six syntactic and semantic relations, namely male to female,
singular to dual, singular to plural, antonym, comparative, and genitive to
past tense. DiaLex thus consists of a collection of word pairs representing
each of the six relations in each of the five dialects. To demonstrate the
utility of DiaLex, we use it to evaluate a set of existing and new Arabic word
embeddings that we developed. Our benchmark, evaluation code, and new word
embedding models will be publicly available.
| [
{
"version": "v1",
"created": "Sun, 22 Nov 2020 08:47:52 GMT"
},
{
"version": "v2",
"created": "Sat, 13 Mar 2021 04:02:19 GMT"
}
] | 1,615,852,800,000 | [
[
"Abdul-Mageed",
"Muhammad",
""
],
[
"Elbassuoni",
"Shady",
""
],
[
"Doughman",
"Jad",
""
],
[
"Elmadany",
"AbdelRahim",
""
],
[
"Nagoudi",
"El Moatez Billah",
""
],
[
"Zoughby",
"Yorgo",
""
],
[
"Shaher",
"Ahmad",
""
],
[
"Gaba",
"Iskander",
""
],
[
"Helal",
"Ahmed",
""
],
[
"El-Razzaz",
"Mohammed",
""
]
] |
2011.11278 | Dianbo Liu Dr | He Zhu and Dianbo Liu | FakeSafe: Human Level Data Protection by Disinformation Mapping using
Cycle-consistent Adversarial Network | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The concept of disinformation is to use fake messages to confuse people in
order to protect the real information. This strategy can be adapted into data
science to protect valuable private and sensitive data. Huge amount of private
data are being generated from personal devices such as smart phone and wearable
in recent years. Being able to utilize these personal data will bring big
opportunities to design personalized products, conduct precision healthcare and
many other tasks that were impossible in the past. However, due to privacy,
safety and regulation reasons, it is often difficult to transfer or store data
in its original form while keeping them safe. Building a secure data transfer
and storage infrastructure to preserving privacy is costly in most cases and
there is always a concern of data security due to human errors. In this study,
we propose a method, named FakeSafe, to provide human level data protection
using generative adversarial network with cycle consistency and conducted
experiments using both benchmark and real world data sets to illustrate
potential applications of FakeSafe.
| [
{
"version": "v1",
"created": "Mon, 23 Nov 2020 08:47:40 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Dec 2020 04:10:21 GMT"
}
] | 1,607,644,800,000 | [
[
"Zhu",
"He",
""
],
[
"Liu",
"Dianbo",
""
]
] |
2011.11358 | Alastair Finlinson Mr | Alastair Finlinson, Sotiris Moschoyiannis | Synthesis and Pruning as a Dynamic Compression Strategy for Efficient
Deep Neural Networks | 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE
MANAGEMENT, 9th International Symposium DATAMOD 2020 FROM DATA TO MODELS AND
BACK, 16 Pages, 7 Figures, 3 Tables, 2 Equations | null | 10.1007/978-3-030-70650-0_1 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The brain is a highly reconfigurable machine capable of task-specific
adaptations. The brain continually rewires itself for a more optimal
configuration to solve problems. We propose a novel strategic synthesis
algorithm for feedforward networks that draws directly from the brain's
behaviours when learning. The proposed approach analyses the network and ranks
weights based on their magnitude. Unlike existing approaches that advocate
random selection, we select highly performing nodes as starting points for new
edges and exploit the Gaussian distribution over the weights to select
corresponding endpoints. The strategy aims only to produce useful connections
and result in a smaller residual network structure. The approach is
complemented with pruning to further the compression. We demonstrate the
techniques to deep feedforward networks. The residual sub-networks that are
formed from the synthesis approaches in this work form common sub-networks with
similarities up to ~90%. Using pruning as a complement to the strategic
synthesis approach, we observe improvements in compression.
| [
{
"version": "v1",
"created": "Mon, 23 Nov 2020 12:30:57 GMT"
}
] | 1,619,049,600,000 | [
[
"Finlinson",
"Alastair",
""
],
[
"Moschoyiannis",
"Sotiris",
""
]
] |
2011.11395 | Giuseppe Fenza | Giuseppe Fenza and Mariacristina Gallo and Vincenzo Loia and Domenico
Marinoand Francesco Orciuoli and Alberto Volpe | Semantic CPPS in Industry 4.0 | null | null | 10.1007/978-3-030-44041-1_91 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Cyber-Physical Systems (CPS) play a crucial role in the era of the
4thIndustrial Revolution. Recently, the application of the CPS to industrial
manufacturing leads to a specialization of them referred as Cyber-Physical
Production Systems (CPPS). Among other challenges, CPS and CPPS should be able
to address interoperability issues, since one of their intrinsic requirement is
the capability to interface and cooperate with other systems. On the other
hand, to fully realize theIndustry 4.0 vision, it is required to address
horizontal, vertical, and end-to-end integration enabling a complete awareness
through the entire supply chain. In this context, Semantic Web standards and
technologies may have a promising role to represent manufacturing knowledge in
a machine-interpretable way for enabling communications among heterogeneous
Industrial assets. This paper proposes an integration of Semantic Web models
available at state of the art for implementing a5C architecture mainly targeted
to collect and process semantic data stream in a way that would unlock the
potentiality of data yield in a smart manufacturing environment. The analysis
of key industrial ontologies and semantic technologies allows us to instantiate
an example scenario for monitoring Overall Equipment Effectiveness(OEE). The
solution uses the SOSA ontology for representing the semantic datastream. Then,
C-SPARQL queries are defined for periodically carrying out useful KPIs to
address the proposed aim.
| [
{
"version": "v1",
"created": "Wed, 18 Nov 2020 21:53:07 GMT"
}
] | 1,606,176,000,000 | [
[
"Fenza",
"Giuseppe",
""
],
[
"Gallo",
"Mariacristina",
""
],
[
"Loia",
"Vincenzo",
""
],
[
"Orciuoli",
"Domenico Marinoand Francesco",
""
],
[
"Volpe",
"Alberto",
""
]
] |
2011.11517 | Tyler Malloy | Tyler Malloy, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro,
Chris R. Sims | Consolidation via Policy Information Regularization in Deep RL for
Multi-Agent Games | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper introduces an information-theoretic constraint on learned policy
complexity in the Multi-Agent Deep Deterministic Policy Gradient (MADDPG)
reinforcement learning algorithm. Previous research with a related approach in
continuous control experiments suggests that this method favors learning
policies that are more robust to changing environment dynamics. The multi-agent
game setting naturally requires this type of robustness, as other agents'
policies change throughout learning, introducing a nonstationary environment.
For this reason, recent methods in continual learning are compared to our
approach, termed Capacity-Limited MADDPG. Results from experimentation in
multi-agent cooperative and competitive tasks demonstrate that the
capacity-limited approach is a good candidate for improving learning
performance in these environments.
| [
{
"version": "v1",
"created": "Mon, 23 Nov 2020 16:28:27 GMT"
}
] | 1,606,176,000,000 | [
[
"Malloy",
"Tyler",
""
],
[
"Klinger",
"Tim",
""
],
[
"Liu",
"Miao",
""
],
[
"Riemer",
"Matthew",
""
],
[
"Tesauro",
"Gerald",
""
],
[
"Sims",
"Chris R.",
""
]
] |
2011.12262 | Sachin Grover | Sachin Grover, David Smith, Subbarao Kambhampati | Model Elicitation through Direct Questioning | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The future will be replete with scenarios where humans are robots will be
working together in complex environments. Teammates interact, and the robot's
interaction has to be about getting useful information about the human's
(teammate's) model. There are many challenges before a robot can interact, such
as incorporating the structural differences in the human's model, ensuring
simpler responses, etc. In this paper, we investigate how a robot can interact
to localize the human model from a set of models. We show how to generate
questions to refine the robot's understanding of the teammate's model. We
evaluate the method in various planning domains. The evaluation shows that
these questions can be generated offline, and can help refine the model through
simple answers.
| [
{
"version": "v1",
"created": "Tue, 24 Nov 2020 18:17:16 GMT"
}
] | 1,606,262,400,000 | [
[
"Grover",
"Sachin",
""
],
[
"Smith",
"David",
""
],
[
"Kambhampati",
"Subbarao",
""
]
] |
2011.12340 | Larry Heck | Larry Heck, Simon Heck, Anirudh Sundar | mForms : Multimodal Form-Filling with Question Answering | 5 pages, 6 figures, 4 tables | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper presents a new approach to form-filling by reformulating the task
as multimodal natural language Question Answering (QA). The reformulation is
achieved by first translating the elements on the GUI form (text fields,
buttons, icons, etc.) to natural language questions, where these questions
capture the element's multimodal semantics. After a match is determined between
the form element (Question) and the user utterance (Answer), the form element
is filled through a pre-trained extractive QA system. By leveraging pre-trained
QA models and not requiring form-specific training, this approach to
form-filling is zero-shot. The paper also presents an approach to further
refine the form-filling by using multi-task training to incorporate a
potentially large number of successive tasks. Finally, the paper introduces a
multimodal natural language form-filling dataset Multimodal Forms (mForms), as
well as a multimodal extension of the popular ATIS dataset to support future
research and experimentation. Results show the new approach not only maintains
robust accuracy for sparse training conditions but achieves state-of-the-art F1
of 0.97 on ATIS with approximately 1/10th of the training data.
| [
{
"version": "v1",
"created": "Tue, 24 Nov 2020 19:47:53 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Mar 2022 21:03:33 GMT"
},
{
"version": "v3",
"created": "Wed, 13 Mar 2024 17:01:54 GMT"
},
{
"version": "v4",
"created": "Sat, 23 Mar 2024 17:53:43 GMT"
}
] | 1,711,411,200,000 | [
[
"Heck",
"Larry",
""
],
[
"Heck",
"Simon",
""
],
[
"Sundar",
"Anirudh",
""
]
] |
2011.12443 | Kyle Tilbury | Kyle Tilbury, Jesse Hoey | The Human Effect Requires Affect: Addressing Social-Psychological
Factors of Climate Change with Machine Learning | Accepted paper at the Tackling Climate Change with Machine Learning
workshop at NeurIPS 2020 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Machine learning has the potential to aid in mitigating the human effects of
climate change. Previous applications of machine learning to tackle the human
effects in climate change include approaches like informing individuals of
their carbon footprint and strategies to reduce it. For these methods to be the
most effective they must consider relevant social-psychological factors for
each individual. Of social-psychological factors at play in climate change,
affect has been previously identified as a key element in perceptions and
willingness to engage in mitigative behaviours. In this work, we propose an
investigation into how affect could be incorporated to enhance machine learning
based interventions for climate change. We propose using affective agent-based
modelling for climate change as well as the use of a simulated climate change
social dilemma to explore the potential benefits of affective machine learning
interventions. Behavioural and informational interventions can be a powerful
tool in helping humans adopt mitigative behaviours. We expect that utilizing
affective ML can make interventions an even more powerful tool and help
mitigative behaviours become widely adopted.
| [
{
"version": "v1",
"created": "Tue, 24 Nov 2020 23:34:54 GMT"
}
] | 1,606,348,800,000 | [
[
"Tilbury",
"Kyle",
""
],
[
"Hoey",
"Jesse",
""
]
] |
2011.12491 | Bradly Stadie | Lunjun Zhang, Ge Yang, Bradly C. Stadie | World Model as a Graph: Learning Latent Landmarks for Planning | null | International Conference on Machine Learning (ICML). 2021 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Planning - the ability to analyze the structure of a problem in the large and
decompose it into interrelated subproblems - is a hallmark of human
intelligence. While deep reinforcement learning (RL) has shown great promise
for solving relatively straightforward control tasks, it remains an open
problem how to best incorporate planning into existing deep RL paradigms to
handle increasingly complex environments. One prominent framework, Model-Based
RL, learns a world model and plans using step-by-step virtual rollouts. This
type of world model quickly diverges from reality when the planning horizon
increases, thus struggling at long-horizon planning. How can we learn world
models that endow agents with the ability to do temporally extended reasoning?
In this work, we propose to learn graph-structured world models composed of
sparse, multi-step transitions. We devise a novel algorithm to learn latent
landmarks that are scattered (in terms of reachability) across the goal space
as the nodes on the graph. In this same graph, the edges are the reachability
estimates distilled from Q-functions. On a variety of high-dimensional
continuous control tasks ranging from robotic manipulation to navigation, we
demonstrate that our method, named L3P, significantly outperforms prior work,
and is oftentimes the only method capable of leveraging both the robustness of
model-free RL and generalization of graph-search algorithms. We believe our
work is an important step towards scalable planning in reinforcement learning.
| [
{
"version": "v1",
"created": "Wed, 25 Nov 2020 02:49:21 GMT"
},
{
"version": "v2",
"created": "Fri, 5 Feb 2021 16:40:47 GMT"
},
{
"version": "v3",
"created": "Wed, 30 Jun 2021 21:00:52 GMT"
}
] | 1,625,184,000,000 | [
[
"Zhang",
"Lunjun",
""
],
[
"Yang",
"Ge",
""
],
[
"Stadie",
"Bradly C.",
""
]
] |
2011.12548 | Y. Sinan Hanay | Rustem Ozakar, Rafet Efe Gazanfer and Y. Sinan Hanay | Measuring Happiness Around the World Through Artificial Intelligence | 4 pages, 2 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this work, we analyze the happiness levels of countries using an unbiased
emotion detector, artificial intelligence (AI). To date, researchers proposed
many factors that may affect happiness such as wealth, health and safety. Even
though these factors all seem relevant, there is no clear consensus between
sociologists on how to interpret these, and the models to estimate the cost of
these utilities include some assumptions. Researchers in social sciences have
been working on determination of the happiness levels in society and
exploration of the factors correlated with it through polls and different
statistical methods. In our work, by using artificial intelligence, we
introduce a different and relatively unbiased approach to this problem. By
using AI, we make no assumption about what makes a person happy, and leave the
decision to AI to detect the emotions from the faces of people collected from
publicly available street footages. We analyzed the happiness levels in eight
different cities around the world through available footage on the Internet and
found out that there is no statistically significant difference between
countries in terms of happiness.
| [
{
"version": "v1",
"created": "Wed, 25 Nov 2020 07:12:11 GMT"
}
] | 1,606,348,800,000 | [
[
"Ozakar",
"Rustem",
""
],
[
"Gazanfer",
"Rafet Efe",
""
],
[
"Hanay",
"Y. Sinan",
""
]
] |
2011.12566 | Po-Lin Lai | Po-Lin Lai, Chih-Yun Chen, Liang-Wei Lo, Chien-Chin Chen | ColdGAN: Resolving Cold Start User Recommendation by using Generative
Adversarial Networks | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Mitigating the new user cold-start problem has been critical in the
recommendation system for online service providers to influence user experience
in decision making which can ultimately affect the intention of users to use a
particular service. Previous studies leveraged various side information from
users and items; however, it may be impractical due to privacy concerns. In
this paper, we present ColdGAN, an end-to-end GAN based model with no use of
side information to resolve this problem. The main idea of the proposed model
is to train a network that learns the rating distributions of experienced users
given their cold-start distributions. We further design a time-based function
to restore the preferences of users to cold-start states. With extensive
experiments on two real-world datasets, the results show that our proposed
method achieves significantly improved performance compared with the
state-of-the-art recommenders.
| [
{
"version": "v1",
"created": "Wed, 25 Nov 2020 08:10:35 GMT"
}
] | 1,606,348,800,000 | [
[
"Lai",
"Po-Lin",
""
],
[
"Chen",
"Chih-Yun",
""
],
[
"Lo",
"Liang-Wei",
""
],
[
"Chen",
"Chien-Chin",
""
]
] |
2011.12599 | Valentina Anita Carriero | Valentina Anita Carriero, Marilena Daquino, Aldo Gangemi, Andrea
Giovanni Nuzzolese, Silvio Peroni, Valentina Presutti, Francesca Tomasi | The Landscape of Ontology Reuse Approaches | null | null | 10.3233/SSW200033 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Ontology reuse aims to foster interoperability and facilitate knowledge
reuse. Several approaches are typically evaluated by ontology engineers when
bootstrapping a new project. However, current practices are often motivated by
subjective, case-by-case decisions, which hamper the definition of a
recommended behaviour. In this chapter we argue that to date there are no
effective solutions for supporting developers' decision-making process when
deciding on an ontology reuse strategy. The objective is twofold: (i) to survey
current approaches to ontology reuse, presenting motivations, strategies,
benefits and limits, and (ii) to analyse two representative approaches and
discuss their merits.
| [
{
"version": "v1",
"created": "Wed, 25 Nov 2020 09:21:07 GMT"
}
] | 1,609,459,200,000 | [
[
"Carriero",
"Valentina Anita",
""
],
[
"Daquino",
"Marilena",
""
],
[
"Gangemi",
"Aldo",
""
],
[
"Nuzzolese",
"Andrea Giovanni",
""
],
[
"Peroni",
"Silvio",
""
],
[
"Presutti",
"Valentina",
""
],
[
"Tomasi",
"Francesca",
""
]
] |
2011.12728 | Alexander Klimenko Y | Alexander Y Klimenko and Dimitri A Klimenko | On limitations of learning algorithms in competitive environments | 8 pages, 1 figure | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We discuss conceptual limitations of generic learning algorithms pursuing
adversarial goals in competitive environments, and prove that they are subject
to limitations that are analogous to the constraints on knowledge imposed by
the famous theorems of G\"odel and Turing. These limitations are shown to be
related to intransitivity, which is commonly present in competitive
environments.
| [
{
"version": "v1",
"created": "Wed, 25 Nov 2020 13:40:08 GMT"
},
{
"version": "v2",
"created": "Fri, 18 Jun 2021 07:07:05 GMT"
}
] | 1,624,233,600,000 | [
[
"Klimenko",
"Alexander Y",
""
],
[
"Klimenko",
"Dimitri A",
""
]
] |
2011.12862 | Sophia Saller | Jana Koehler, Joseph B\"urgler, Urs Fontana, Etienne Fux, Florian
Herzog, Marc Pouly, Sophia Saller, Anastasia Salyaeva, Peter Scheiblechner,
Kai Waelti | Cable Tree Wiring -- Benchmarking Solvers on a Real-World Scheduling
Problem with a Variety of Precedence Constraints | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Cable trees are used in industrial products to transmit energy and
information between different product parts. To this date, they are mostly
assembled by humans and only few automated manufacturing solutions exist using
complex robotic machines. For these machines, the wiring plan has to be
translated into a wiring sequence of cable plugging operations to be followed
by the machine. In this paper, we study and formalize the problem of deriving
the optimal wiring sequence for a given layout of a cable tree. We summarize
our investigations to model this cable tree wiring Problem (CTW) as a traveling
salesman problem with atomic, soft atomic, and disjunctive precedence
constraints as well as tour-dependent edge costs such that it can be solved by
state-of-the-art constraint programming (CP), Optimization Modulo Theories
(OMT), and mixed-integer programming (MIP) solvers. It is further shown, how
the CTW problem can be viewed as a soft version of the coupled tasks scheduling
problem. We discuss various modeling variants for the problem, prove its
NP-hardness, and empirically compare CP, OMT, and MIP solvers on a benchmark
set of 278 instances. The complete benchmark set with all models and instance
data is available on github and is accepted for inclusion in the MiniZinc
challenge 2020.
| [
{
"version": "v1",
"created": "Wed, 25 Nov 2020 16:34:04 GMT"
}
] | 1,606,348,800,000 | [
[
"Koehler",
"Jana",
""
],
[
"Bürgler",
"Joseph",
""
],
[
"Fontana",
"Urs",
""
],
[
"Fux",
"Etienne",
""
],
[
"Herzog",
"Florian",
""
],
[
"Pouly",
"Marc",
""
],
[
"Saller",
"Sophia",
""
],
[
"Salyaeva",
"Anastasia",
""
],
[
"Scheiblechner",
"Peter",
""
],
[
"Waelti",
"Kai",
""
]
] |
2011.12863 | Francesca Foffano | Francesca Foffano, Teresa Scantamburlo, Atia Cort\'es, and Chiara
Bissolo | European Strategy on AI: Are we truly fostering social good? | 6 pages, 1 figures, submitted at IJCAI 2020 Workshop on AI for Social
Good | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial intelligence (AI) is already part of our daily lives and is
playing a key role in defining the economic and social shape of the future. In
2018, the European Commission introduced its AI strategy able to compete in the
next years with world powers such as China and US, but relying on the respect
of European values and fundamental rights. As a result, most of the Member
States have published their own National Strategy with the aim to work on a
coordinated plan for Europe. In this paper, we present an ongoing study on how
European countries are approaching the field of Artificial Intelligence, with
its promises and risks, through the lens of their national AI strategies. In
particular, we aim to investigate how European countries are investing in AI
and to what extent the stated plans can contribute to the benefit of the whole
society. This paper reports the main findings of a qualitative analysis of the
investment plans reported in 15 European National Strategies
| [
{
"version": "v1",
"created": "Wed, 25 Nov 2020 16:39:12 GMT"
}
] | 1,606,348,800,000 | [
[
"Foffano",
"Francesca",
""
],
[
"Scantamburlo",
"Teresa",
""
],
[
"Cortés",
"Atia",
""
],
[
"Bissolo",
"Chiara",
""
]
] |
2011.13089 | Hui Wei Dr. | Hui Wei | The Evolution of Concept-Acquisition based on Developmental Psychology | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | A conceptual system with rich connotation is key to improving the performance
of knowledge-based artificial intelligence systems. While a conceptual system,
which has abundant concepts and rich semantic relationships, and is
developable, evolvable, and adaptable to multi-task environments, its actual
construction is not only one of the major challenges of knowledge engineering,
but also the fundamental goal of research on knowledge and conceptualization.
Finding a new method to represent concepts and construct a conceptual system
will therefore greatly improve the performance of many intelligent systems.
Fortunately the core of human cognition is a system with relatively complete
concepts and a mechanism that ensures the establishment and development of the
system. The human conceptual system can not be achieved immediately, but rather
must develop gradually. Developmental psychology carefully observes the process
of concept acquisition in humans at the behavioral level, and along with
cognitive psychology has proposed some rough explanations of those
observations. However, due to the lack of research in aspects such as
representation, systematic models, algorithm details and realization, many of
the results of developmental psychology have not been applied directly to the
building of artificial conceptual systems. For example, Karmiloff-Smith's
Representation Redescription (RR) supposition reflects a concept-acquisition
process that re-describes a lower level representation of a concept to a higher
one. This paper is inspired by this developmental psychology viewpoint. We use
an object-oriented approach to re-explain and materialize RR supposition from
the formal semantic perspective, because the OO paradigm is a natural way to
describe the outside world, and it also has strict grammar regulations.
| [
{
"version": "v1",
"created": "Thu, 26 Nov 2020 01:57:24 GMT"
}
] | 1,606,694,400,000 | [
[
"Wei",
"Hui",
""
]
] |
2011.13169 | Muhammad Naseer Bajwa | Adriano Lucieri, Muhammad Naseer Bajwa, Andreas Dengel, Sheraz Ahmed | Achievements and Challenges in Explaining Deep Learning based
Computer-Aided Diagnosis Systems | 17 pages, 2 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Remarkable success of modern image-based AI methods and the resulting
interest in their applications in critical decision-making processes has led to
a surge in efforts to make such intelligent systems transparent and
explainable. The need for explainable AI does not stem only from ethical and
moral grounds but also from stricter legislation around the world mandating
clear and justifiable explanations of any decision taken or assisted by AI.
Especially in the medical context where Computer-Aided Diagnosis can have a
direct influence on the treatment and well-being of patients, transparency is
of utmost importance for safe transition from lab research to real world
clinical practice. This paper provides a comprehensive overview of current
state-of-the-art in explaining and interpreting Deep Learning based algorithms
in applications of medical research and diagnosis of diseases. We discuss early
achievements in development of explainable AI for validation of known disease
criteria, exploration of new potential biomarkers, as well as methods for the
subsequent correction of AI models. Various explanation methods like visual,
textual, post-hoc, ante-hoc, local and global have been thoroughly and
critically analyzed. Subsequently, we also highlight some of the remaining
challenges that stand in the way of practical applications of AI as a clinical
decision support tool and provide recommendations for the direction of future
research.
| [
{
"version": "v1",
"created": "Thu, 26 Nov 2020 08:08:19 GMT"
}
] | 1,606,694,400,000 | [
[
"Lucieri",
"Adriano",
""
],
[
"Bajwa",
"Muhammad Naseer",
""
],
[
"Dengel",
"Andreas",
""
],
[
"Ahmed",
"Sheraz",
""
]
] |
2011.13203 | Malvin Gattinger | Hans van Ditmarsch, Malvin Gattinger, Rahim Ramezanian | Everyone Knows that Everyone Knows: Gossip Protocols for Super Experts | null | Studia Logica 2023 | 10.1007/s11225-022-10032-3 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | A gossip protocol is a procedure for sharing secrets in a network. The basic
action in a gossip protocol is a pairwise message exchange (telephone call)
wherein the calling agents exchange all the secrets they know. An agent who
knows all secrets is an expert. The usual termination condition is that all
agents are experts. Instead, we explore protocols wherein the termination
condition is that all agents know that all agents are experts. We call such
agents super experts. We also investigate gossip protocols that are common
knowledge among the agents. Additionally, we model that agents who are super
experts do not make and do not answer calls, and that this is common knowledge.
We investigate conditions under which protocols terminate, both in the
synchronous case, where there is a global clock, and in the asynchronous case,
where there is not. We show that a commonly known protocol with engaged agents
may terminate faster than the same commonly known protocol without engaged
agents.
| [
{
"version": "v1",
"created": "Thu, 26 Nov 2020 09:57:04 GMT"
},
{
"version": "v2",
"created": "Thu, 17 Mar 2022 09:48:33 GMT"
},
{
"version": "v3",
"created": "Thu, 22 Dec 2022 21:03:23 GMT"
}
] | 1,679,443,200,000 | [
[
"van Ditmarsch",
"Hans",
""
],
[
"Gattinger",
"Malvin",
""
],
[
"Ramezanian",
"Rahim",
""
]
] |
2011.13277 | Damien Pellier | Maxence Grand, Humbert Fiorino, Damien Pellier | AMLSI: A Novel Accurate Action Model Learning Algorithm | 8 pages | Proceedings of the International Workshop on Knowledge Engineering
for Planning and Scheduling (KEPS), ICAPS, Oct 2020, Nancy, France | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This paper presents new approach based on grammar induction called AMLSI
Action Model Learning with State machine Interactions. The AMLSI approach does
not require a training dataset of plan traces to work. AMLSI proceeds by trial
and error: it queries the system to learn with randomly generated action
sequences, and it observes the state transitions of the system, then AMLSI
returns a PDDL domain corresponding to the system. A key issue for domain
learning is the ability to plan with the learned domains. It often happens that
a small learning error leads to a domain that is unusable for planning. Unlike
other algorithms, we show that AMLSI is able to lift this lock by learning
domains from partial and noisy observations with sufficient accuracy to allow
planners to solve new problems.
| [
{
"version": "v1",
"created": "Thu, 26 Nov 2020 13:25:08 GMT"
}
] | 1,606,694,400,000 | [
[
"Grand",
"Maxence",
""
],
[
"Fiorino",
"Humbert",
""
],
[
"Pellier",
"Damien",
""
]
] |
2011.13297 | Damien Pellier | Damien Pellier, Humbert Fiorino | Totally and Partially Ordered Hierarchical Planners in PDDL4J Library | 2 pages | Proceedings of the International Planning Competition, ICAPS,
Nancy, France, 2020 | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this paper, we outline the implementation of the TFD (Totally Ordered Fast
Downward) and the PFD (Partially ordered Fast Downward) hierarchical planners
that participated in the first HTN IPC competition in 2020. These two planners
are based on forward-chaining task decomposition coupled with a compact
grounding of actions, methods, tasks and HTN problems.
| [
{
"version": "v1",
"created": "Thu, 26 Nov 2020 14:00:37 GMT"
}
] | 1,606,694,400,000 | [
[
"Pellier",
"Damien",
""
],
[
"Fiorino",
"Humbert",
""
]
] |
2011.13464 | Jane Wang | Jane X. Wang | Meta-learning in natural and artificial intelligence | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Meta-learning, or learning to learn, has gained renewed interest in recent
years within the artificial intelligence community. However, meta-learning is
incredibly prevalent within nature, has deep roots in cognitive science and
psychology, and is currently studied in various forms within neuroscience. The
aim of this review is to recast previous lines of research in the study of
biological intelligence within the lens of meta-learning, placing these works
into a common framework. More recent points of interaction between AI and
neuroscience will be discussed, as well as interesting new directions that
arise under this perspective.
| [
{
"version": "v1",
"created": "Thu, 26 Nov 2020 20:21:39 GMT"
}
] | 1,606,694,400,000 | [
[
"Wang",
"Jane X.",
""
]
] |
2011.13572 | Sijie Mai | Sijie Mai, Songlong Xing, Jiaxuan He, Ying Zeng, Haifeng Hu | Analyzing Unaligned Multimodal Sequence via Graph Convolution and Graph
Pooling Fusion | preprint, work in progress | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we study the task of multimodal sequence analysis which aims
to draw inferences from visual, language and acoustic sequences. A majority of
existing works generally focus on aligned fusion, mostly at word level, of the
three modalities to accomplish this task, which is impractical in real-world
scenarios. To overcome this issue, we seek to address the task of multimodal
sequence analysis on unaligned modality sequences which is still relatively
underexplored and also more challenging. Recurrent neural network (RNN) and its
variants are widely used in multimodal sequence analysis, but they are
susceptible to the issues of gradient vanishing/explosion and high time
complexity due to its recurrent nature. Therefore, we propose a novel model,
termed Multimodal Graph, to investigate the effectiveness of graph neural
networks (GNN) on modeling multimodal sequential data. The graph-based
structure enables parallel computation in time dimension and can learn longer
temporal dependency in long unaligned sequences. Specifically, our Multimodal
Graph is hierarchically structured to cater to two stages, i.e., intra- and
inter-modal dynamics learning. For the first stage, a graph convolutional
network is employed for each modality to learn intra-modal dynamics. In the
second stage, given that the multimodal sequences are unaligned, the commonly
considered word-level fusion does not pertain. To this end, we devise a graph
pooling fusion network to automatically learn the associations between various
nodes from different modalities. Additionally, we define multiple ways to
construct the adjacency matrix for sequential data. Experimental results
suggest that our graph-based model reaches state-of-the-art performance on two
benchmark datasets.
| [
{
"version": "v1",
"created": "Fri, 27 Nov 2020 06:12:14 GMT"
},
{
"version": "v2",
"created": "Wed, 2 Dec 2020 04:36:37 GMT"
},
{
"version": "v3",
"created": "Fri, 23 Apr 2021 17:09:39 GMT"
}
] | 1,619,395,200,000 | [
[
"Mai",
"Sijie",
""
],
[
"Xing",
"Songlong",
""
],
[
"He",
"Jiaxuan",
""
],
[
"Zeng",
"Ying",
""
],
[
"Hu",
"Haifeng",
""
]
] |
2011.13636 | Miao Qin | Miao Qin, Yongchuan Tang | Combination of interval-valued belief structures based on belief entropy | Simply using MDPI as a template. It does not indicate that this
article will be submitted to Entropy in the future | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates the issues of combination and normalization of
interval-valued belief structures within the framework of Dempster-Shafer
theory of evidence. Existing approaches are reviewed and thoroughly analyzed.
The advantages and drawbacks of previous approach are presented. A new
optimality approach based on uncertainty measure is developed, where the
problem of combining interval-valued belief structures degenerates into
combining basic probability assignments. Numerical examples are provided to
illustrate the rationality of the proposed approach.
| [
{
"version": "v1",
"created": "Fri, 27 Nov 2020 10:09:52 GMT"
}
] | 1,606,694,400,000 | [
[
"Qin",
"Miao",
""
],
[
"Tang",
"Yongchuan",
""
]
] |
2011.13689 | Andrei Haidu | Andrei Haidu and Michael Beetz | Automated acquisition of structured, semantic models of manipulation
activities from human VR demonstration | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper we present a system capable of collecting and annotating, human
performed, robot understandable, everyday activities from virtual environments.
The human movements are mapped in the simulated world using off-the-shelf
virtual reality devices with full body, and eye tracking capabilities. All the
interactions in the virtual world are physically simulated, thus movements and
their effects are closely relatable to the real world. During the activity
execution, a subsymbolic data logger is recording the environment and the human
gaze on a per-frame basis, enabling offline scene reproduction and replays.
Coupled with the physics engine, online monitors (symbolic data loggers) are
parsing (using various grammars) and recording events, actions, and their
effects in the simulated world.
| [
{
"version": "v1",
"created": "Fri, 27 Nov 2020 11:58:32 GMT"
}
] | 1,606,694,400,000 | [
[
"Haidu",
"Andrei",
""
],
[
"Beetz",
"Michael",
""
]
] |
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