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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2011.13721 | Alexis De Colnet | Alexis de Colnet and Stefan Mengel | Lower Bounds for Approximate Knowledge Compilation | 11 pages, including appendices | null | 10.24963/ijcai.2020/254 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge compilation studies the trade-off between succinctness and
efficiency of different representation languages. For many languages, there are
known strong lower bounds on the representation size, but recent work shows
that, for some languages, one can bypass these bounds using approximate
compilation. The idea is to compile an approximation of the knowledge for which
the number of errors can be controlled. We focus on circuits in deterministic
decomposable negation normal form (d-DNNF), a compilation language suitable in
contexts such as probabilistic reasoning, as it supports efficient model
counting and probabilistic inference. Moreover, there are known size lower
bounds for d-DNNF which by relaxing to approximation one might be able to
avoid. In this paper we formalize two notions of approximation: weak
approximation which has been studied before in the decision diagram literature
and strong approximation which has been used in recent algorithmic results. We
then show lower bounds for approximation by d-DNNF, complementing the positive
results from the literature.
| [
{
"version": "v1",
"created": "Fri, 27 Nov 2020 13:11:32 GMT"
}
] | 1,606,694,400,000 | [
[
"de Colnet",
"Alexis",
""
],
[
"Mengel",
"Stefan",
""
]
] |
2011.13782 | Michael Luo Zhiyu | Rachit Dubey, Erin Grant, Michael Luo, Karthik Narasimhan, Thomas
Griffiths | Connecting Context-specific Adaptation in Humans to Meta-learning | 9 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Cognitive control, the ability of a system to adapt to the demands of a task,
is an integral part of cognition. A widely accepted fact about cognitive
control is that it is context-sensitive: Adults and children alike infer
information about a task's demands from contextual cues and use these
inferences to learn from ambiguous cues. However, the precise way in which
people use contextual cues to guide adaptation to a new task remains poorly
understood. This work connects the context-sensitive nature of cognitive
control to a method for meta-learning with context-conditioned adaptation. We
begin by identifying an essential difference between human learning and current
approaches to meta-learning: In contrast to humans, existing meta-learning
algorithms do not make use of task-specific contextual cues but instead rely
exclusively on online feedback in the form of task-specific labels or rewards.
To remedy this, we introduce a framework for using contextual information about
a task to guide the initialization of task-specific models before adaptation to
online feedback. We show how context-conditioned meta-learning can capture
human behavior in a cognitive task and how it can be scaled to improve the
speed of learning in various settings, including few-shot classification and
low-sample reinforcement learning. Our work demonstrates that guiding
meta-learning with task information can capture complex, human-like behavior,
thereby deepening our understanding of cognitive control.
| [
{
"version": "v1",
"created": "Fri, 27 Nov 2020 15:31:39 GMT"
},
{
"version": "v2",
"created": "Tue, 1 Dec 2020 01:33:18 GMT"
}
] | 1,606,867,200,000 | [
[
"Dubey",
"Rachit",
""
],
[
"Grant",
"Erin",
""
],
[
"Luo",
"Michael",
""
],
[
"Narasimhan",
"Karthik",
""
],
[
"Griffiths",
"Thomas",
""
]
] |
2011.14016 | Alan Lindsay | Alan Lindsay, Bart Craenen, Sara Dalzel-Job, Robin L. Hill, Ronald P.
A. Petrick | Investigating Human Response, Behaviour, and Preference in Joint-Task
Interaction | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Human interaction relies on a wide range of signals, including non-verbal
cues. In order to develop effective Explainable Planning (XAIP) agents it is
important that we understand the range and utility of these communication
channels. Our starting point is existing results from joint task interaction
and their study in cognitive science. Our intention is that these lessons can
inform the design of interaction agents -- including those using planning
techniques -- whose behaviour is conditioned on the user's response, including
affective measures of the user (i.e., explicitly incorporating the user's
affective state within the planning model). We have identified several concepts
at the intersection of plan-based agent behaviour and joint task interaction
and have used these to design two agents: one reactive and the other partially
predictive. We have designed an experiment in order to examine human behaviour
and response as they interact with these agents. In this paper we present the
designed study and the key questions that are being investigated. We also
present the results from an empirical analysis where we examined the behaviour
of the two agents for simulated users.
| [
{
"version": "v1",
"created": "Fri, 27 Nov 2020 22:16:59 GMT"
}
] | 1,606,780,800,000 | [
[
"Lindsay",
"Alan",
""
],
[
"Craenen",
"Bart",
""
],
[
"Dalzel-Job",
"Sara",
""
],
[
"Hill",
"Robin L.",
""
],
[
"Petrick",
"Ronald P. A.",
""
]
] |
2011.14124 | Edward Lockhart | Edward Lockhart, Neil Burch, Nolan Bard, Sebastian Borgeaud, Tom
Eccles, Lucas Smaira, Ray Smith | Human-Agent Cooperation in Bridge Bidding | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a human-compatible reinforcement-learning approach to a
cooperative game, making use of a third-party hand-coded human-compatible bot
to generate initial training data and to perform initial evaluation. Our
learning approach consists of imitation learning, search, and policy iteration.
Our trained agents achieve a new state-of-the-art for bridge bidding in three
settings: an agent playing in partnership with a copy of itself; an agent
partnering a pre-existing bot; and an agent partnering a human player.
| [
{
"version": "v1",
"created": "Sat, 28 Nov 2020 12:37:02 GMT"
}
] | 1,606,780,800,000 | [
[
"Lockhart",
"Edward",
""
],
[
"Burch",
"Neil",
""
],
[
"Bard",
"Nolan",
""
],
[
"Borgeaud",
"Sebastian",
""
],
[
"Eccles",
"Tom",
""
],
[
"Smaira",
"Lucas",
""
],
[
"Smith",
"Ray",
""
]
] |
2011.14475 | Eduardo C\'esar Garrido-Merch\'an | Eduardo C. Garrido-Merch\'an and Martin Molina and Francisco M.
Mendoza | An Artificial Consciousness Model and its relations with Philosophy of
Mind | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work seeks to study the beneficial properties that an autonomous agent
can obtain by implementing a cognitive architecture similar to the one of
conscious beings. Along this document, a conscious model of autonomous agent
based in a global workspace architecture is presented. We describe how this
agent is viewed from different perspectives of philosophy of mind, being
inspired by their ideas. The goal of this model is to create autonomous agents
able to navigate within an environment composed of multiple independent
magnitudes, adapting to its surroundings in order to find the best possible
position in base of its inner preferences. The purpose of the model is to test
the effectiveness of many cognitive mechanisms that are incorporated, such as
an attention mechanism for magnitude selection, pos-session of inner feelings
and preferences, usage of a memory system to storage beliefs and past
experiences, and incorporating a global workspace which controls and integrates
information processed by all the subsystem of the model. We show in a large
experiment set how an autonomous agent can benefit from having a cognitive
architecture such as the one described.
| [
{
"version": "v1",
"created": "Mon, 30 Nov 2020 00:24:17 GMT"
},
{
"version": "v2",
"created": "Tue, 1 Dec 2020 17:27:10 GMT"
}
] | 1,606,867,200,000 | [
[
"Garrido-Merchán",
"Eduardo C.",
""
],
[
"Molina",
"Martin",
""
],
[
"Mendoza",
"Francisco M.",
""
]
] |
2011.15067 | Marlene Berke | Marlene Berke, Mario Belledonne, and Julian Jara-Ettinger | Learning a metacognition for object perception | SVRHM workshop at NeurIPS | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Beyond representing the external world, humans also represent their own
cognitive processes. In the context of perception, this metacognition helps us
identify unreliable percepts, such as when we recognize that we are seeing an
illusion. Here we propose MetaGen, a model for the unsupervised learning of
metacognition. In MetaGen, metacognition is expressed as a generative model of
how a perceptual system produces noisy percepts. Using basic principles of how
the world works (such as object permanence, part of infants' core knowledge),
MetaGen jointly infers the objects in the world causing the percepts and a
representation of its own perceptual system. MetaGen can then use this
metacognition to infer which objects are actually present in the world. On
simulated data, we find that MetaGen quickly learns a metacognition and
improves overall accuracy, outperforming models that lack a metacognition.
| [
{
"version": "v1",
"created": "Mon, 30 Nov 2020 18:05:00 GMT"
}
] | 1,606,780,800,000 | [
[
"Berke",
"Marlene",
""
],
[
"Belledonne",
"Mario",
""
],
[
"Jara-Ettinger",
"Julian",
""
]
] |
2012.00583 | Xiaohan Cheng | Xiaohan Cheng | Obtain Employee Turnover Rate and Optimal Reduction Strategy Based On
Neural Network and Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nowadays, human resource is an important part of various resources of
enterprises. For enterprises, high-loyalty and high-quality talented persons
are often the core competitiveness of enterprises. Therefore, it is of great
practical significance to predict whether employees leave and reduce the
turnover rate of employees. First, this paper established a multi-layer
perceptron predictive model of employee turnover rate. A model based on Sarsa
which is a kind of reinforcement learning algorithm is proposed to
automatically generate a set of strategies to reduce the employee turnover
rate. These strategies are a collection of strategies that can reduce the
employee turnover rate the most and cost less from the perspective of the
enterprise, and can be used as a reference plan for the enterprise to optimize
the employee system. The experimental results show that the algorithm can
indeed improve the efficiency and accuracy of the specific strategy.
| [
{
"version": "v1",
"created": "Tue, 1 Dec 2020 15:48:23 GMT"
}
] | 1,606,867,200,000 | [
[
"Cheng",
"Xiaohan",
""
]
] |
2012.01410 | Daniele Francesco Santamaria | Domenico Cantone, Carmelo Fabio Longo, Marianna Nicolosi-Asmundo,
Daniele Francesco Santamaria, Corrado Santoro | Ontological Smart Contracts in OASIS: Ontology for Agents, Systems, and
Integration of Services (Extended Version) | Please cite
https://www.scopus.com/record/display.uri?eid=2-s2.0-85130258663&origin=resultslist | Intelligent Distributed Computing XIV, Studies in Computational
Intelligence 1026, 2021 | 10.1007/978-3-030-96627-0_22 | Chapter 22, pp. 237--247 | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this contribution we extend an ontology for modelling agents and their
interactions, called Ontology for Agents, Systems, and Integration of Services
(in short, OASIS), with conditionals and ontological smart contracts (in short,
OSCs). OSCs are ontological representations of smart contracts that allow to
establish responsibilities and authorizations among agents and set agreements,
whereas conditionals allow one to restrict and limit agent interactions, define
activation mechanisms that trigger agent actions, and define constraints and
contract terms on OSCs. Conditionals and OSCs, as defined in OASIS, are applied
to extend with ontological capabilities digital public ledgers such as the
blockchain and smart contracts implemented on it. We will also sketch the
architecture of a framework based on the OASIS definition of OSCs that exploits
the Ethereum platform and the Interplanetary File System.
| [
{
"version": "v1",
"created": "Wed, 2 Dec 2020 18:58:26 GMT"
},
{
"version": "v2",
"created": "Fri, 10 Sep 2021 14:39:54 GMT"
},
{
"version": "v3",
"created": "Tue, 14 Sep 2021 19:56:58 GMT"
},
{
"version": "v4",
"created": "Tue, 20 Feb 2024 21:37:17 GMT"
}
] | 1,708,560,000,000 | [
[
"Cantone",
"Domenico",
""
],
[
"Longo",
"Carmelo Fabio",
""
],
[
"Nicolosi-Asmundo",
"Marianna",
""
],
[
"Santamaria",
"Daniele Francesco",
""
],
[
"Santoro",
"Corrado",
""
]
] |
2012.01569 | Uwe Aickelin | Hadi A. Khorshidi and Uwe Aickelin | Multicriteria Group Decision-Making Under Uncertainty Using Interval
Data and Cloud Models | Journal of the Operational Research Society, 2020 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this study, we propose a multicriteria group decision making (MCGDM)
algorithm under uncertainty where data is collected as intervals. The proposed
MCGDM algorithm aggregates the data, determines the optimal weights for
criteria and ranks alternatives with no further input. The intervals give
flexibility to experts in assessing alternatives against criteria and provide
an opportunity to gain maximum information. We also propose a novel method to
aggregate expert judgements using cloud models. We introduce an experimental
approach to check the validity of the aggregation method. After that, we use
the aggregation method for an MCGDM problem. Here, we find the optimal weights
for each criterion by proposing a bilevel optimisation model. Then, we extend
the technique for order of preference by similarity to ideal solution (TOPSIS)
for data based on cloud models to prioritise alternatives. As a result, the
algorithm can gain information from decision makers with different levels of
uncertainty and examine alternatives with no more information from
decision-makers. The proposed MCGDM algorithm is implemented on a case study of
a cybersecurity problem to illustrate its feasibility and effectiveness. The
results verify the robustness and validity of the proposed MCGDM using
sensitivity analysis and comparison with other existing algorithms.
| [
{
"version": "v1",
"created": "Tue, 1 Dec 2020 06:34:48 GMT"
}
] | 1,607,040,000,000 | [
[
"Khorshidi",
"Hadi A.",
""
],
[
"Aickelin",
"Uwe",
""
]
] |
2012.02194 | Uwe Aickelin | Justin Kane Gunn, Hadi Akbarzadeh Khorshidi, Uwe Aickelin | Methods of ranking for aggregated fuzzy numbers from interval-valued
data | 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper primarily presents two methods of ranking aggregated fuzzy numbers
from intervals using the Interval Agreement Approach (IAA). The two proposed
ranking methods within this study contain the combination and application of
previously proposed similarity measures, along with attributes novel to that of
aggregated fuzzy numbers from interval-valued data. The shortcomings of
previous measures, along with the improvements of the proposed methods, are
illustrated using both a synthetic and real-world application. The real-world
application regards the Technique for Order of Preference by Similarity to
Ideal Solution (TOPSIS) algorithm, modified to include both the previous and
newly proposed methods.
| [
{
"version": "v1",
"created": "Thu, 3 Dec 2020 02:56:15 GMT"
}
] | 1,607,299,200,000 | [
[
"Gunn",
"Justin Kane",
""
],
[
"Khorshidi",
"Hadi Akbarzadeh",
""
],
[
"Aickelin",
"Uwe",
""
]
] |
2012.02903 | Christine Allen-Blanchette | Christine Allen-Blanchette and Kostas Daniilidis | Joint Estimation of Image Representations and their Lie Invariants | Resolves typographical errors | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Images encode both the state of the world and its content. The former is
useful for tasks such as planning and control, and the latter for
classification. The automatic extraction of this information is challenging
because of the high-dimensionality and entangled encoding inherent to the image
representation. This article introduces two theoretical approaches aimed at the
resolution of these challenges. The approaches allow for the interpolation and
extrapolation of images from an image sequence by joint estimation of the image
representation and the generators of the sequence dynamics. In the first
approach, the image representations are learned using probabilistic PCA
\cite{tipping1999probabilistic}. The linear-Gaussian conditional distributions
allow for a closed form analytical description of the latent distributions but
assumes the underlying image manifold is a linear subspace. In the second
approach, the image representations are learned using probabilistic nonlinear
PCA which relieves the linear manifold assumption at the cost of requiring a
variational approximation of the latent distributions. In both approaches, the
underlying dynamics of the image sequence are modelled explicitly to
disentangle them from the image representations. The dynamics themselves are
modelled with Lie group structure which enforces the desirable properties of
smoothness and composability of inter-image transformations.
| [
{
"version": "v1",
"created": "Sat, 5 Dec 2020 00:07:41 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Dec 2020 13:28:42 GMT"
}
] | 1,607,472,000,000 | [
[
"Allen-Blanchette",
"Christine",
""
],
[
"Daniilidis",
"Kostas",
""
]
] |
2012.02947 | Nikhil Krishnaswamy | Nikhil Krishnaswamy and James Pustejovsky | Neurosymbolic AI for Situated Language Understanding | 18 pages + refs, 16 figures, presented at the 8th Annual Conference
on Advances in Cognitive Systems (ACS), 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, data-intensive AI, particularly the domain of natural
language processing and understanding, has seen significant progress driven by
the advent of large datasets and deep neural networks that have sidelined more
classic AI approaches to the field. These systems can apparently demonstrate
sophisticated linguistic understanding or generation capabilities, but often
fail to transfer their skills to situations they have not encountered before.
We argue that computational situated grounding provides a solution to some of
these learning challenges by creating situational representations that both
serve as a formal model of the salient phenomena, and contain rich amounts of
exploitable, task-appropriate data for training new, flexible computational
models. Our model reincorporates some ideas of classic AI into a framework of
neurosymbolic intelligence, using multimodal contextual modeling of interactive
situations, events, and object properties. We discuss how situated grounding
provides diverse data and multiple levels of modeling for a variety of AI
learning challenges, including learning how to interact with object
affordances, learning semantics for novel structures and configurations, and
transferring such learned knowledge to new objects and situations.
| [
{
"version": "v1",
"created": "Sat, 5 Dec 2020 05:03:28 GMT"
}
] | 1,607,385,600,000 | [
[
"Krishnaswamy",
"Nikhil",
""
],
[
"Pustejovsky",
"James",
""
]
] |
2012.03058 | Xingyu Zhao | Xingyu Zhao, Wei Huang, Xiaowei Huang, Valentin Robu, David Flynn | BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations | Preprint accepted by UAI2021. The final version to appear in the
UAI2021 volume of Proceedings of Machine Learning Research | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Given the pressing need for assuring algorithmic transparency, Explainable AI
(XAI) has emerged as one of the key areas of AI research. In this paper, we
develop a novel Bayesian extension to the LIME framework, one of the most
widely used approaches in XAI -- which we call BayLIME. Compared to LIME,
BayLIME exploits prior knowledge and Bayesian reasoning to improve both the
consistency in repeated explanations of a single prediction and the robustness
to kernel settings. BayLIME also exhibits better explanation fidelity than the
state-of-the-art (LIME, SHAP and GradCAM) by its ability to integrate prior
knowledge from, e.g., a variety of other XAI techniques, as well as
verification and validation (V&V) methods. We demonstrate the desirable
properties of BayLIME through both theoretical analysis and extensive
experiments.
| [
{
"version": "v1",
"created": "Sat, 5 Dec 2020 15:41:52 GMT"
},
{
"version": "v2",
"created": "Thu, 13 May 2021 20:17:32 GMT"
},
{
"version": "v3",
"created": "Wed, 19 May 2021 12:28:47 GMT"
},
{
"version": "v4",
"created": "Thu, 20 May 2021 07:46:30 GMT"
},
{
"version": "v5",
"created": "Sat, 29 May 2021 07:49:00 GMT"
}
] | 1,622,505,600,000 | [
[
"Zhao",
"Xingyu",
""
],
[
"Huang",
"Wei",
""
],
[
"Huang",
"Xiaowei",
""
],
[
"Robu",
"Valentin",
""
],
[
"Flynn",
"David",
""
]
] |
2012.03119 | Nicolas Prevot | Nicolas Prevot | GpuShareSat: a SAT solver using the GPU for clause sharing | 13 pages, 4 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We describe a SAT solver using both the GPU (CUDA) and the CPU with a new
clause exchange strategy. The CPU runs a classic multithreaded CDCL SAT solver.
EachCPU thread exports all the clauses it learns to the GPU. The GPU makes a
heavy usage of bitwise operations. It notices when a clause would have been
used by a CPU thread and notifies that thread, in which case it imports that
clause. This relies on the GPU repeatedly testing millions of clauses against
hundreds of assignments. All the clauses are tested independantly from each
other (which allows the GPU massively parallel approach), but against all the
assignments at once, using bitwise operations. This allows CPU threads to only
import clauses which would have been useful for them. Our solver is based upon
glucose-syrup. Experiments show that this leads to a strong performance
improvement, with 22 more instances solved on the SAT 2020 competition than
glucose-syrup.
| [
{
"version": "v1",
"created": "Sat, 5 Dec 2020 20:57:23 GMT"
}
] | 1,607,385,600,000 | [
[
"Prevot",
"Nicolas",
""
]
] |
2012.03190 | Xuejiao Tang | Xuejiao Tang, Jiong Qiu, Ruijun Chen, Wenbin Zhang, Vasileios
Iosifidis, Zhen Liu, Wei Meng, Mingli Zhang and Ji Zhang | A Data-driven Human Responsibility Management System | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | An ideal safe workplace is described as a place where staffs fulfill
responsibilities in a well-organized order, potential hazardous events are
being monitored in real-time, as well as the number of accidents and relevant
damages are minimized. However, occupational-related death and injury are still
increasing and have been highly attended in the last decades due to the lack of
comprehensive safety management. A smart safety management system is therefore
urgently needed, in which the staffs are instructed to fulfill responsibilities
as well as automating risk evaluations and alerting staffs and departments when
needed. In this paper, a smart system for safety management in the workplace
based on responsibility big data analysis and the internet of things (IoT) are
proposed. The real world implementation and assessment demonstrate that the
proposed systems have superior accountability performance and improve the
responsibility fulfillment through real-time supervision and self-reminder.
| [
{
"version": "v1",
"created": "Sun, 6 Dec 2020 06:16:51 GMT"
}
] | 1,607,385,600,000 | [
[
"Tang",
"Xuejiao",
""
],
[
"Qiu",
"Jiong",
""
],
[
"Chen",
"Ruijun",
""
],
[
"Zhang",
"Wenbin",
""
],
[
"Iosifidis",
"Vasileios",
""
],
[
"Liu",
"Zhen",
""
],
[
"Meng",
"Wei",
""
],
[
"Zhang",
"Mingli",
""
],
[
"Zhang",
"Ji",
""
]
] |
2012.03204 | Hangtian Jia | Hangtian Jia, Yujing Hu, Yingfeng Chen, Chunxu Ren, Tangjie Lv,
Changjie Fan, Chongjie Zhang | Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game
Environment for Multi-agent Reinforcement Learning | 7 pages,12 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The development of deep reinforcement learning (DRL) has benefited from the
emergency of a variety type of game environments where new challenging problems
are proposed and new algorithms can be tested safely and quickly, such as Board
games, RTS, FPS, and MOBA games. However, many existing environments lack
complexity and flexibility and assume the actions are synchronously executed in
multi-agent settings, which become less valuable. We introduce the Fever
Basketball game, a novel reinforcement learning environment where agents are
trained to play basketball game. It is a complex and challenging environment
that supports multiple characters, multiple positions, and both the
single-agent and multi-agent player control modes. In addition, to better
simulate real-world basketball games, the execution time of actions differs
among players, which makes Fever Basketball a novel asynchronized environment.
We evaluate commonly used multi-agent algorithms of both independent learners
and joint-action learners in three game scenarios with varying difficulties,
and heuristically propose two baseline methods to diminish the extra
non-stationarity brought by asynchronism in Fever Basketball Benchmarks.
Besides, we propose an integrated curricula training (ICT) framework to better
handle Fever Basketball problems, which includes several game-rule based
cascading curricula learners and a coordination curricula switcher focusing on
enhancing coordination within the team. The results show that the game remains
challenging and can be used as a benchmark environment for studies like
long-time horizon, sparse rewards, credit assignment, and non-stationarity,
etc. in multi-agent settings.
| [
{
"version": "v1",
"created": "Sun, 6 Dec 2020 07:51:59 GMT"
}
] | 1,607,385,600,000 | [
[
"Jia",
"Hangtian",
""
],
[
"Hu",
"Yujing",
""
],
[
"Chen",
"Yingfeng",
""
],
[
"Ren",
"Chunxu",
""
],
[
"Lv",
"Tangjie",
""
],
[
"Fan",
"Changjie",
""
],
[
"Zhang",
"Chongjie",
""
]
] |
2012.03527 | Sandi Baressi \v{S}egota | Nikola An{\dj}eli\'c, Sandi Baressi \v{S}egota, Ivan Lorencin and
Zlatan Car | Estimation of Gas Turbine Shaft Torque and Fuel Flow of a CODLAG
Propulsion System Using Genetic Programming Algorithm | 25 pages, 5 figures, 7 tables | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper, the publicly available dataset of condition based maintenance
of combined diesel-electric and gas (CODLAG) propulsion system for ships has
been utilized to obtain symbolic expressions which could estimate gas turbine
shaft torque and fuel flow using genetic programming (GP) algorithm. The entire
dataset consists of 11934 samples that was divided into training and testing
portions of dataset in an 80:20 ratio. The training dataset used to train the
GP algorithm to obtain symbolic expressions for gas turbine shaft torque and
fuel flow estimation consisted of 9548 samples. The best symbolic expressions
obtained for gas turbine shaft torque and fuel flow estimation were obtained
based on their $R^2$ score generated as a result of the application of the
testing portion of the dataset on the aforementioned symbolic expressions. The
testing portion of the dataset consisted of 2386 samples. The three best
symbolic expressions obtained for gas turbine shaft torque estimation generated
$R^2$ scores of 0.999201, 0.999296, and 0.999374, respectively. The three best
symbolic expressions obtained for fuel flow estimation generated $R^2$ scores
of 0.995495, 0.996465, and 0.996487, respectively.
| [
{
"version": "v1",
"created": "Mon, 7 Dec 2020 08:39:58 GMT"
}
] | 1,607,385,600,000 | [
[
"Anđelić",
"Nikola",
""
],
[
"Šegota",
"Sandi Baressi",
""
],
[
"Lorencin",
"Ivan",
""
],
[
"Car",
"Zlatan",
""
]
] |
2012.03624 | Geoffrey Harris | Geoff Harris | Improving Constraint Satisfaction Algorithm Efficiency for the
AllDifferent Constraint | *sigh* - it has been gently and kindly pointed out to me that I have
simply re-discovered the channelling of constraints across alternate problem
specifications. Gosh this is oddly amusing albeit embarrassing! | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Combinatorial problems stated as Constraint Satisfaction Problems (CSP) are
examined. It is shown by example that any algorithm designed for the original
CSP, and involving the AllDifferent constraint, has at least the same level of
efficacy when simultaneously applied to both the original and its complementary
problem. The 1-to-1 mapping employed to transform a CSP to its complementary
problem, which is also a CSP, is introduced. This "Dual CSP" method and its
application are outlined. The analysis of several random problem instances
demonstrate the benefits of this method for variable domain reduction compared
to the standard approach to CSP. Extensions to additional constraints other
than AllDifferent, as well as the use of hybrid algorithms, are proposed as
candidates for this Dual CSP method.
| [
{
"version": "v1",
"created": "Mon, 7 Dec 2020 12:14:55 GMT"
},
{
"version": "v2",
"created": "Sun, 13 Dec 2020 09:59:33 GMT"
}
] | 1,607,990,400,000 | [
[
"Harris",
"Geoff",
""
]
] |
2012.03721 | Uwe Aickelin | Justin Kane Gunn, Hadi Akbarzadeh Khorshidi, Uwe Aickelin | Similarity measure for aggregated fuzzy numbers from interval-valued
data | Soft Computing Letters, 100002 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper presents a method to compute the degree of similarity between two
aggregated fuzzy numbers from intervals using the Interval Agreement Approach
(IAA). The similarity measure proposed within this study contains several
features and attributes, of which are novel to aggregated fuzzy numbers. The
attributes completely redefined or modified within this study include area,
perimeter, centroids, quartiles and the agreement ratio. The recommended
weighting for each feature has been learned using Principal Component Analysis
(PCA). Furthermore, an illustrative example is provided to detail the
application and potential future use of the similarity measure.
| [
{
"version": "v1",
"created": "Fri, 4 Dec 2020 03:44:40 GMT"
}
] | 1,607,385,600,000 | [
[
"Gunn",
"Justin Kane",
""
],
[
"Khorshidi",
"Hadi Akbarzadeh",
""
],
[
"Aickelin",
"Uwe",
""
]
] |
2012.04216 | Ben Hutchinson | Angie Peng and Jeff Naecker and Ben Hutchinson and Andrew Smart and
Nyalleng Moorosi | Fairness Preferences, Actual and Hypothetical: A Study of Crowdworker
Incentives | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How should we decide which fairness criteria or definitions to adopt in
machine learning systems? To answer this question, we must study the fairness
preferences of actual users of machine learning systems. Stringent parity
constraints on treatment or impact can come with trade-offs, and may not even
be preferred by the social groups in question (Zafar et al., 2017). Thus it
might be beneficial to elicit what the group's preferences are, rather than
rely on a priori defined mathematical fairness constraints. Simply asking for
self-reported rankings of users is challenging because research has shown that
there are often gaps between people's stated and actual preferences(Bernheim et
al., 2013).
This paper outlines a research program and experimental designs for
investigating these questions. Participants in the experiments are invited to
perform a set of tasks in exchange for a base payment--they are told upfront
that they may receive a bonus later on, and the bonus could depend on some
combination of output quantity and quality. The same group of workers then
votes on a bonus payment structure, to elicit preferences. The voting is
hypothetical (not tied to an outcome) for half the group and actual (tied to
the actual payment outcome) for the other half, so that we can understand the
relation between a group's actual preferences and hypothetical (stated)
preferences. Connections and lessons from fairness in machine learning are
explored.
| [
{
"version": "v1",
"created": "Tue, 8 Dec 2020 05:00:57 GMT"
}
] | 1,607,472,000,000 | [
[
"Peng",
"Angie",
""
],
[
"Naecker",
"Jeff",
""
],
[
"Hutchinson",
"Ben",
""
],
[
"Smart",
"Andrew",
""
],
[
"Moorosi",
"Nyalleng",
""
]
] |
2012.04424 | Stefan Mengel | Danel Le Berre, Pierre Marquis, Stefan Mengel, Romain Wallon | On Irrelevant Literals in Pseudo-Boolean Constraint Learning | published at IJCAI 2020 | null | 10.24963/ijcai.2020/160 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Learning pseudo-Boolean (PB) constraints in PB solvers exploiting cutting
planes based inference is not as well understood as clause learning in
conflict-driven clause learning solvers. In this paper, we show that PB
constraints derived using cutting planes may contain \emph{irrelevant
literals}, i.e., literals whose assigned values (whatever they are) never
change the truth value of the constraint. Such literals may lead to infer
constraints that are weaker than they should be, impacting the size of the
proof built by the solver, and thus also affecting its performance. This
suggests that current implementations of PB solvers based on cutting planes
should be reconsidered to prevent the generation of irrelevant literals.
Indeed, detecting and removing irrelevant literals is too expensive in practice
to be considered as an option (the associated problem is NP-hard.
| [
{
"version": "v1",
"created": "Tue, 8 Dec 2020 13:52:09 GMT"
}
] | 1,607,472,000,000 | [
[
"Berre",
"Danel Le",
""
],
[
"Marquis",
"Pierre",
""
],
[
"Mengel",
"Stefan",
""
],
[
"Wallon",
"Romain",
""
]
] |
2012.04442 | Michael Neumann | Michael Neumann, Sebastian Koralewski and Michael Beetz | URoboSim -- An Episodic Simulation Framework for Prospective Reasoning
in Robotic Agents | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Anticipating what might happen as a result of an action is an essential
ability humans have in order to perform tasks effectively. On the other hand,
robots capabilities in this regard are quite lacking. While machine learning is
used to increase the ability of prospection it is still limiting for novel
situations. A possibility to improve the prospection ability of robots is
through simulation of imagined motions and the physical results of these
actions. Therefore, we present URoboSim, a robot simulator that allows robots
to perform tasks as mental simulation before performing this task in reality.
We show the capabilities of URoboSim in form of mental simulations, generating
data for machine learning and the usage as belief state for a real robot.
| [
{
"version": "v1",
"created": "Tue, 8 Dec 2020 14:23:24 GMT"
}
] | 1,607,472,000,000 | [
[
"Neumann",
"Michael",
""
],
[
"Koralewski",
"Sebastian",
""
],
[
"Beetz",
"Michael",
""
]
] |
2012.04626 | Marc Rigter | Marc Rigter, Bruno Lacerda, Nick Hawes | Minimax Regret Optimisation for Robust Planning in Uncertain Markov
Decision Processes | Full version of AAAI 2021 paper, with corrigendum attached that
describes error in original paper | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The parameters for a Markov Decision Process (MDP) often cannot be specified
exactly. Uncertain MDPs (UMDPs) capture this model ambiguity by defining sets
which the parameters belong to. Minimax regret has been proposed as an
objective for planning in UMDPs to find robust policies which are not overly
conservative. In this work, we focus on planning for Stochastic Shortest Path
(SSP) UMDPs with uncertain cost and transition functions. We introduce a
Bellman equation to compute the regret for a policy. We propose a dynamic
programming algorithm that utilises the regret Bellman equation, and show that
it optimises minimax regret exactly for UMDPs with independent uncertainties.
For coupled uncertainties, we extend our approach to use options to enable a
trade off between computation and solution quality. We evaluate our approach on
both synthetic and real-world domains, showing that it significantly
outperforms existing baselines.
| [
{
"version": "v1",
"created": "Tue, 8 Dec 2020 18:48:14 GMT"
},
{
"version": "v2",
"created": "Sun, 12 Feb 2023 15:43:28 GMT"
}
] | 1,676,332,800,000 | [
[
"Rigter",
"Marc",
""
],
[
"Lacerda",
"Bruno",
""
],
[
"Hawes",
"Nick",
""
]
] |
2012.04751 | Sebastian Risi | Djordje Grbic, Rasmus Berg Palm, Elias Najarro, Claire Glanois,
Sebastian Risi | EvoCraft: A New Challenge for Open-Endedness | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces EvoCraft, a framework for Minecraft designed to study
open-ended algorithms. We introduce an API that provides an open-source Python
interface for communicating with Minecraft to place and track blocks. In
contrast to previous work in Minecraft that focused on learning to play the
game, the grand challenge we pose here is to automatically search for
increasingly complex artifacts in an open-ended fashion. Compared to other
environments used to study open-endedness, Minecraft allows the construction of
almost any kind of structure, including actuated machines with circuits and
mechanical components. We present initial baseline results in evolving simple
Minecraft creations through both interactive and automated evolution. While
evolution succeeds when tasked to grow a structure towards a specific target,
it is unable to find a solution when rewarded for creating a simple machine
that moves. Thus, EvoCraft offers a challenging new environment for automated
search methods (such as evolution) to find complex artifacts that we hope will
spur the development of more open-ended algorithms. A Python implementation of
the EvoCraft framework is available at:
https://github.com/real-itu/Evocraft-py.
| [
{
"version": "v1",
"created": "Tue, 8 Dec 2020 21:36:18 GMT"
}
] | 1,607,558,400,000 | [
[
"Grbic",
"Djordje",
""
],
[
"Palm",
"Rasmus Berg",
""
],
[
"Najarro",
"Elias",
""
],
[
"Glanois",
"Claire",
""
],
[
"Risi",
"Sebastian",
""
]
] |
2012.04759 | Yiming Xu | Yiming Xu, Diego Klabjan | Concept Drift and Covariate Shift Detection Ensemble with Lagged Labels | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In model serving, having one fixed model during the entire often life-long
inference process is usually detrimental to model performance, as data
distribution evolves over time, resulting in lack of reliability of the model
trained on historical data. It is important to detect changes and retrain the
model in time. The existing methods generally have three weaknesses: 1) using
only classification error rate as signal, 2) assuming ground truth labels are
immediately available after features from samples are received and 3) unable to
decide what data to use to retrain the model when change occurs. We address the
first problem by utilizing six different signals to capture a wide range of
characteristics of data, and we address the second problem by allowing lag of
labels, where labels of corresponding features are received after a lag in
time. For the third problem, our proposed method automatically decides what
data to use to retrain based on the signals. Extensive experiments on
structured and unstructured data for different type of data changes establish
that our method consistently outperforms the state-of-the-art methods by a
large margin.
| [
{
"version": "v1",
"created": "Tue, 8 Dec 2020 21:57:05 GMT"
},
{
"version": "v2",
"created": "Sat, 12 Dec 2020 20:48:31 GMT"
},
{
"version": "v3",
"created": "Tue, 15 Dec 2020 03:49:59 GMT"
}
] | 1,608,076,800,000 | [
[
"Xu",
"Yiming",
""
],
[
"Klabjan",
"Diego",
""
]
] |
2012.05123 | Sander Beckers | Sander Beckers | The Counterfactual NESS Definition of Causation | Preprint of accepted AAAI2021 paper | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In previous work with Joost Vennekens I proposed a definition of actual
causation that is based on certain plausible principles, thereby allowing the
debate on causation to shift away from its heavy focus on examples towards a
more systematic analysis. This paper contributes to that analysis in two ways.
First, I show that our definition is in fact a formalization of Wright's famous
NESS definition of causation combined with a counterfactual difference-making
condition. This means that our definition integrates two highly influential
approaches to causation that are claimed to stand in opposition to each other.
Second, I modify our definition to offer a substantial improvement: I weaken
the difference-making condition in such a way that it avoids the problematic
analysis of cases of preemption. The resulting Counterfactual NESS definition
of causation forms a natural compromise between counterfactual approaches and
the NESS approach.
| [
{
"version": "v1",
"created": "Wed, 9 Dec 2020 15:57:56 GMT"
},
{
"version": "v2",
"created": "Tue, 15 Dec 2020 21:46:12 GMT"
}
] | 1,608,163,200,000 | [
[
"Beckers",
"Sander",
""
]
] |
2012.05603 | Sander Beckers | Sander Beckers | Equivalent Causal Models | Preprint of accepted AAAI2021 paper | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The aim of this paper is to offer the first systematic exploration and
definition of equivalent causal models in the context where both models are not
made up of the same variables. The idea is that two models are equivalent when
they agree on all "essential" causal information that can be expressed using
their common variables. I do so by focussing on the two main features of causal
models, namely their structural relations and their functional relations. In
particular, I define several relations of causal ancestry and several relations
of causal sufficiency, and require that the most general of these relations are
preserved across equivalent models.
| [
{
"version": "v1",
"created": "Thu, 10 Dec 2020 11:43:35 GMT"
}
] | 1,607,644,800,000 | [
[
"Beckers",
"Sander",
""
]
] |
2012.05766 | Antonio Rago | Emanuele Albini, Piyawat Lertvittayakumjorn, Antonio Rago and
Francesca Toni | Deep Argumentative Explanations | 16 pages, 10 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the recent, widespread focus on eXplainable AI (XAI), explanations
computed by XAI methods tend to provide little insight into the functioning of
Neural Networks (NNs). We propose a novel framework for obtaining (local)
explanations from NNs while providing transparency about their inner workings,
and show how to deploy it for various neural architectures and tasks. We refer
to our novel explanations collectively as Deep Argumentative eXplanations (DAXs
in short), given that they reflect the deep structure of the underlying NNs and
that they are defined in terms of notions from computational argumentation, a
form of symbolic AI offering useful reasoning abstractions for explanation. We
evaluate DAXs empirically showing that they exhibit deep fidelity and low
computational cost. We also conduct human experiments indicating that DAXs are
comprehensible to humans and align with their judgement, while also being
competitive, in terms of user acceptance, with some existing approaches to XAI
that also have an argumentative spirit.
| [
{
"version": "v1",
"created": "Thu, 10 Dec 2020 15:55:09 GMT"
},
{
"version": "v2",
"created": "Mon, 1 Mar 2021 16:46:05 GMT"
},
{
"version": "v3",
"created": "Wed, 10 Mar 2021 17:12:30 GMT"
},
{
"version": "v4",
"created": "Mon, 14 Jun 2021 12:29:14 GMT"
}
] | 1,623,715,200,000 | [
[
"Albini",
"Emanuele",
""
],
[
"Lertvittayakumjorn",
"Piyawat",
""
],
[
"Rago",
"Antonio",
""
],
[
"Toni",
"Francesca",
""
]
] |
2012.05773 | Antonio Rago | Antonio Rago, Emanuele Albini, Pietro Baroni and Francesca Toni | Influence-Driven Explanations for Bayesian Network Classifiers | 11 pages, 2 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the most pressing issues in AI in recent years has been the need to
address the lack of explainability of many of its models. We focus on
explanations for discrete Bayesian network classifiers (BCs), targeting greater
transparency of their inner workings by including intermediate variables in
explanations, rather than just the input and output variables as is standard
practice. The proposed influence-driven explanations (IDXs) for BCs are
systematically generated using the causal relationships between variables
within the BC, called influences, which are then categorised by logical
requirements, called relation properties, according to their behaviour. These
relation properties both provide guarantees beyond heuristic explanation
methods and allow the information underpinning an explanation to be tailored to
a particular context's and user's requirements, e.g., IDXs may be dialectical
or counterfactual. We demonstrate IDXs' capability to explain various forms of
BCs, e.g., naive or multi-label, binary or categorical, and also integrate
recent approaches to explanations for BCs from the literature. We evaluate IDXs
with theoretical and empirical analyses, demonstrating their considerable
advantages when compared with existing explanation methods.
| [
{
"version": "v1",
"created": "Thu, 10 Dec 2020 16:00:51 GMT"
},
{
"version": "v2",
"created": "Mon, 1 Mar 2021 16:54:24 GMT"
},
{
"version": "v3",
"created": "Wed, 10 Mar 2021 17:04:12 GMT"
}
] | 1,615,420,800,000 | [
[
"Rago",
"Antonio",
""
],
[
"Albini",
"Emanuele",
""
],
[
"Baroni",
"Pietro",
""
],
[
"Toni",
"Francesca",
""
]
] |
2012.05860 | Daoming Zong | Daoming Zong and Shiliang Sun | GNN-XML: Graph Neural Networks for Extreme Multi-label Text
Classification | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Extreme multi-label text classification (XMTC) aims to tag a text instance
with the most relevant subset of labels from an extremely large label set. XMTC
has attracted much recent attention due to massive label sets yielded by modern
applications, such as news annotation and product recommendation. The main
challenges of XMTC are the data scalability and sparsity, thereby leading to
two issues: i) the intractability to scale to the extreme label setting, ii)
the presence of long-tailed label distribution, implying that a large fraction
of labels have few positive training instances. To overcome these problems, we
propose GNN-XML, a scalable graph neural network framework tailored for XMTC
problems. Specifically, we exploit label correlations via mining their
co-occurrence patterns and build a label graph based on the correlation matrix.
We then conduct the attributed graph clustering by performing graph convolution
with a low-pass graph filter to jointly model label dependencies and label
features, which induces semantic label clusters. We further propose a
bilateral-branch graph isomorphism network to decouple representation learning
and classifier learning for better modeling tail labels. Experimental results
on multiple benchmark datasets show that GNN-XML significantly outperforms
state-of-the-art methods while maintaining comparable prediction efficiency and
model size.
| [
{
"version": "v1",
"created": "Thu, 10 Dec 2020 18:18:34 GMT"
}
] | 1,607,644,800,000 | [
[
"Zong",
"Daoming",
""
],
[
"Sun",
"Shiliang",
""
]
] |
2012.05893 | Sharada Mohanty | Sharada Mohanty, Erik Nygren, Florian Laurent, Manuel Schneider,
Christian Scheller, Nilabha Bhattacharya, Jeremy Watson, Adrian Egli,
Christian Eichenberger, Christian Baumberger, Gereon Vienken, Irene Sturm,
Guillaume Sartoretti, Giacomo Spigler | Flatland-RL : Multi-Agent Reinforcement Learning on Trains | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Efficient automated scheduling of trains remains a major challenge for modern
railway systems. The underlying vehicle rescheduling problem (VRSP) has been a
major focus of Operations Research (OR) since decades. Traditional approaches
use complex simulators to study VRSP, where experimenting with a broad range of
novel ideas is time consuming and has a huge computational overhead. In this
paper, we introduce a two-dimensional simplified grid environment called
"Flatland" that allows for faster experimentation. Flatland does not only
reduce the complexity of the full physical simulation, but also provides an
easy-to-use interface to test novel approaches for the VRSP, such as
Reinforcement Learning (RL) and Imitation Learning (IL). In order to probe the
potential of Machine Learning (ML) research on Flatland, we (1) ran a first
series of RL and IL experiments and (2) design and executed a public Benchmark
at NeurIPS 2020 to engage a large community of researchers to work on this
problem. Our own experimental results, on the one hand, demonstrate that ML has
potential in solving the VRSP on Flatland. On the other hand, we identify key
topics that need further research. Overall, the Flatland environment has proven
to be a robust and valuable framework to investigate the VRSP for railway
networks. Our experiments provide a good starting point for further research
and for the participants of the NeurIPS 2020 Flatland Benchmark. All of these
efforts together have the potential to have a substantial impact on shaping the
mobility of the future.
| [
{
"version": "v1",
"created": "Thu, 10 Dec 2020 18:54:27 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Dec 2020 14:51:22 GMT"
}
] | 1,607,904,000,000 | [
[
"Mohanty",
"Sharada",
""
],
[
"Nygren",
"Erik",
""
],
[
"Laurent",
"Florian",
""
],
[
"Schneider",
"Manuel",
""
],
[
"Scheller",
"Christian",
""
],
[
"Bhattacharya",
"Nilabha",
""
],
[
"Watson",
"Jeremy",
""
],
[
"Egli",
"Adrian",
""
],
[
"Eichenberger",
"Christian",
""
],
[
"Baumberger",
"Christian",
""
],
[
"Vienken",
"Gereon",
""
],
[
"Sturm",
"Irene",
""
],
[
"Sartoretti",
"Guillaume",
""
],
[
"Spigler",
"Giacomo",
""
]
] |
2012.05997 | Atefeh Keshavarzi Zafarghandi | Atefeh Keshavarzi Zafarghandi, Rineke Verbrugge and Bart Verheij | Strong Admissibility for Abstract Dialectical Frameworks | 9 pages, 3 Figures, SAC '21 conference: The 36th ACM/SIGAPP Symposium
on Applied Computing | null | 10.1145/3412841.3441962 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Abstract dialectical frameworks (ADFs) have been introduced as a formalism
for modeling and evaluating argumentation allowing general logical satisfaction
conditions. Different criteria used to settle the acceptance of arguments are
called semantics. Semantics of ADFs have so far mainly been defined based on
the concept of admissibility. However, the notion of strongly admissible
semantics studied for abstract argumentation frameworks has not yet been
introduced for ADFs. In the current work we present the concept of strong
admissibility of interpretations for ADFs. Further, we show that strongly
admissible interpretations of ADFs form a lattice with the grounded
interpretation as top element.
| [
{
"version": "v1",
"created": "Thu, 10 Dec 2020 21:50:35 GMT"
}
] | 1,607,904,000,000 | [
[
"Zafarghandi",
"Atefeh Keshavarzi",
""
],
[
"Verbrugge",
"Rineke",
""
],
[
"Verheij",
"Bart",
""
]
] |
2012.06000 | Thomas P Quinn | Thomas P. Quinn, Stephan Jacobs, Manisha Senadeera, Vuong Le, Simon
Coghlan | The Three Ghosts of Medical AI: Can the Black-Box Present Deliver? | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Our title alludes to the three Christmas ghosts encountered by Ebenezer
Scrooge in \textit{A Christmas Carol}, who guide Ebenezer through the past,
present, and future of Christmas holiday events. Similarly, our article will
take readers through a journey of the past, present, and future of medical AI.
In doing so, we focus on the crux of modern machine learning: the reliance on
powerful but intrinsically opaque models. When applied to the healthcare
domain, these models fail to meet the needs for transparency that their
clinician and patient end-users require. We review the implications of this
failure, and argue that opaque models (1) lack quality assurance, (2) fail to
elicit trust, and (3) restrict physician-patient dialogue. We then discuss how
upholding transparency in all aspects of model design and model validation can
help ensure the reliability of medical AI.
| [
{
"version": "v1",
"created": "Thu, 10 Dec 2020 22:22:30 GMT"
}
] | 1,607,904,000,000 | [
[
"Quinn",
"Thomas P.",
""
],
[
"Jacobs",
"Stephan",
""
],
[
"Senadeera",
"Manisha",
""
],
[
"Le",
"Vuong",
""
],
[
"Coghlan",
"Simon",
""
]
] |
2012.06005 | Taoan Huang | Taoan Huang, Bistra Dilkina, Sven Koenig | Learning to Resolve Conflicts for Multi-Agent Path Finding with
Conflict-Based Search | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conflict-Based Search (CBS) is a state-of-the-art algorithm for multi-agent
path finding. At the high level, CBS repeatedly detects conflicts and resolves
one of them by splitting the current problem into two subproblems. Previous
work chooses the conflict to resolve by categorizing the conflict into three
classes and always picking a conflict from the highest-priority class. In this
work, we propose an oracle for conflict selection that results in smaller
search tree sizes than the one used in previous work. However, the computation
of the oracle is slow. Thus, we propose a machine-learning framework for
conflict selection that observes the decisions made by the oracle and learns a
conflict-selection strategy represented by a linear ranking function that
imitates the oracle's decisions accurately and quickly. Experiments on
benchmark maps indicate that our method significantly improves the success
rates, the search tree sizes and runtimes over the current state-of-the-art CBS
solver.
| [
{
"version": "v1",
"created": "Thu, 10 Dec 2020 22:44:35 GMT"
}
] | 1,607,904,000,000 | [
[
"Huang",
"Taoan",
""
],
[
"Dilkina",
"Bistra",
""
],
[
"Koenig",
"Sven",
""
]
] |
2012.06008 | Liang Han | Liang Han, Zhaozheng Yin, Zhurong Xia, Mingqian Tang, Rong Jin | Price Suggestion for Online Second-hand Items with Texts and Images | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents an intelligent price suggestion system for online
second-hand listings based on their uploaded images and text descriptions. The
goal of price prediction is to help sellers set effective and reasonable prices
for their second-hand items with the images and text descriptions uploaded to
the online platforms. Specifically, we design a multi-modal price suggestion
system which takes as input the extracted visual and textual features along
with some statistical item features collected from the second-hand item
shopping platform to determine whether the image and text of an uploaded
second-hand item are qualified for reasonable price suggestion with a binary
classification model, and provide price suggestions for second-hand items with
qualified images and text descriptions with a regression model. To satisfy
different demands, two different constraints are added into the joint training
of the classification model and the regression model. Moreover, a customized
loss function is designed for optimizing the regression model to provide price
suggestions for second-hand items, which can not only maximize the gain of the
sellers but also facilitate the online transaction. We also derive a set of
metrics to better evaluate the proposed price suggestion system. Extensive
experiments on a large real-world dataset demonstrate the effectiveness of the
proposed multi-modal price suggestion system.
| [
{
"version": "v1",
"created": "Thu, 10 Dec 2020 22:50:42 GMT"
}
] | 1,607,904,000,000 | [
[
"Han",
"Liang",
""
],
[
"Yin",
"Zhaozheng",
""
],
[
"Xia",
"Zhurong",
""
],
[
"Tang",
"Mingqian",
""
],
[
"Jin",
"Rong",
""
]
] |
2012.06157 | Rupam Acharyya | Ankani Chattoraj, Rupam Acharyya, Shouman Das, Md. Iftekhar Tanveer,
Ehsan Hoque | Fairness in Rating Prediction by Awareness of Verbal and Gesture Quality
of Public Speeches | null | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | The role of verbal and non-verbal cues towards great public speaking has been
a topic of exploration for many decades. We identify a commonality across
present theories, the element of "variety or heterogeneity" in channels or
modes of communication (e.g. resorting to stories, scientific facts, emotional
connections, facial expressions etc.) which is essential for effectively
communicating information. We use this observation to formalize a novel
HEterogeneity Metric, HEM, that quantifies the quality of a talk both in the
verbal and non-verbal domain (transcript and facial gestures). We use TED talks
as an input repository of public speeches because it consists of speakers from
a diverse community besides having a wide outreach. We show that there is an
interesting relationship between HEM and the ratings of TED talks given to
speakers by viewers. It emphasizes that HEM inherently and successfully
represents the quality of a talk based on "variety or heterogeneity". Further,
we also discover that HEM successfully captures the prevalent bias in ratings
with respect to race and gender, that we call sensitive attributes (because
prediction based on these might result in unfair outcome). We incorporate the
HEM metric into the loss function of a neural network with the goal to reduce
unfairness in rating predictions with respect to race and gender. Our results
show that the modified loss function improves fairness in prediction without
considerably affecting prediction accuracy of the neural network. Our work ties
together a novel metric for public speeches in both verbal and non-verbal
domain with the computational power of a neural network to design a fair
prediction system for speakers.
| [
{
"version": "v1",
"created": "Fri, 11 Dec 2020 06:36:55 GMT"
},
{
"version": "v2",
"created": "Wed, 16 Dec 2020 20:48:35 GMT"
},
{
"version": "v3",
"created": "Tue, 16 Nov 2021 04:59:04 GMT"
}
] | 1,637,107,200,000 | [
[
"Chattoraj",
"Ankani",
""
],
[
"Acharyya",
"Rupam",
""
],
[
"Das",
"Shouman",
""
],
[
"Tanveer",
"Md. Iftekhar",
""
],
[
"Hoque",
"Ehsan",
""
]
] |
2012.06306 | Simon Gottschalk | Simon Gottschalk and Elena Demidova | EventKG+BT: Generation of Interactive Biography Timelines from a
Knowledge Graph | ESWC 2020 Satellite Events pp 91-97 | null | 10.1007/978-3-030-62327-2_16 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Research on notable accomplishments and important events in the life of
people of public interest usually requires close reading of long encyclopedic
or biographical sources, which is a tedious and time-consuming task. Whereas
semantic reference sources, such as the EventKG knowledge graph, provide
structured representations of relevant facts, they often include hundreds of
events and temporal relations for particular entities. In this paper, we
present EventKG+BT - a timeline generation system that creates concise and
interactive spatio-temporal representations of biographies from a knowledge
graph using distant supervision.
| [
{
"version": "v1",
"created": "Fri, 4 Dec 2020 13:06:27 GMT"
}
] | 1,607,904,000,000 | [
[
"Gottschalk",
"Simon",
""
],
[
"Demidova",
"Elena",
""
]
] |
2012.06344 | Raffaele Marino | Raffaele Marino | Learning from Survey Propagation: a Neural Network for MAX-E-$3$-SAT | null | Mach. Learn.: Sci. Technol. 2 (2021) 035032 | 10.1088/2632-2153/ac0496 | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Many natural optimization problems are NP-hard, which implies that they are
probably hard to solve exactly in the worst-case. However, it suffices to get
reasonably good solutions for all (or even most) instances in practice. This
paper presents a new algorithm for computing approximate solutions in
${\Theta(N})$ for the Maximum Exact 3-Satisfiability (MAX-E-$3$-SAT) problem by
using deep learning methodology. This methodology allows us to create a
learning algorithm able to fix Boolean variables by using local information
obtained by the Survey Propagation algorithm. By performing an accurate
analysis, on random CNF instances of the MAX-E-$3$-SAT with several Boolean
variables, we show that this new algorithm, avoiding any decimation strategy,
can build assignments better than a random one, even if the convergence of the
messages is not found. Although this algorithm is not competitive with
state-of-the-art Maximum Satisfiability (MAX-SAT) solvers, it can solve
substantially larger and more complicated problems than it ever saw during
training.
| [
{
"version": "v1",
"created": "Thu, 10 Dec 2020 07:59:54 GMT"
},
{
"version": "v2",
"created": "Sun, 14 Feb 2021 09:22:57 GMT"
}
] | 1,663,027,200,000 | [
[
"Marino",
"Raffaele",
""
]
] |
2012.06474 | Alessandro Zonta | A. Zonta, S.K. Smit and A.E. Eiben | Generating Human-Like Movement: A Comparison Between Two Approaches
Based on Environmental Features | 31 pages, 16 figures, submitted to Expert Systems with Applications | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Modelling realistic human behaviours in simulation is an ongoing challenge
that resides between several fields like social sciences, philosophy, and
artificial intelligence. Human movement is a special type of behaviour driven
by intent (e.g. to get groceries) and the surrounding environment (e.g.
curiosity to see new interesting places). Services available online and offline
do not normally consider the environment when planning a path, which is
decisive especially on a leisure trip. Two novel algorithms have been presented
to generate human-like trajectories based on environmental features. The
Attraction-Based A* algorithm includes in its computation information from the
environmental features meanwhile, the Feature-Based A* algorithm also injects
information from the real trajectories in its computation. The human-likeness
aspect has been tested by a human expert judging the final generated
trajectories as realistic. This paper presents a comparison between the two
approaches in some key metrics like efficiency, efficacy, and hyper-parameters
sensitivity. We show how, despite generating trajectories that are closer to
the real one according to our predefined metrics, the Feature-Based A*
algorithm fall short in time efficiency compared to the Attraction-Based A*
algorithm, hindering the usability of the model in the real world.
| [
{
"version": "v1",
"created": "Fri, 11 Dec 2020 16:45:32 GMT"
}
] | 1,607,904,000,000 | [
[
"Zonta",
"A.",
""
],
[
"Smit",
"S. K.",
""
],
[
"Eiben",
"A. E.",
""
]
] |
2012.06686 | Raymond Anneborg | Raymond Anneborg | Computing Machinery and Knowledge | 7 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The purpose of this paper is to discuss the possibilities for computing
machinery, or AI agents, to know and to possess knowledge. This is done mainly
from a virtue epistemology perspective and definition of knowledge. However,
this inquiry also shed light on the human condition, what it means for a human
to know, and to possess knowledge. The paper argues that it is possible for an
AI agent to know and examines this from both current state-of-the-art in
artificial intelligence as well as from the perspective of what the future AI
development might bring in terms of superintelligent AI agents.
| [
{
"version": "v1",
"created": "Sat, 31 Oct 2020 09:27:53 GMT"
}
] | 1,607,990,400,000 | [
[
"Anneborg",
"Raymond",
""
]
] |
2012.07195 | Qi Zhang | Qi Zhang, Edmund H. Durfee, Satinder Singh | Efficient Querying for Cooperative Probabilistic Commitments | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Multiagent systems can use commitments as the core of a general coordination
infrastructure, supporting both cooperative and non-cooperative interactions.
Agents whose objectives are aligned, and where one agent can help another
achieve greater reward by sacrificing some of its own reward, should choose a
cooperative commitment to maximize their joint reward. We present a solution to
the problem of how cooperative agents can efficiently find an (approximately)
optimal commitment by querying about carefully-selected commitment choices. We
prove structural properties of the agents' values as functions of the
parameters of the commitment specification, and develop a greedy method for
composing a query with provable approximation bounds, which we empirically show
can find nearly optimal commitments in a fraction of the time methods that lack
our insights require.
| [
{
"version": "v1",
"created": "Mon, 14 Dec 2020 00:47:09 GMT"
}
] | 1,607,990,400,000 | [
[
"Zhang",
"Qi",
""
],
[
"Durfee",
"Edmund H.",
""
],
[
"Singh",
"Satinder",
""
]
] |
2012.07228 | Lei Li | Lei Li, Minghe Xue, Huanhuan Chen, Xindong Wu | Trustworthy Preference Completion in Social Choice | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | As from time to time it is impractical to ask agents to provide linear orders
over all alternatives, for these partial rankings it is necessary to conduct
preference completion. Specifically, the personalized preference of each agent
over all the alternatives can be estimated with partial rankings from
neighboring agents over subsets of alternatives. However, since the agents'
rankings are nondeterministic, where they may provide rankings with noise, it
is necessary and important to conduct the trustworthy preference completion.
Hence, in this paper firstly, a trust-based anchor-kNN algorithm is proposed to
find $k$-nearest trustworthy neighbors of the agent with trust-oriented
Kendall-Tau distances, which will handle the cases when an agent exhibits
irrational behaviors or provides only noisy rankings. Then, for alternative
pairs, a bijection can be built from the ranking space to the preference space,
and its certainty and conflict can be evaluated based on a well-built
statistical measurement Probability-Certainty Density Function. Therefore, a
certain common voting rule for the first $k$ trustworthy neighboring agents
based on certainty and conflict can be taken to conduct the trustworthy
preference completion. The properties of the proposed certainty and conflict
have been studied empirically, and the proposed approach has been
experimentally validated compared to state-of-arts approaches with several data
sets.
| [
{
"version": "v1",
"created": "Mon, 14 Dec 2020 03:03:13 GMT"
}
] | 1,607,990,400,000 | [
[
"Li",
"Lei",
""
],
[
"Xue",
"Minghe",
""
],
[
"Chen",
"Huanhuan",
""
],
[
"Wu",
"Xindong",
""
]
] |
2012.07464 | Alejandro Su\'arez Hern\'andez | Alejandro Su\'arez-Hern\'andez and Javier Segovia-Aguas and Carme
Torras and Guillem Aleny\`a | Online Action Recognition | Accepted version in AAAI 21:
https://ojs.aaai.org/index.php/AAAI/article/view/17423 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recognition in planning seeks to find agent intentions, goals or activities
given a set of observations and a knowledge library (e.g. goal states, plans or
domain theories). In this work we introduce the problem of Online Action
Recognition. It consists in recognizing, in an open world, the planning action
that best explains a partially observable state transition from a knowledge
library of first-order STRIPS actions, which is initially empty. We frame this
as an optimization problem, and propose two algorithms to address it: Action
Unification (AU) and Online Action Recognition through Unification (OARU). The
former builds on logic unification and generalizes two input actions using
weighted partial MaxSAT. The latter looks for an action within the library that
explains an observed transition. If there is such action, it generalizes it
making use of AU, building in this way an AU hierarchy. Otherwise, OARU inserts
a Trivial Grounded Action (TGA) in the library that explains just that
transition. We report results on benchmarks from the International Planning
Competition and PDDLGym, where OARU recognizes actions accurately with respect
to expert knowledge, and shows real-time performance.
| [
{
"version": "v1",
"created": "Mon, 14 Dec 2020 12:37:20 GMT"
},
{
"version": "v2",
"created": "Tue, 3 Aug 2021 14:38:17 GMT"
}
] | 1,628,035,200,000 | [
[
"Suárez-Hernández",
"Alejandro",
""
],
[
"Segovia-Aguas",
"Javier",
""
],
[
"Torras",
"Carme",
""
],
[
"Alenyà",
"Guillem",
""
]
] |
2012.08033 | Blai Bonet | Blai Bonet and Hector Geffner | General Policies, Serializations, and Planning Width | Longer version of AAAI-2021 paper that includes proofs and more
explanations | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | It has been observed that in many of the benchmark planning domains, atomic
goals can be reached with a simple polynomial exploration procedure, called IW,
that runs in time exponential in the problem width. Such problems have indeed a
bounded width: a width that does not grow with the number of problem variables
and is often no greater than two. Yet, while the notion of width has become
part of the state-of-the-art planning algorithms like BFWS, there is still no
good explanation for why so many benchmark domains have bounded width. In this
work, we address this question by relating bounded width and serialized width
to ideas of generalized planning, where general policies aim to solve multiple
instances of a planning problem all at once. We show that bounded width is a
property of planning domains that admit optimal general policies in terms of
features that are explicitly or implicitly represented in the domain encoding.
The results are extended to much larger class of domains with bounded
serialized width where the general policies do not have to be optimal. The
study leads also to a new simple, meaningful, and expressive language for
specifying domain serializations in the form of policy sketches which can be
used for encoding domain control knowledge by hand or for learning it from
traces. The use of sketches and the meaning of the theoretical results are all
illustrated through a number of examples.
| [
{
"version": "v1",
"created": "Tue, 15 Dec 2020 01:33:59 GMT"
},
{
"version": "v2",
"created": "Wed, 23 Dec 2020 16:14:01 GMT"
}
] | 1,608,768,000,000 | [
[
"Bonet",
"Blai",
""
],
[
"Geffner",
"Hector",
""
]
] |
2012.08479 | Hiroyuki Kido | Hiroyuki Kido, Keishi Okamoto | Bayes Meets Entailment and Prediction: Commonsense Reasoning with
Non-monotonicity, Paraconsistency and Predictive Accuracy | This paper was submitted to AAAI 2021 and rejected | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The recent success of Bayesian methods in neuroscience and artificial
intelligence gives rise to the hypothesis that the brain is a Bayesian machine.
Since logic and learning are both practices of the human brain, it leads to
another hypothesis that there is a Bayesian interpretation underlying both
logical reasoning and machine learning. In this paper, we introduce a
generative model of logical consequence relations. It formalises the process of
how the truth value of a sentence is probabilistically generated from the
probability distribution over states of the world. We show that the generative
model characterises a classical consequence relation, paraconsistent
consequence relation and nonmonotonic consequence relation. In particular, the
generative model gives a new consequence relation that outperforms them in
reasoning with inconsistent knowledge. We also show that the generative model
gives a new classification algorithm that outperforms several representative
algorithms in predictive accuracy and complexity on the Kaggle Titanic dataset.
| [
{
"version": "v1",
"created": "Tue, 15 Dec 2020 18:22:27 GMT"
},
{
"version": "v2",
"created": "Wed, 16 Dec 2020 02:18:21 GMT"
},
{
"version": "v3",
"created": "Wed, 27 Jan 2021 18:13:00 GMT"
}
] | 1,611,792,000,000 | [
[
"Kido",
"Hiroyuki",
""
],
[
"Okamoto",
"Keishi",
""
]
] |
2012.08564 | Eleni Nisioti | Eleni Nisioti and Cl\'ement Moulin-Frier | Grounding Artificial Intelligence in the Origins of Human Behavior | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advances in Artificial Intelligence (AI) have revived the quest for
agents able to acquire an open-ended repertoire of skills. However, although
this ability is fundamentally related to the characteristics of human
intelligence, research in this field rarely considers the processes that may
have guided the emergence of complex cognitive capacities during the evolution
of the species.
Research in Human Behavioral Ecology (HBE) seeks to understand how the
behaviors characterizing human nature can be conceived as adaptive responses to
major changes in the structure of our ecological niche. In this paper, we
propose a framework highlighting the role of environmental complexity in
open-ended skill acquisition, grounded in major hypotheses from HBE and recent
contributions in Reinforcement learning (RL). We use this framework to
highlight fundamental links between the two disciplines, as well as to identify
feedback loops that bootstrap ecological complexity and create promising
research directions for AI researchers.
| [
{
"version": "v1",
"created": "Tue, 15 Dec 2020 19:28:45 GMT"
},
{
"version": "v2",
"created": "Thu, 17 Dec 2020 14:07:50 GMT"
}
] | 1,608,249,600,000 | [
[
"Nisioti",
"Eleni",
""
],
[
"Moulin-Frier",
"Clément",
""
]
] |
2012.08622 | Bilal Farooq | Ali Yazdizadeh and Bilal Farooq | Smart Mobility Ontology: Current Trends and Future Directions | Published as a book chapter in: Handbook of Smart Cities, Springer,
2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ontology is the explicit and formal representation of the concepts in a
domain and relations among them. Transportation science is a wide domain
dealing with mobility over various complex and interconnected transportation
systems, such as land, aviation, and maritime transport, and can take
considerable advantage from ontology development. While several studies can be
found in the recent literature, there exists a large potential to improve and
develop a comprehensive smart mobility ontology. The current chapter aims to
present different aspects of ontology development in general, such as ontology
development methods, languages, tools, and software. Subsequently, it presents
the currently available mobility-related ontologies developed across different
domains, such as transportation, smart cities, goods mobility, sensors. Current
gaps in the available ontologies are identified, and future directions
regarding ontology development are proposed that can incorporate the
forthcoming autonomous and connected vehicles, mobility as a service (MaaS),
and other disruptive transportation technologies and services.
| [
{
"version": "v1",
"created": "Tue, 15 Dec 2020 21:28:43 GMT"
}
] | 1,608,163,200,000 | [
[
"Yazdizadeh",
"Ali",
""
],
[
"Farooq",
"Bilal",
""
]
] |
2012.08888 | Peipei Kang | Lei Yang, Zitong Zhang, Xiaotian Jia, Peipei Kang, Wensheng Zhang,
Dongya Wang | Solving the Travelling Thief Problem based on Item Selection Weight and
Reverse Order Allocation | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The Travelling Thief Problem (TTP) is a challenging combinatorial
optimization problem that attracts many scholars. The TTP interconnects two
well-known NP-hard problems: the Travelling Salesman Problem (TSP) and the 0-1
Knapsack Problem (KP). Increasingly algorithms have been proposed for solving
this novel problem that combines two interdependent sub-problems. In this
paper, TTP is investigated theoretically and empirically. An algorithm based on
the score value calculated by our proposed formulation in picking items and
sorting items in the reverse order in the light of the scoring value is
proposed to solve the problem. Different approaches for solving the TTP are
compared and analyzed; the experimental investigations suggest that our
proposed approach is very efficient in meeting or beating current
state-of-the-art heuristic solutions on a comprehensive set of benchmark TTP
instances.
| [
{
"version": "v1",
"created": "Wed, 16 Dec 2020 12:06:05 GMT"
}
] | 1,608,163,200,000 | [
[
"Yang",
"Lei",
""
],
[
"Zhang",
"Zitong",
""
],
[
"Jia",
"Xiaotian",
""
],
[
"Kang",
"Peipei",
""
],
[
"Zhang",
"Wensheng",
""
],
[
"Wang",
"Dongya",
""
]
] |
2012.08911 | Sijie Mai | Sijie Mai, Shuangjia Zheng, Yuedong Yang, Haifeng Hu | Communicative Message Passing for Inductive Relation Reasoning | Accepted by AAAI-2021 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Relation prediction for knowledge graphs aims at predicting missing
relationships between entities. Despite the importance of inductive relation
prediction, most previous works are limited to a transductive setting and
cannot process previously unseen entities. The recent proposed subgraph-based
relation reasoning models provided alternatives to predict links from the
subgraph structure surrounding a candidate triplet inductively. However, we
observe that these methods often neglect the directed nature of the extracted
subgraph and weaken the role of relation information in the subgraph modeling.
As a result, they fail to effectively handle the asymmetric/anti-symmetric
triplets and produce insufficient embeddings for the target triplets. To this
end, we introduce a \textbf{C}\textbf{o}mmunicative \textbf{M}essage
\textbf{P}assing neural network for \textbf{I}nductive re\textbf{L}ation
r\textbf{E}asoning, \textbf{CoMPILE}, that reasons over local directed subgraph
structures and has a vigorous inductive bias to process entity-independent
semantic relations. In contrast to existing models, CoMPILE strengthens the
message interactions between edges and entitles through a communicative kernel
and enables a sufficient flow of relation information. Moreover, we demonstrate
that CoMPILE can naturally handle asymmetric/anti-symmetric relations without
the need for explosively increasing the number of model parameters by
extracting the directed enclosing subgraphs. Extensive experiments show
substantial performance gains in comparison to state-of-the-art methods on
commonly used benchmark datasets with variant inductive settings.
| [
{
"version": "v1",
"created": "Wed, 16 Dec 2020 12:42:06 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Jul 2021 12:18:21 GMT"
}
] | 1,627,344,000,000 | [
[
"Mai",
"Sijie",
""
],
[
"Zheng",
"Shuangjia",
""
],
[
"Yang",
"Yuedong",
""
],
[
"Hu",
"Haifeng",
""
]
] |
2012.09049 | Jorge Martinez Gil Ph.D. | Georg Buchgeher, David Gabauer, Jorge Martinez-Gil, Lisa Ehrlinger | Knowledge Graphs in Manufacturing and Production: A Systematic
Literature Review | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge graphs in manufacturing and production aim to make production lines
more efficient and flexible with higher quality output. This makes knowledge
graphs attractive for companies to reach Industry 4.0 goals. However, existing
research in the field is quite preliminary, and more research effort on
analyzing how knowledge graphs can be applied in the field of manufacturing and
production is needed. Therefore, we have conducted a systematic literature
review as an attempt to characterize the state-of-the-art in this field, i.e.,
by identifying exiting research and by identifying gaps and opportunities for
further research. To do that, we have focused on finding the primary studies in
the existing literature, which were classified and analyzed according to four
criteria: bibliometric key facts, research type facets, knowledge graph
characteristics, and application scenarios. Besides, an evaluation of the
primary studies has also been carried out to gain deeper insights in terms of
methodology, empirical evidence, and relevance. As a result, we can offer a
complete picture of the domain, which includes such interesting aspects as the
fact that knowledge fusion is currently the main use case for knowledge graphs,
that empirical research and industrial application are still missing to a large
extent, that graph embeddings are not fully exploited, and that technical
literature is fast-growing but seems to be still far from its peak.
| [
{
"version": "v1",
"created": "Wed, 16 Dec 2020 16:15:28 GMT"
}
] | 1,608,163,200,000 | [
[
"Buchgeher",
"Georg",
""
],
[
"Gabauer",
"David",
""
],
[
"Martinez-Gil",
"Jorge",
""
],
[
"Ehrlinger",
"Lisa",
""
]
] |
2012.09424 | Zelong Yang | Zelong Yang, Yan Wang, Piji Li, Shaobin Lin, Shuming Shi, Shao-Lun
Huang, Wei Bi | Predicting Events in MOBA Games: Prediction, Attribution, and Evaluation | null | null | 10.1109/TG.2022.3159704 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The multiplayer online battle arena (MOBA) games have become increasingly
popular in recent years. Consequently, many efforts have been devoted to
providing pre-game or in-game predictions for them. However, these works are
limited in the following two aspects: 1) the lack of sufficient in-game
features; 2) the absence of interpretability in the prediction results. These
two limitations greatly restrict the practical performance and industrial
application of the current works. In this work, we collect and release a
large-scale dataset containing rich in-game features for the popular MOBA game
Honor of Kings. We then propose to predict four types of important events in an
interpretable way by attributing the predictions to the input features using
two gradient-based attribution methods: Integrated Gradients and SmoothGrad. To
evaluate the explanatory power of different models and attribution methods, a
fidelity-based evaluation metric is further proposed. Finally, we evaluate the
accuracy and Fidelity of several competitive methods on the collected dataset
to assess how well machines predict events in MOBA games.
| [
{
"version": "v1",
"created": "Thu, 17 Dec 2020 07:28:35 GMT"
},
{
"version": "v2",
"created": "Wed, 23 Dec 2020 07:42:51 GMT"
},
{
"version": "v3",
"created": "Thu, 24 Dec 2020 07:47:19 GMT"
},
{
"version": "v4",
"created": "Tue, 22 Mar 2022 06:54:14 GMT"
},
{
"version": "v5",
"created": "Mon, 28 Mar 2022 14:12:55 GMT"
}
] | 1,648,512,000,000 | [
[
"Yang",
"Zelong",
""
],
[
"Wang",
"Yan",
""
],
[
"Li",
"Piji",
""
],
[
"Lin",
"Shaobin",
""
],
[
"Shi",
"Shuming",
""
],
[
"Huang",
"Shao-Lun",
""
],
[
"Bi",
"Wei",
""
]
] |
2012.10147 | Manfred Eppe | Manfred Eppe, Christian Gumbsch, Matthias Kerzel, Phuong D.H. Nguyen,
Martin V. Butz and Stefan Wermter | Hierarchical principles of embodied reinforcement learning: A review | null | Nature Machine Intelligence, 4(1) (2022) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cognitive Psychology and related disciplines have identified several critical
mechanisms that enable intelligent biological agents to learn to solve complex
problems. There exists pressing evidence that the cognitive mechanisms that
enable problem-solving skills in these species build on hierarchical mental
representations. Among the most promising computational approaches to provide
comparable learning-based problem-solving abilities for artificial agents and
robots is hierarchical reinforcement learning. However, so far the existing
computational approaches have not been able to equip artificial agents with
problem-solving abilities that are comparable to intelligent animals, including
human and non-human primates, crows, or octopuses. Here, we first survey the
literature in Cognitive Psychology, and related disciplines, and find that many
important mental mechanisms involve compositional abstraction, curiosity, and
forward models. We then relate these insights with contemporary hierarchical
reinforcement learning methods, and identify the key machine intelligence
approaches that realise these mechanisms. As our main result, we show that all
important cognitive mechanisms have been implemented independently in isolated
computational architectures, and there is simply a lack of approaches that
integrate them appropriately. We expect our results to guide the development of
more sophisticated cognitively inspired hierarchical methods, so that future
artificial agents achieve a problem-solving performance on the level of
intelligent animals.
| [
{
"version": "v1",
"created": "Fri, 18 Dec 2020 10:19:38 GMT"
},
{
"version": "v2",
"created": "Thu, 18 Aug 2022 09:45:25 GMT"
}
] | 1,660,867,200,000 | [
[
"Eppe",
"Manfred",
""
],
[
"Gumbsch",
"Christian",
""
],
[
"Kerzel",
"Matthias",
""
],
[
"Nguyen",
"Phuong D. H.",
""
],
[
"Butz",
"Martin V.",
""
],
[
"Wermter",
"Stefan",
""
]
] |
2012.10171 | Menghui Zhu | Sheng Chen, Menghui Zhu, Deheng Ye, Weinan Zhang, Qiang Fu, Wei Yang | Which Heroes to Pick? Learning to Draft in MOBA Games with Neural
Networks and Tree Search | IEEE Transactions on Games | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hero drafting is essential in MOBA game playing as it builds the team of each
side and directly affects the match outcome. State-of-the-art drafting methods
fail to consider: 1) drafting efficiency when the hero pool is expanded; 2) the
multi-round nature of a MOBA 5v5 match series, i.e., two teams play best-of-N
and the same hero is only allowed to be drafted once throughout the series. In
this paper, we formulate the drafting process as a multi-round combinatorial
game and propose a novel drafting algorithm based on neural networks and
Monte-Carlo tree search, named JueWuDraft. Specifically, we design a long-term
value estimation mechanism to handle the best-of-N drafting case. Taking Honor
of Kings, one of the most popular MOBA games at present, as a running case, we
demonstrate the practicality and effectiveness of JueWuDraft when compared to
state-of-the-art drafting methods.
| [
{
"version": "v1",
"created": "Fri, 18 Dec 2020 11:19:00 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Jul 2021 03:34:40 GMT"
},
{
"version": "v3",
"created": "Fri, 2 Jul 2021 03:48:24 GMT"
},
{
"version": "v4",
"created": "Thu, 5 Aug 2021 09:01:42 GMT"
}
] | 1,628,208,000,000 | [
[
"Chen",
"Sheng",
""
],
[
"Zhu",
"Menghui",
""
],
[
"Ye",
"Deheng",
""
],
[
"Zhang",
"Weinan",
""
],
[
"Fu",
"Qiang",
""
],
[
"Yang",
"Wei",
""
]
] |
2012.10232 | Bata Vasic Dr | Iva Vasic, Bata Vasic, and Zorica Nikolic | Artificial Intelligence ordered 3D vertex importance | 8 pages, 4 figures | FBIM Transactions, Vol. 8 No. 2, pp. 193-201, 2020 | 10.12709/fbim.08.08.02.21 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Ranking vertices of multidimensional networks is crucial in many areas of
research, including selecting and determining the importance of decisions. Some
decisions are significantly more important than others, and their weight
categorization is also imortant. This paper defines a completely new method for
determining the weight decisions using artificial intelligence for importance
ranking of three-dimensional network vertices, improving the existing Ordered
Statistics Vertex Extraction and Tracking Algorithm (OSVETA) based on
modulation of quantized indices (QIM) and error correction codes. The technique
we propose in this paper offers significant improvements the efficiency of
determination the importance of network vertices in relation to statistical
OSVETA criteria, replacing heuristic methods with methods of precise prediction
of modern neural networks. The new artificial intelligence technique enables a
significantly better definition of the 3D meshes and a better assessment of
their topological features. The new method contributions result in a greater
precision in defining stable vertices, significantly reducing the probability
of deleting mesh vertices.
| [
{
"version": "v1",
"created": "Thu, 17 Dec 2020 06:54:59 GMT"
}
] | 1,608,508,800,000 | [
[
"Vasic",
"Iva",
""
],
[
"Vasic",
"Bata",
""
],
[
"Nikolic",
"Zorica",
""
]
] |
2012.10473 | Tim Ritmeester | Tim Ritmeester and Hildegard Meyer-Ortmanns | State Estimation of Power Flows for Smart Grids via Belief Propagation | 15 pages, 16 figures | Phys. Rev. E 102, 012311 (2020) | 10.1103/PhysRevE.102.012311 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Belief propagation is an algorithm that is known from statistical physics and
computer science. It provides an efficient way of calculating marginals that
involve large sums of products which are efficiently rearranged into nested
products of sums to approximate the marginals. It allows a reliable estimation
of the state and its variance of power grids that is needed for the control and
forecast of power grid management. At prototypical examples of IEEE-grids we
show that belief propagation not only scales linearly with the grid size for
the state estimation itself, but also facilitates and accelerates the retrieval
of missing data and allows an optimized positioning of measurement units. Based
on belief propagation, we give a criterion for how to assess whether other
algorithms, using only local information, are adequate for state estimation for
a given grid. We also demonstrate how belief propagation can be utilized for
coarse-graining power grids towards representations that reduce the
computational effort when the coarse-grained version is integrated into a
larger grid. It provides a criterion for partitioning power grids into areas in
order to minimize the error of flow estimates between different areas.
| [
{
"version": "v1",
"created": "Fri, 18 Dec 2020 19:22:03 GMT"
}
] | 1,608,595,200,000 | [
[
"Ritmeester",
"Tim",
""
],
[
"Meyer-Ortmanns",
"Hildegard",
""
]
] |
2012.10489 | Joyjit Chatterjee | Joyjit Chatterjee, Nina Dethlefs | XAI4Wind: A Multimodal Knowledge Graph Database for Explainable Decision
Support in Operations & Maintenance of Wind Turbines | Updated version of knowledge graph resource paper - updates include
additions to the Appendix on more properties in the knowledge graph,
corrected typos/grammatical errors etc | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Condition-based monitoring (CBM) has been widely utilised in the wind
industry for monitoring operational inconsistencies and failures in turbines,
with techniques ranging from signal processing and vibration analysis to
artificial intelligence (AI) models using Supervisory Control & Acquisition
(SCADA) data. However, existing studies do not present a concrete basis to
facilitate explainable decision support in operations and maintenance (O&M),
particularly for automated decision support through recommendation of
appropriate maintenance action reports corresponding to failures predicted by
CBM techniques. Knowledge graph databases (KGs) model a collection of
domain-specific information and have played an intrinsic role for real-world
decision support in domains such as healthcare and finance, but have seen very
limited attention in the wind industry. We propose XAI4Wind, a multimodal
knowledge graph for explainable decision support in real-world operational
turbines and demonstrate through experiments several use-cases of the proposed
KG towards O&M planning through interactive query and reasoning and providing
novel insights using graph data science algorithms. The proposed KG combines
multimodal knowledge like SCADA parameters and alarms with natural language
maintenance actions, images etc. By integrating our KG with an Explainable AI
model for anomaly prediction, we show that it can provide effective
human-intelligible O&M strategies for predicted operational inconsistencies in
various turbine sub-components. This can help instil better trust and
confidence in conventionally black-box AI models. We make our KG publicly
available and envisage that it can serve as the building ground for providing
autonomous decision support in the wind industry.
| [
{
"version": "v1",
"created": "Fri, 18 Dec 2020 19:54:19 GMT"
},
{
"version": "v2",
"created": "Wed, 24 Feb 2021 04:38:47 GMT"
}
] | 1,614,211,200,000 | [
[
"Chatterjee",
"Joyjit",
""
],
[
"Dethlefs",
"Nina",
""
]
] |
2012.10592 | Lixing Tan | Lixing Tan, Zhaohui Zhu, Jinjin Zhang | More on extension-based semantics of argumentation | 86 pages, 10 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | After a few decades of development, computational argumentation has become
one of the active realms in AI. This paper considers extension-based concrete
and abstract semantics of argumentation. For concrete ones, based on Grossi and
Modgil's recent work, this paper considers some issues on graded
extension-based semantics of abstract argumentation framework (AAF, for short).
First, an alternative fundamental lemma is given, which generalizes the
corresponding result due to Grossi and Modgil by relaxing the constraint on
parameters. This lemma provides a new sufficient condition for preserving
conflict-freeness and brings a Galois adjunction between admissible sets and
complete extensions, which is of vital importance in constructing some special
extensions in terms of iterations of the defense function. Applying such a
lemma, some flaws in Grossi and Modgil's work are corrected, and the structural
property and universal definability of various extension-based semantics are
given. Second, an operator so-called reduced meet modulo an ultrafilter is
presented, which is a simple but powerful tool in exploring infinite AAFs. The
neutrality function and the defense function, which play central roles in
Dung's abstract argumentation theory, are shown to be distributive over reduced
meets modulo any ultrafilter. A variety of fundamental semantics of AAFs,
including conflict-free, admissible, complete and stable semantics, etc, are
shown to be closed under this operator. Based on this fact, a number of
applications of such operators are considered. In particular, we provide a
simple and uniform method to prove the universal definability of a family of
range related semantics. Since all graded concrete semantics considered in this
paper are generalizations of corresponding non-graded ones, all results about
them obtained in this paper also hold in the traditional situation.
| [
{
"version": "v1",
"created": "Sat, 19 Dec 2020 04:32:19 GMT"
},
{
"version": "v2",
"created": "Sun, 27 Dec 2020 01:41:18 GMT"
},
{
"version": "v3",
"created": "Thu, 20 May 2021 04:58:41 GMT"
}
] | 1,621,555,200,000 | [
[
"Tan",
"Lixing",
""
],
[
"Zhu",
"Zhaohui",
""
],
[
"Zhang",
"Jinjin",
""
]
] |
2012.10700 | Quentin Cohen-Solal | Quentin Cohen-Solal and Tristan Cazenave | Minimax Strikes Back | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep Reinforcement Learning (DRL) reaches a superhuman level of play in many
complete information games. The state of the art search algorithm used in
combination with DRL is Monte Carlo Tree Search (MCTS). We take another
approach to DRL using a Minimax algorithm instead of MCTS and learning only the
evaluation of states, not the policy. We show that for multiple games it is
competitive with the state of the art DRL for the learning performances and for
the confrontations.
| [
{
"version": "v1",
"created": "Sat, 19 Dec 2020 14:42:41 GMT"
}
] | 1,608,595,200,000 | [
[
"Cohen-Solal",
"Quentin",
""
],
[
"Cazenave",
"Tristan",
""
]
] |
2012.10928 | Milad Moradi | Milad Moradi, Matthias Samwald | Explaining Black-box Models for Biomedical Text Classification | null | null | 10.1109/JBHI.2021.3056748 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a novel method named Biomedical Confident Itemsets
Explanation (BioCIE), aiming at post-hoc explanation of black-box machine
learning models for biomedical text classification. Using sources of domain
knowledge and a confident itemset mining method, BioCIE discretizes the
decision space of a black-box into smaller subspaces and extracts semantic
relationships between the input text and class labels in different subspaces.
Confident itemsets discover how biomedical concepts are related to class labels
in the black-box's decision space. BioCIE uses the itemsets to approximate the
black-box's behavior for individual predictions. Optimizing fidelity,
interpretability, and coverage measures, BioCIE produces class-wise
explanations that represent decision boundaries of the black-box. Results of
evaluations on various biomedical text classification tasks and black-box
models demonstrated that BioCIE can outperform perturbation-based and decision
set methods in terms of producing concise, accurate, and interpretable
explanations. BioCIE improved the fidelity of instance-wise and class-wise
explanations by 11.6% and 7.5%, respectively. It also improved the
interpretability of explanations by 8%. BioCIE can be effectively used to
explain how a black-box biomedical text classification model semantically
relates input texts to class labels. The source code and supplementary material
are available at https://github.com/mmoradi-iut/BioCIE.
| [
{
"version": "v1",
"created": "Sun, 20 Dec 2020 13:58:52 GMT"
}
] | 1,612,742,400,000 | [
[
"Moradi",
"Milad",
""
],
[
"Samwald",
"Matthias",
""
]
] |
2012.11078 | Patrick Rodler | Patrick Rodler | DynamicHS: Streamlining Reiter's Hitting-Set Tree for Sequential
Diagnosis | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Given a system that does not work as expected, Sequential Diagnosis (SD) aims
at suggesting a series of system measurements to isolate the true explanation
for the system's misbehavior from a potentially exponential set of possible
explanations. To reason about the best next measurement, SD methods usually
require a sample of possible fault explanations at each step of the iterative
diagnostic process. The computation of this sample can be accomplished by
various diagnostic search algorithms. Among those, Reiter's HS-Tree is one of
the most popular due its desirable properties and general applicability.
Usually, HS-Tree is used in a stateless fashion throughout the SD process to
(re)compute a sample of possible fault explanations in each iteration, each
time given the latest (updated) system knowledge including all so-far collected
measurements. At this, the built search tree is discarded between two
iterations, although often large parts of the tree have to be rebuilt in the
next iteration, involving redundant operations and calls to costly reasoning
services.
As a remedy to this, we propose DynamicHS, a variant of HS-Tree that
maintains state throughout the diagnostic session and additionally embraces
special strategies to minimize the number of expensive reasoner invocations. In
this vein, DynamicHS provides an answer to a longstanding question posed by
Raymond Reiter in his seminal paper from 1987.
Extensive evaluations on real-world diagnosis problems prove the
reasonability of the DynamicHS and testify its clear superiority to HS-Tree
wrt. computation time. More specifically, DynamicHS outperformed HS-Tree in 96%
of the executed sequential diagnosis sessions and, per run, the latter required
up to 800% the time of the former. Remarkably, DynamicHS achieves these
performance improvements while preserving all desirable properties as well as
the general applicability of HS-Tree.
| [
{
"version": "v1",
"created": "Mon, 21 Dec 2020 01:59:19 GMT"
}
] | 1,608,595,200,000 | [
[
"Rodler",
"Patrick",
""
]
] |
2012.11154 | Isaac Godfried | Isaac Godfried, Kriti Mahajan, Maggie Wang, Kevin Li, Pranjalya Tiwari | FlowDB a large scale precipitation, river, and flash flood dataset | NeurIPS 2020 Workshop Tackling Climate Change with Machine Learning | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Flooding results in 8 billion dollars of damage annually in the US and causes
the most deaths of any weather related event. Due to climate change scientists
expect more heavy precipitation events in the future. However, no current
datasets exist that contain both hourly precipitation and river flow data. We
introduce a novel hourly river flow and precipitation dataset and a second
subset of flash flood events with damage estimates and injury counts. Using
these datasets we create two challenges (1) general stream flow forecasting and
(2) flash flood damage estimation. We have created several publicly available
benchmarks and an easy to use package. Additionally, in the future we aim to
augment our dataset with snow pack data and soil index moisture data to improve
predictions.
| [
{
"version": "v1",
"created": "Mon, 21 Dec 2020 07:08:41 GMT"
}
] | 1,608,595,200,000 | [
[
"Godfried",
"Isaac",
""
],
[
"Mahajan",
"Kriti",
""
],
[
"Wang",
"Maggie",
""
],
[
"Li",
"Kevin",
""
],
[
"Tiwari",
"Pranjalya",
""
]
] |
2012.11243 | Yaman K Singla | Yaman Kumar, Swati Aggarwal, Debanjan Mahata, Rajiv Ratn Shah,
Ponnurangam Kumaraguru, Roger Zimmermann | Get It Scored Using AutoSAS -- An Automated System for Scoring Short
Answers | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the era of MOOCs, online exams are taken by millions of candidates, where
scoring short answers is an integral part. It becomes intractable to evaluate
them by human graders. Thus, a generic automated system capable of grading
these responses should be designed and deployed. In this paper, we present a
fast, scalable, and accurate approach towards automated Short Answer Scoring
(SAS). We propose and explain the design and development of a system for SAS,
namely AutoSAS. Given a question along with its graded samples, AutoSAS can
learn to grade that prompt successfully. This paper further lays down the
features such as lexical diversity, Word2Vec, prompt, and content overlap that
plays a pivotal role in building our proposed model. We also present a
methodology for indicating the factors responsible for scoring an answer. The
trained model is evaluated on an extensively used public dataset, namely
Automated Student Assessment Prize Short Answer Scoring (ASAP-SAS). AutoSAS
shows state-of-the-art performance and achieves better results by over 8% in
some of the question prompts as measured by Quadratic Weighted Kappa (QWK),
showing performance comparable to humans.
| [
{
"version": "v1",
"created": "Mon, 21 Dec 2020 10:47:30 GMT"
}
] | 1,608,595,200,000 | [
[
"Kumar",
"Yaman",
""
],
[
"Aggarwal",
"Swati",
""
],
[
"Mahata",
"Debanjan",
""
],
[
"Shah",
"Rajiv Ratn",
""
],
[
"Kumaraguru",
"Ponnurangam",
""
],
[
"Zimmermann",
"Roger",
""
]
] |
2012.11634 | Henrique Santos | Henrique Santos, Minor Gordon, Zhicheng Liang, Gretchen Forbush,
Deborah L. McGuinness | Exploring and Analyzing Machine Commonsense Benchmarks | Commonsense Knowledge Graphs Workshop 2021 (CSKGs) @AAAI-21 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Commonsense question-answering (QA) tasks, in the form of benchmarks, are
constantly being introduced for challenging and comparing commonsense QA
systems. The benchmarks provide question sets that systems' developers can use
to train and test new models before submitting their implementations to
official leaderboards. Although these tasks are created to evaluate systems in
identified dimensions (e.g. topic, reasoning type), this metadata is limited
and largely presented in an unstructured format or completely not present.
Because machine common sense is a fast-paced field, the problem of fully
assessing current benchmarks and systems with regards to these evaluation
dimensions is aggravated. We argue that the lack of a common vocabulary for
aligning these approaches' metadata limits researchers in their efforts to
understand systems' deficiencies and in making effective choices for future
tasks. In this paper, we first discuss this MCS ecosystem in terms of its
elements and their metadata. Then, we present how we are supporting the
assessment of approaches by initially focusing on commonsense benchmarks. We
describe our initial MCS Benchmark Ontology, an extensible common vocabulary
that formalizes benchmark metadata, and showcase how it is supporting the
development of a Benchmark tool that enables benchmark exploration and
analysis.
| [
{
"version": "v1",
"created": "Mon, 21 Dec 2020 19:01:55 GMT"
}
] | 1,608,681,600,000 | [
[
"Santos",
"Henrique",
""
],
[
"Gordon",
"Minor",
""
],
[
"Liang",
"Zhicheng",
""
],
[
"Forbush",
"Gretchen",
""
],
[
"McGuinness",
"Deborah L.",
""
]
] |
2012.11689 | Kai Wei | Jixuan Wang, Kai Wei, Martin Radfar, Weiwei Zhang, Clement Chung | Encoding Syntactic Knowledge in Transformer Encoder for Intent Detection
and Slot Filling | This is a pre-print version of paper accepted by AAAI2021 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We propose a novel Transformer encoder-based architecture with syntactical
knowledge encoded for intent detection and slot filling. Specifically, we
encode syntactic knowledge into the Transformer encoder by jointly training it
to predict syntactic parse ancestors and part-of-speech of each token via
multi-task learning. Our model is based on self-attention and feed-forward
layers and does not require external syntactic information to be available at
inference time. Experiments show that on two benchmark datasets, our models
with only two Transformer encoder layers achieve state-of-the-art results.
Compared to the previously best performed model without pre-training, our
models achieve absolute F1 score and accuracy improvement of 1.59% and 0.85%
for slot filling and intent detection on the SNIPS dataset, respectively. Our
models also achieve absolute F1 score and accuracy improvement of 0.1% and
0.34% for slot filling and intent detection on the ATIS dataset, respectively,
over the previously best performed model. Furthermore, the visualization of the
self-attention weights illustrates the benefits of incorporating syntactic
information during training.
| [
{
"version": "v1",
"created": "Mon, 21 Dec 2020 21:25:11 GMT"
}
] | 1,608,681,600,000 | [
[
"Wang",
"Jixuan",
""
],
[
"Wei",
"Kai",
""
],
[
"Radfar",
"Martin",
""
],
[
"Zhang",
"Weiwei",
""
],
[
"Chung",
"Clement",
""
]
] |
2012.11792 | Mehrdad Zakershahrak | Mehrdad Zakershahrak and Samira Ghodratnama | Are We On The Same Page? Hierarchical Explanation Generation for
Planning Tasks in Human-Robot Teaming using Reinforcement Learning | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Providing explanations is considered an imperative ability for an AI agent in
a human-robot teaming framework. The right explanation provides the rationale
behind an AI agent's decision-making. However, to maintain the human teammate's
cognitive demand to comprehend the provided explanations, prior works have
focused on providing explanations in a specific order or intertwining the
explanation generation with plan execution. Moreover, these approaches do not
consider the degree of details required to share throughout the provided
explanations. In this work, we argue that the agent-generated explanations,
especially the complex ones, should be abstracted to be aligned with the level
of details the human teammate desires to maintain the recipient's cognitive
load. Therefore, learning a hierarchical explanations model is a challenging
task. Moreover, the agent needs to follow a consistent high-level policy to
transfer the learned teammate preferences to a new scenario while lower-level
detailed plans are different. Our evaluation confirmed the process of
understanding an explanation, especially a complex and detailed explanation, is
hierarchical. The human preference that reflected this aspect corresponded
exactly to creating and employing abstraction for knowledge assimilation hidden
deeper in our cognitive process. We showed that hierarchical explanations
achieved better task performance and behavior interpretability while reduced
cognitive load. These results shed light on designing explainable agents
utilizing reinforcement learning and planning across various domains.
| [
{
"version": "v1",
"created": "Tue, 22 Dec 2020 02:14:52 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Feb 2021 03:42:47 GMT"
}
] | 1,614,556,800,000 | [
[
"Zakershahrak",
"Mehrdad",
""
],
[
"Ghodratnama",
"Samira",
""
]
] |
2012.11835 | Xuefei Ning | Xuefei Ning, Junbo Zhao, Wenshuo Li, Tianchen Zhao, Yin Zheng,
Huazhong Yang, Yu Wang | Discovering Robust Convolutional Architecture at Targeted Capacity: A
Multi-Shot Approach | 9 pages, 9 pages appendices | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Convolutional neural networks (CNNs) are vulnerable to adversarial examples,
and studies show that increasing the model capacity of an architecture topology
(e.g., width expansion) can bring consistent robustness improvements. This
reveals a clear robustness-efficiency trade-off that should be considered in
architecture design. In this paper, considering scenarios with capacity budget,
we aim to discover adversarially robust architecture at targeted capacities.
Recent studies employed one-shot neural architecture search (NAS) to discover
robust architectures. However, since the capacities of different topologies
cannot be aligned in the search process, one-shot NAS methods favor topologies
with larger capacities in the supernet. And the discovered topology might be
suboptimal when augmented to the targeted capacity. We propose a novel
multi-shot NAS method to address this issue and explicitly search for robust
architectures at targeted capacities. At the targeted FLOPs of 2000M, the
discovered MSRobNet-2000 outperforms the recent NAS-discovered architecture
RobNet-large under various criteria by a large margin of 4%-7%. And at the
targeted FLOPs of 1560M, MSRobNet-1560 surpasses another NAS-discovered
architecture RobNet-free by 2.3% and 1.3% in the clean and PGD-7 accuracies,
respectively. All codes are available at https://github.com/walkerning/aw\_nas.
| [
{
"version": "v1",
"created": "Tue, 22 Dec 2020 05:21:25 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Jan 2021 09:44:52 GMT"
},
{
"version": "v3",
"created": "Sat, 27 Mar 2021 03:36:02 GMT"
}
] | 1,617,062,400,000 | [
[
"Ning",
"Xuefei",
""
],
[
"Zhao",
"Junbo",
""
],
[
"Li",
"Wenshuo",
""
],
[
"Zhao",
"Tianchen",
""
],
[
"Zheng",
"Yin",
""
],
[
"Yang",
"Huazhong",
""
],
[
"Wang",
"Yu",
""
]
] |
2012.11936 | Valentina Anita Carriero | Nacira Abbas, Kholoud Alghamdi, Mortaza Alinam, Francesca Alloatti,
Glenda Amaral, Claudia d'Amato, Luigi Asprino, Martin Beno, Felix Bensmann,
Russa Biswas, Ling Cai, Riley Capshaw, Valentina Anita Carriero, Irene
Celino, Amine Dadoun, Stefano De Giorgis, Harm Delva, John Domingue, Michel
Dumontier, Vincent Emonet, Marieke van Erp, Paola Espinoza Arias, Omaima
Fallatah, Sebasti\'an Ferrada, Marc Gallofr\'e Oca\~na, Michalis Georgiou,
Genet Asefa Gesese, Frances Gillis-Webber, Francesca Giovannetti, Mar\`ia
Granados Buey, Ismail Harrando, Ivan Heibi, Vitor Horta, Laurine Huber,
Federico Igne, Mohamad Yaser Jaradeh, Neha Keshan, Aneta Koleva, Bilal
Koteich, Kabul Kurniawan, Mengya Liu, Chuangtao Ma, Lientje Maas, Martin
Mansfield, Fabio Mariani, Eleonora Marzi, Sepideh Mesbah, Maheshkumar Mistry,
Alba Catalina Morales Tirado, Anna Nguyen, Viet Bach Nguyen, Allard Oelen,
Valentina Pasqual, Heiko Paulheim, Axel Polleres, Margherita Porena, Jan
Portisch, Valentina Presutti, Kader Pustu-Iren, Ariam Rivas Mendez, Soheil
Roshankish, Sebastian Rudolph, Harald Sack, Ahmad Sakor, Jaime Salas, Thomas
Schleider, Meilin Shi, Gianmarco Spinaci, Chang Sun, Tabea Tietz, Molka
Tounsi Dhouib, Alessandro Umbrico, Wouter van den Berg, Weiqin Xu | Knowledge Graphs Evolution and Preservation -- A Technical Report from
ISWS 2019 | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the grand challenges discussed during the Dagstuhl Seminar "Knowledge
Graphs: New Directions for Knowledge Representation on the Semantic Web" and
described in its report is that of a: "Public FAIR Knowledge Graph of
Everything: We increasingly see the creation of knowledge graphs that capture
information about the entirety of a class of entities. [...] This grand
challenge extends this further by asking if we can create a knowledge graph of
"everything" ranging from common sense concepts to location based entities.
This knowledge graph should be "open to the public" in a FAIR manner
democratizing this mass amount of knowledge." Although linked open data (LOD)
is one knowledge graph, it is the closest realisation (and probably the only
one) to a public FAIR Knowledge Graph (KG) of everything. Surely, LOD provides
a unique testbed for experimenting and evaluating research hypotheses on open
and FAIR KG. One of the most neglected FAIR issues about KGs is their ongoing
evolution and long term preservation. We want to investigate this problem, that
is to understand what preserving and supporting the evolution of KGs means and
how these problems can be addressed. Clearly, the problem can be approached
from different perspectives and may require the development of different
approaches, including new theories, ontologies, metrics, strategies,
procedures, etc. This document reports a collaborative effort performed by 9
teams of students, each guided by a senior researcher as their mentor,
attending the International Semantic Web Research School (ISWS 2019). Each team
provides a different perspective to the problem of knowledge graph evolution
substantiated by a set of research questions as the main subject of their
investigation. In addition, they provide their working definition for KG
preservation and evolution.
| [
{
"version": "v1",
"created": "Tue, 22 Dec 2020 11:21:09 GMT"
}
] | 1,608,681,600,000 | [
[
"Abbas",
"Nacira",
""
],
[
"Alghamdi",
"Kholoud",
""
],
[
"Alinam",
"Mortaza",
""
],
[
"Alloatti",
"Francesca",
""
],
[
"Amaral",
"Glenda",
""
],
[
"d'Amato",
"Claudia",
""
],
[
"Asprino",
"Luigi",
""
],
[
"Beno",
"Martin",
""
],
[
"Bensmann",
"Felix",
""
],
[
"Biswas",
"Russa",
""
],
[
"Cai",
"Ling",
""
],
[
"Capshaw",
"Riley",
""
],
[
"Carriero",
"Valentina Anita",
""
],
[
"Celino",
"Irene",
""
],
[
"Dadoun",
"Amine",
""
],
[
"De Giorgis",
"Stefano",
""
],
[
"Delva",
"Harm",
""
],
[
"Domingue",
"John",
""
],
[
"Dumontier",
"Michel",
""
],
[
"Emonet",
"Vincent",
""
],
[
"van Erp",
"Marieke",
""
],
[
"Arias",
"Paola Espinoza",
""
],
[
"Fallatah",
"Omaima",
""
],
[
"Ferrada",
"Sebastián",
""
],
[
"Ocaña",
"Marc Gallofré",
""
],
[
"Georgiou",
"Michalis",
""
],
[
"Gesese",
"Genet Asefa",
""
],
[
"Gillis-Webber",
"Frances",
""
],
[
"Giovannetti",
"Francesca",
""
],
[
"Buey",
"Marìa Granados",
""
],
[
"Harrando",
"Ismail",
""
],
[
"Heibi",
"Ivan",
""
],
[
"Horta",
"Vitor",
""
],
[
"Huber",
"Laurine",
""
],
[
"Igne",
"Federico",
""
],
[
"Jaradeh",
"Mohamad Yaser",
""
],
[
"Keshan",
"Neha",
""
],
[
"Koleva",
"Aneta",
""
],
[
"Koteich",
"Bilal",
""
],
[
"Kurniawan",
"Kabul",
""
],
[
"Liu",
"Mengya",
""
],
[
"Ma",
"Chuangtao",
""
],
[
"Maas",
"Lientje",
""
],
[
"Mansfield",
"Martin",
""
],
[
"Mariani",
"Fabio",
""
],
[
"Marzi",
"Eleonora",
""
],
[
"Mesbah",
"Sepideh",
""
],
[
"Mistry",
"Maheshkumar",
""
],
[
"Tirado",
"Alba Catalina Morales",
""
],
[
"Nguyen",
"Anna",
""
],
[
"Nguyen",
"Viet Bach",
""
],
[
"Oelen",
"Allard",
""
],
[
"Pasqual",
"Valentina",
""
],
[
"Paulheim",
"Heiko",
""
],
[
"Polleres",
"Axel",
""
],
[
"Porena",
"Margherita",
""
],
[
"Portisch",
"Jan",
""
],
[
"Presutti",
"Valentina",
""
],
[
"Pustu-Iren",
"Kader",
""
],
[
"Mendez",
"Ariam Rivas",
""
],
[
"Roshankish",
"Soheil",
""
],
[
"Rudolph",
"Sebastian",
""
],
[
"Sack",
"Harald",
""
],
[
"Sakor",
"Ahmad",
""
],
[
"Salas",
"Jaime",
""
],
[
"Schleider",
"Thomas",
""
],
[
"Shi",
"Meilin",
""
],
[
"Spinaci",
"Gianmarco",
""
],
[
"Sun",
"Chang",
""
],
[
"Tietz",
"Tabea",
""
],
[
"Dhouib",
"Molka Tounsi",
""
],
[
"Umbrico",
"Alessandro",
""
],
[
"Berg",
"Wouter van den",
""
],
[
"Xu",
"Weiqin",
""
]
] |
2012.11957 | Yao Zhang | Yao Zhang, Xu Zhang, Jun Wang, Hongru Liang, Wenqiang Lei, Zhe Sun,
Adam Jatowt, Zhenglu Yang | Generalized Relation Learning with Semantic Correlation Awareness for
Link Prediction | Preprint of accepted AAAI2021 paper | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Developing link prediction models to automatically complete knowledge graphs
has recently been the focus of significant research interest. The current
methods for the link prediction taskhavetwonaturalproblems:1)the relation
distributions in KGs are usually unbalanced, and 2) there are many unseen
relations that occur in practical situations. These two problems limit the
training effectiveness and practical applications of the existing link
prediction models. We advocate a holistic understanding of KGs and we propose
in this work a unified Generalized Relation Learning framework GRL to address
the above two problems, which can be plugged into existing link prediction
models. GRL conducts a generalized relation learning, which is aware of
semantic correlations between relations that serve as a bridge to connect
semantically similar relations. After training with GRL, the closeness of
semantically similar relations in vector space and the discrimination of
dissimilar relations are improved. We perform comprehensive experiments on six
benchmarks to demonstrate the superior capability of GRL in the link prediction
task. In particular, GRL is found to enhance the existing link prediction
models making them insensitive to unbalanced relation distributions and capable
of learning unseen relations.
| [
{
"version": "v1",
"created": "Tue, 22 Dec 2020 12:22:03 GMT"
},
{
"version": "v2",
"created": "Sun, 18 Apr 2021 08:57:36 GMT"
}
] | 1,618,876,800,000 | [
[
"Zhang",
"Yao",
""
],
[
"Zhang",
"Xu",
""
],
[
"Wang",
"Jun",
""
],
[
"Liang",
"Hongru",
""
],
[
"Lei",
"Wenqiang",
""
],
[
"Sun",
"Zhe",
""
],
[
"Jatowt",
"Adam",
""
],
[
"Yang",
"Zhenglu",
""
]
] |
2012.12186 | Rinu Boney | Rinu Boney, Alexander Ilin, Juho Kannala, Jarno Sepp\"anen | Learning to Play Imperfect-Information Games by Imitating an Oracle
Planner | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider learning to play multiplayer imperfect-information games with
simultaneous moves and large state-action spaces. Previous attempts to tackle
such challenging games have largely focused on model-free learning methods,
often requiring hundreds of years of experience to produce competitive agents.
Our approach is based on model-based planning. We tackle the problem of partial
observability by first building an (oracle) planner that has access to the full
state of the environment and then distilling the knowledge of the oracle to a
(follower) agent which is trained to play the imperfect-information game by
imitating the oracle's choices. We experimentally show that planning with naive
Monte Carlo tree search does not perform very well in large combinatorial
action spaces. We therefore propose planning with a fixed-depth tree search and
decoupled Thompson sampling for action selection. We show that the planner is
able to discover efficient playing strategies in the games of Clash Royale and
Pommerman and the follower policy successfully learns to implement them by
training on a few hundred battles.
| [
{
"version": "v1",
"created": "Tue, 22 Dec 2020 17:29:57 GMT"
}
] | 1,608,681,600,000 | [
[
"Boney",
"Rinu",
""
],
[
"Ilin",
"Alexander",
""
],
[
"Kannala",
"Juho",
""
],
[
"Seppänen",
"Jarno",
""
]
] |
2012.12192 | Liang Ma | Liang Ma | Query Answering via Decentralized Search | Updated author list | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Expert networks are formed by a group of expert-professionals with different
specialties to collaboratively resolve specific queries posted to the network.
In such networks, when a query reaches an expert who does not have sufficient
expertise, this query needs to be routed to other experts for further
processing until it is completely solved; therefore, query answering efficiency
is sensitive to the underlying query routing mechanism being used. Among all
possible query routing mechanisms, decentralized search, operating purely on
each expert's local information without any knowledge of network global
structure, represents the most basic and scalable routing mechanism, which is
applicable to any network scenarios even in dynamic networks. However, there is
still a lack of fundamental understanding of the efficiency of decentralized
search in expert networks. In this regard, we investigate decentralized search
by quantifying its performance under a variety of network settings. Our key
findings reveal the existence of network conditions, under which decentralized
search can achieve significantly short query routing paths (i.e., between
$O(\log n)$ and $O(\log^2 n)$ hops, $n$: total number of experts in the
network). Based on such theoretical foundation, we further study how the unique
properties of decentralized search in expert networks is related to the
anecdotal small-world phenomenon. In addition, we demonstrate that
decentralized search is robust against estimation errors introduced by
misinterpreting the required expertise levels. To the best of our knowledge,
this is the first work studying fundamental behaviors of decentralized search
in expert networks. The developed performance bounds, confirmed by real
datasets, are able to assist in predicting network performance and designing
complex expert networks.
| [
{
"version": "v1",
"created": "Fri, 18 Dec 2020 14:46:49 GMT"
},
{
"version": "v2",
"created": "Sat, 26 Dec 2020 22:26:13 GMT"
}
] | 1,609,200,000,000 | [
[
"Ma",
"Liang",
""
]
] |
2012.12218 | Sein Minn | Sein Minn | BKT-LSTM: Efficient Student Modeling for knowledge tracing and student
performance prediction | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, we have seen a rapid rise in usage of online educational platforms.
The personalized education became crucially important in future learning
environments. Knowledge tracing (KT) refers to the detection of students'
knowledge states and predict future performance given their past outcomes for
providing adaptive solution to Intelligent Tutoring Systems (ITS). Bayesian
Knowledge Tracing (BKT) is a model to capture mastery level of each skill with
psychologically meaningful parameters and widely used in successful tutoring
systems. However, it is unable to detect learning transfer across skills
because each skill model is learned independently and shows lower efficiency in
student performance prediction. While recent KT models based on deep neural
networks shows impressive predictive power but it came with a price. Ten of
thousands of parameters in neural networks are unable to provide
psychologically meaningful interpretation that reflect to cognitive theory. In
this paper, we proposed an efficient student model called BKT-LSTM. It contains
three meaningful components: individual \textit{skill mastery} assessed by BKT,
\textit{ability profile} (learning transfer across skills) detected by k-means
clustering and \textit{problem difficulty}. All these components are taken into
account in student's future performance prediction by leveraging predictive
power of LSTM. BKT-LSTM outperforms state-of-the-art student models in
student's performance prediction by considering these meaningful features
instead of using binary values of student's past interaction in DKT. We also
conduct ablation studies on each of BKT-LSTM model components to examine their
value and each component shows significant contribution in student's
performance prediction. Thus, it has potential for providing adaptive and
personalized instruction in real-world educational systems.
| [
{
"version": "v1",
"created": "Tue, 22 Dec 2020 18:05:36 GMT"
},
{
"version": "v2",
"created": "Wed, 6 Jan 2021 03:46:09 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Jun 2021 16:03:53 GMT"
}
] | 1,623,196,800,000 | [
[
"Minn",
"Sein",
""
]
] |
2012.12262 | Mohammad Reza Davahli | Mohammad Reza Davahli | The Last State of Artificial Intelligence in Project Management | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Artificial intelligence (AI) has been used to advance different fields, such
as education, healthcare, and finance. However, the application of AI in the
field of project management (PM) has not progressed equally. This paper reports
on a systematic review of the published studies used to investigate the
application of AI in PM. This systematic review identified relevant papers
using Web of Science, Science Direct, and Google Scholar databases. Of the 652
articles found, 58 met the predefined criteria and were included in the review.
Included papers were classified per the following dimensions: PM knowledge
areas, PM processes, and AI techniques. The results indicated that the
application of AI in PM was in its early stages and AI models have not applied
for multiple PM processes especially in processes groups of project stakeholder
management, project procurements management, and project communication
management. However, the most popular PM processes among included papers were
project effort prediction and cost estimation, and the most popular AI
techniques were support vector machines, neural networks, and genetic
algorithms.
| [
{
"version": "v1",
"created": "Wed, 16 Dec 2020 05:10:08 GMT"
}
] | 1,608,681,600,000 | [
[
"Davahli",
"Mohammad Reza",
""
]
] |
2012.12335 | Carlos N\'u\~nez Molina | Carlos N\'u\~nez-Molina, Vladislav Nikolov, Ignacio Vellido, Juan
Fern\'andez-Olivares | Goal Reasoning by Selecting Subgoals with Deep Q-Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work we propose a goal reasoning method which learns to select
subgoals with Deep Q-Learning in order to decrease the load of a planner when
faced with scenarios with tight time restrictions, such as online execution
systems. We have designed a CNN-based goal selection module and trained it on a
standard video game environment, testing it on different games (planning
domains) and levels (planning problems) to measure its generalization
abilities. When comparing its performance with a satisfying planner, the
results obtained show both approaches are able to find plans of good quality,
but our method greatly decreases planning time. We conclude our approach can be
successfully applied to different types of domains (games), and shows good
generalization properties when evaluated on new levels (problems) of the same
game (domain).
| [
{
"version": "v1",
"created": "Tue, 22 Dec 2020 20:12:29 GMT"
}
] | 1,608,768,000,000 | [
[
"Núñez-Molina",
"Carlos",
""
],
[
"Nikolov",
"Vladislav",
""
],
[
"Vellido",
"Ignacio",
""
],
[
"Fernández-Olivares",
"Juan",
""
]
] |
2012.12588 | Jean-Guy Mailly | Jean-Guy Mailly and Julien Rossit | Stability in Abstract Argumentation | 7 pages, 7 figures, accepted to the 18th International Workshop on
Non-Monotonic Reasoning (NMR 2020) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The notion of stability in a structured argumentation setup characterizes
situations where the acceptance status associated with a given literal will not
be impacted by any future evolution of this setup. In this paper, we abstract
away from the logical structure of arguments, and we transpose this notion of
stability to the context of Dungean argumentation frameworks. In particular, we
show how this problem can be translated into reasoning with Argument-Incomplete
AFs. Then we provide preliminary complexity results for stability under four
prominent semantics, in the case of both credulous and skeptical reasoning.
Finally, we illustrate to what extent this notion can be useful with an
application to argument-based negotiation.
| [
{
"version": "v1",
"created": "Wed, 23 Dec 2020 10:34:38 GMT"
}
] | 1,608,768,000,000 | [
[
"Mailly",
"Jean-Guy",
""
],
[
"Rossit",
"Julien",
""
]
] |
2012.12634 | Simin Liu | Simin Liu | Overview of FPGA deep learning acceleration based on convolutional
neural network | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In recent years, deep learning has become more and more mature, and as a
commonly used algorithm in deep learning, convolutional neural networks have
been widely used in various visual tasks. In the past, research based on deep
learning algorithms mainly relied on hardware such as GPUs and CPUs. However,
with the increasing development of FPGAs, both field programmable logic gate
arrays, it has become the main implementation hardware platform that combines
various neural network deep learning algorithms This article is a review
article, which mainly introduces the related theories and algorithms of
convolution. It summarizes the application scenarios of several existing FPGA
technologies based on convolutional neural networks, and mainly introduces the
application of accelerators. At the same time, it summarizes some accelerators'
under-utilization of logic resources or under-utilization of memory bandwidth,
so that they can't get the best performance.
| [
{
"version": "v1",
"created": "Wed, 23 Dec 2020 12:44:24 GMT"
}
] | 1,608,768,000,000 | [
[
"Liu",
"Simin",
""
]
] |
2012.12732 | Giulio Mazzi | Giulio Mazzi, Alberto Castellini, Alessandro Farinelli | Identification of Unexpected Decisions in Partially Observable
Monte-Carlo Planning: a Rule-Based Approach | AAMAS 2021, 3-7 May 2021, London-UK (Virtual) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Partially Observable Monte-Carlo Planning (POMCP) is a powerful online
algorithm able to generate approximate policies for large Partially Observable
Markov Decision Processes. The online nature of this method supports
scalability by avoiding complete policy representation. The lack of an explicit
representation however hinders interpretability. In this work, we propose a
methodology based on Satisfiability Modulo Theory (SMT) for analyzing POMCP
policies by inspecting their traces, namely sequences of
belief-action-observation triplets generated by the algorithm. The proposed
method explores local properties of policy behavior to identify unexpected
decisions. We propose an iterative process of trace analysis consisting of
three main steps, i) the definition of a question by means of a parametric
logical formula describing (probabilistic) relationships between beliefs and
actions, ii) the generation of an answer by computing the parameters of the
logical formula that maximize the number of satisfied clauses (solving a
MAX-SMT problem), iii) the analysis of the generated logical formula and the
related decision boundaries for identifying unexpected decisions made by POMCP
with respect to the original question. We evaluate our approach on Tiger, a
standard benchmark for POMDPs, and a real-world problem related to mobile robot
navigation. Results show that the approach can exploit human knowledge on the
domain, outperforming state-of-the-art anomaly detection methods in identifying
unexpected decisions. An improvement of the Area Under Curve up to 47\% has
been achieved in our tests.
| [
{
"version": "v1",
"created": "Wed, 23 Dec 2020 15:09:28 GMT"
},
{
"version": "v2",
"created": "Wed, 28 Apr 2021 14:16:54 GMT"
}
] | 1,619,654,400,000 | [
[
"Mazzi",
"Giulio",
""
],
[
"Castellini",
"Alberto",
""
],
[
"Farinelli",
"Alessandro",
""
]
] |
2012.13026 | Siqi Wang | Xiren Zhou and Siqi Wang and Ruisheng Diao and Desong Bian and Jiahui
Duan and Di Shi | Rethink AI-based Power Grid Control: Diving Into Algorithm Design | Accepted by 34th NeurIPS Ml4eng Workshop, 2020 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Recently, deep reinforcement learning (DRL)-based approach has shown
promisein solving complex decision and control problems in power engineering
domain.In this paper, we present an in-depth analysis of DRL-based voltage
control fromaspects of algorithm selection, state space representation, and
reward engineering.To resolve observed issues, we propose a novel imitation
learning-based approachto directly map power grid operating points to effective
actions without any interimreinforcement learning process. The performance
results demonstrate that theproposed approach has strong generalization ability
with much less training time.The agent trained by imitation learning is
effective and robust to solve voltagecontrol problem and outperforms the former
RL agents.
| [
{
"version": "v1",
"created": "Wed, 23 Dec 2020 23:38:41 GMT"
}
] | 1,608,854,400,000 | [
[
"Zhou",
"Xiren",
""
],
[
"Wang",
"Siqi",
""
],
[
"Diao",
"Ruisheng",
""
],
[
"Bian",
"Desong",
""
],
[
"Duan",
"Jiahui",
""
],
[
"Shi",
"Di",
""
]
] |
2012.13037 | Daniel Kasenberg | Vasanth Sarathy, Daniel Kasenberg, Shivam Goel, Jivko Sinapov,
Matthias Scheutz | SPOTTER: Extending Symbolic Planning Operators through Targeted
Reinforcement Learning | Accepted to AAMAS 2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Symbolic planning models allow decision-making agents to sequence actions in
arbitrary ways to achieve a variety of goals in dynamic domains. However, they
are typically handcrafted and tend to require precise formulations that are not
robust to human error. Reinforcement learning (RL) approaches do not require
such models, and instead learn domain dynamics by exploring the environment and
collecting rewards. However, RL approaches tend to require millions of episodes
of experience and often learn policies that are not easily transferable to
other tasks. In this paper, we address one aspect of the open problem of
integrating these approaches: how can decision-making agents resolve
discrepancies in their symbolic planning models while attempting to accomplish
goals? We propose an integrated framework named SPOTTER that uses RL to augment
and support ("spot") a planning agent by discovering new operators needed by
the agent to accomplish goals that are initially unreachable for the agent.
SPOTTER outperforms pure-RL approaches while also discovering transferable
symbolic knowledge and does not require supervision, successful plan traces or
any a priori knowledge about the missing planning operator.
| [
{
"version": "v1",
"created": "Thu, 24 Dec 2020 00:31:02 GMT"
}
] | 1,608,854,400,000 | [
[
"Sarathy",
"Vasanth",
""
],
[
"Kasenberg",
"Daniel",
""
],
[
"Goel",
"Shivam",
""
],
[
"Sinapov",
"Jivko",
""
],
[
"Scheutz",
"Matthias",
""
]
] |
2012.13136 | Naeha Sharif | Naeha Sharif and Lyndon White and Mohammed Bennamoun and Wei Liu and
Syed Afaq Ali Shah | LCEval: Learned Composite Metric for Caption Evaluation | 18 pages | International Journal of Computer Vision (October 2019) | 10.1007/s11263-019-01206-z | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automatic evaluation metrics hold a fundamental importance in the development
and fine-grained analysis of captioning systems. While current evaluation
metrics tend to achieve an acceptable correlation with human judgements at the
system level, they fail to do so at the caption level. In this work, we propose
a neural network-based learned metric to improve the caption-level caption
evaluation. To get a deeper insight into the parameters which impact a learned
metrics performance, this paper investigates the relationship between different
linguistic features and the caption-level correlation of the learned metrics.
We also compare metrics trained with different training examples to measure the
variations in their evaluation. Moreover, we perform a robustness analysis,
which highlights the sensitivity of learned and handcrafted metrics to various
sentence perturbations. Our empirical analysis shows that our proposed metric
not only outperforms the existing metrics in terms of caption-level correlation
but it also shows a strong system-level correlation against human assessments.
| [
{
"version": "v1",
"created": "Thu, 24 Dec 2020 06:38:24 GMT"
}
] | 1,608,854,400,000 | [
[
"Sharif",
"Naeha",
""
],
[
"White",
"Lyndon",
""
],
[
"Bennamoun",
"Mohammed",
""
],
[
"Liu",
"Wei",
""
],
[
"Shah",
"Syed Afaq Ali",
""
]
] |
2012.13204 | Nassim Dehouche | Nassim Dehouche | Predicting Seminal Quality with the Dominance-Based Rough Sets Approach | null | ICIC Express Letters Volume 14, Number 7, July 2020 | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The paper relies on the clinical data of a previously published study. We
identify two very questionable assumptions of said work, namely confusing
evidence of absence and absence of evidence, and neglecting the ordinal nature
of attributes' domains. We then show that using an adequate ordinal methodology
such as the dominance-based rough sets approach (DRSA) can significantly
improve the predictive accuracy of the expert system, resulting in almost
complete accuracy for a dataset of 100 instances. Beyond the performance of
DRSA in solving the diagnosis problem at hand, these results suggest the
inadequacy and triviality of the underlying dataset. We provide links to open
data from the UCI machine learning repository to allow for an easy
verification/refutation of the claims made in this paper.
| [
{
"version": "v1",
"created": "Thu, 24 Dec 2020 11:45:32 GMT"
}
] | 1,608,854,400,000 | [
[
"Dehouche",
"Nassim",
""
]
] |
2012.13300 | Abhishek Dubey | Geoffrey Pettet and Ayan Mukhopadhyay and Mykel Kochenderfer and
Abhishek Dubey | Hierarchical Planning for Resource Allocation in Emergency Response
Systems | Accepted for publication in the proceedings of the 12th ACM/IEEE
International Conference on Cyber-Physical Systems (ICCPS-2021) | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | A classical problem in city-scale cyber-physical systems (CPS) is resource
allocation under uncertainty. Typically, such problems are modeled as Markov
(or semi-Markov) decision processes. While online, offline, and decentralized
approaches have been applied to such problems, they have difficulty scaling to
large decision problems. We present a general approach to hierarchical planning
that leverages structure in city-level CPS problems for resource allocation
under uncertainty. We use the emergency response as a case study and show how a
large resource allocation problem can be split into smaller problems. We then
create a principled framework for solving the smaller problems and tackling the
interaction between them. Finally, we use real-world data from Nashville,
Tennessee, a major metropolitan area in the United States, to validate our
approach. Our experiments show that the proposed approach outperforms
state-of-the-art approaches used in the field of emergency response.
| [
{
"version": "v1",
"created": "Thu, 24 Dec 2020 15:55:23 GMT"
},
{
"version": "v2",
"created": "Thu, 4 Mar 2021 03:17:15 GMT"
}
] | 1,614,902,400,000 | [
[
"Pettet",
"Geoffrey",
""
],
[
"Mukhopadhyay",
"Ayan",
""
],
[
"Kochenderfer",
"Mykel",
""
],
[
"Dubey",
"Abhishek",
""
]
] |
2012.13315 | Ellen Vitercik | Maria-Florina Balcan, Tuomas Sandholm, and Ellen Vitercik | Generalization in portfolio-based algorithm selection | AAAI 2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Portfolio-based algorithm selection has seen tremendous practical success
over the past two decades. This algorithm configuration procedure works by
first selecting a portfolio of diverse algorithm parameter settings, and then,
on a given problem instance, using an algorithm selector to choose a parameter
setting from the portfolio with strong predicted performance. Oftentimes, both
the portfolio and the algorithm selector are chosen using a training set of
typical problem instances from the application domain at hand. In this paper,
we provide the first provable guarantees for portfolio-based algorithm
selection. We analyze how large the training set should be to ensure that the
resulting algorithm selector's average performance over the training set is
close to its future (expected) performance. This involves analyzing three key
reasons why these two quantities may diverge: 1) the learning-theoretic
complexity of the algorithm selector, 2) the size of the portfolio, and 3) the
learning-theoretic complexity of the algorithm's performance as a function of
its parameters. We introduce an end-to-end learning-theoretic analysis of the
portfolio construction and algorithm selection together. We prove that if the
portfolio is large, overfitting is inevitable, even with an extremely simple
algorithm selector. With experiments, we illustrate a tradeoff exposed by our
theoretical analysis: as we increase the portfolio size, we can hope to include
a well-suited parameter setting for every possible problem instance, but it
becomes impossible to avoid overfitting.
| [
{
"version": "v1",
"created": "Thu, 24 Dec 2020 16:33:17 GMT"
}
] | 1,608,854,400,000 | [
[
"Balcan",
"Maria-Florina",
""
],
[
"Sandholm",
"Tuomas",
""
],
[
"Vitercik",
"Ellen",
""
]
] |
2012.13387 | Samira Ghodratnama | Samira Ghodratnama and Mehrdad Zakershahrak and Fariborz Sobhanmanesh | Adaptive Summaries: A Personalized Concept-based Summarization Approach
by Learning from Users' Feedback | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Exploring the tremendous amount of data efficiently to make a decision,
similar to answering a complicated question, is challenging with many
real-world application scenarios. In this context, automatic summarization has
substantial importance as it will provide the foundation for big data analytic.
Traditional summarization approaches optimize the system to produce a short
static summary that fits all users that do not consider the subjectivity aspect
of summarization, i.e., what is deemed valuable for different users, making
these approaches impractical in real-world use cases. This paper proposes an
interactive concept-based summarization model, called Adaptive Summaries, that
helps users make their desired summary instead of producing a single inflexible
summary. The system learns from users' provided information gradually while
interacting with the system by giving feedback in an iterative loop. Users can
choose either reject or accept action for selecting a concept being included in
the summary with the importance of that concept from users' perspectives and
confidence level of their feedback. The proposed approach can guarantee
interactive speed to keep the user engaged in the process. Furthermore, it
eliminates the need for reference summaries, which is a challenging issue for
summarization tasks. Evaluations show that Adaptive Summaries helps users make
high-quality summaries based on their preferences by maximizing the
user-desired content in the generated summaries.
| [
{
"version": "v1",
"created": "Thu, 24 Dec 2020 18:27:50 GMT"
},
{
"version": "v2",
"created": "Sun, 19 Dec 2021 02:05:08 GMT"
}
] | 1,640,044,800,000 | [
[
"Ghodratnama",
"Samira",
""
],
[
"Zakershahrak",
"Mehrdad",
""
],
[
"Sobhanmanesh",
"Fariborz",
""
]
] |
2012.13400 | Athirai A. Irissappane | Athirai A. Irissappane, Hanfei Yu, Yankun Shen, Anubha Agrawal, Gray
Stanton | Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data
via Adversarial Training | arXiv admin note: text overlap with arXiv:1903.08289 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Online reviews are a vital source of information when purchasing a service or
a product. Opinion spammers manipulate these reviews, deliberately altering the
overall perception of the service. Though there exists a corpus of online
reviews, only a few have been labeled as spam or non-spam, making it difficult
to train spam detection models. We propose an adversarial training mechanism
leveraging the capabilities of Generative Pre-Training 2 (GPT-2) for
classifying opinion spam with limited labeled data and a large set of unlabeled
data. Experiments on TripAdvisor and YelpZip datasets show that the proposed
model outperforms state-of-the-art techniques by at least 7% in terms of
accuracy when labeled data is limited. The proposed model can also generate
synthetic spam/non-spam reviews with reasonable perplexity, thereby, providing
additional labeled data during training.
| [
{
"version": "v1",
"created": "Thu, 24 Dec 2020 18:59:51 GMT"
}
] | 1,608,854,400,000 | [
[
"Irissappane",
"Athirai A.",
""
],
[
"Yu",
"Hanfei",
""
],
[
"Shen",
"Yankun",
""
],
[
"Agrawal",
"Anubha",
""
],
[
"Stanton",
"Gray",
""
]
] |
2012.14474 | Benjamin Goertzel | Ben Goertzel | Paraconsistent Foundations for Probabilistic Reasoning, Programming and
Concept Formation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is argued that 4-valued paraconsistent truth values (called here "p-bits")
can serve as a conceptual, mathematical and practical foundation for highly
AI-relevant forms of probabilistic logic and probabilistic programming and
concept formation.
First it is shown that appropriate averaging-across-situations and
renormalization of 4-valued p-bits operating in accordance with Constructible
Duality (CD) logic yields PLN (Probabilistic Logic Networks)
strength-and-confidence truth values. Then variations on the Curry-Howard
correspondence are used to map these paraconsistent and probabilistic logics
into probabilistic types suitable for use within dependent type based
programming languages.
Zach Weber's paraconsistent analysis of the sorites paradox is extended to
form a paraconsistent / probabilistic / fuzzy analysis of concept boundaries;
and a paraconsistent version of concept formation via Formal Concept Analysis
is presented, building on a definition of fuzzy property-value degrees in terms
of relative entropy on paraconsistent probability distributions.
These general points are fleshed out via reference to the realization of
probabilistic reasoning and programming and concept formation in the OpenCog
AGI framework which is centered on collaborative multi-algorithm updating of a
common knowledge metagraph.
| [
{
"version": "v1",
"created": "Mon, 28 Dec 2020 20:14:49 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Jan 2021 17:51:17 GMT"
}
] | 1,610,668,800,000 | [
[
"Goertzel",
"Ben",
""
]
] |
2012.14762 | Davide Mario Longo | Georg Gottlob, Matthias Lanzinger, Davide Mario Longo, Cem Okulmus and
Reinhard Pichler | The HyperTrac Project: Recent Progress and Future Research Directions on
Hypergraph Decompositions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Constraint Satisfaction Problems (CSPs) play a central role in many
applications in Artificial Intelligence and Operations Research. In general,
solving CSPs is NP-complete. The structure of CSPs is best described by
hypergraphs. Therefore, various forms of hypergraph decompositions have been
proposed in the literature to identify tractable fragments of CSPs. However,
also the computation of a concrete hypergraph decomposition is a challenging
task in itself. In this paper, we report on recent progress in the study of
hypergraph decompositions and we outline several directions for future
research.
| [
{
"version": "v1",
"created": "Tue, 29 Dec 2020 14:21:54 GMT"
}
] | 1,609,459,200,000 | [
[
"Gottlob",
"Georg",
""
],
[
"Lanzinger",
"Matthias",
""
],
[
"Longo",
"Davide Mario",
""
],
[
"Okulmus",
"Cem",
""
],
[
"Pichler",
"Reinhard",
""
]
] |
2012.15835 | Robert B. Allen | Robert B. Allen | Semantic Modeling with SUMO | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | We explore using the Suggested Upper Merged Ontology (SUMO) to develop a
semantic simulation. We provide two proof-of-concept demonstrations modeling
transitions in a simulated gasoline engine using a general-purpose programming
language. Rather than focusing on computationally highly intensive techniques,
we explore a less computationally intensive approach related to familiar
software engineering testing procedures. In addition, we propose structured
representations of terms based on linguistic approaches to lexicography.
| [
{
"version": "v1",
"created": "Thu, 31 Dec 2020 18:53:59 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Jan 2021 14:53:38 GMT"
},
{
"version": "v3",
"created": "Tue, 12 Jan 2021 18:13:42 GMT"
}
] | 1,610,496,000,000 | [
[
"Allen",
"Robert B.",
""
]
] |
2101.00058 | Mark Law | Mark Law | Conflict-driven Inductive Logic Programming | Under consideration in Theory and Practice of Logic Programming
(TPLP) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The goal of Inductive Logic Programming (ILP) is to learn a program that
explains a set of examples. Until recently, most research on ILP targeted
learning Prolog programs. The ILASP system instead learns Answer Set Programs
(ASP). Learning such expressive programs widens the applicability of ILP
considerably; for example, enabling preference learning, learning common-sense
knowledge, including defaults and exceptions, and learning non-deterministic
theories.
Early versions of ILASP can be considered meta-level ILP approaches, which
encode a learning task as a logic program and delegate the search to an ASP
solver. More recently, ILASP has shifted towards a new method, inspired by
conflict-driven SAT and ASP solvers. The fundamental idea of the approach,
called Conflict-driven ILP (CDILP), is to iteratively interleave the search for
a hypothesis with the generation of constraints which explain why the current
hypothesis does not cover a particular example. These coverage constraints
allow ILASP to rule out not just the current hypothesis, but an entire class of
hypotheses that do not satisfy the coverage constraint.
This paper formalises the CDILP approach and presents the ILASP3 and ILASP4
systems for CDILP, which are demonstrated to be more scalable than previous
ILASP systems, particularly in the presence of noise.
Under consideration in Theory and Practice of Logic Programming (TPLP).
| [
{
"version": "v1",
"created": "Thu, 31 Dec 2020 20:24:28 GMT"
},
{
"version": "v2",
"created": "Thu, 23 Dec 2021 10:17:55 GMT"
},
{
"version": "v3",
"created": "Fri, 14 Jan 2022 19:21:47 GMT"
}
] | 1,642,550,400,000 | [
[
"Law",
"Mark",
""
]
] |
2101.00280 | Joar Skalse | Joar Skalse | A General Counterexample to Any Decision Theory and Some Responses | 4 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper I present an argument and a general schema which can be used to
construct a problem case for any decision theory, in a way that could be taken
to show that one cannot formulate a decision theory that is never outperformed
by any other decision theory. I also present and discuss a number of possible
responses to this argument. One of these responses raises the question of what
it means for two decision problems to be "equivalent" in the relevant sense,
and gives an answer to this question which would invalidate the first argument.
However, this position would have further consequences for how we compare
different decision theories in decision problems already discussed in the
literature (including e.g. Newcomb's problem).
| [
{
"version": "v1",
"created": "Fri, 1 Jan 2021 17:47:11 GMT"
}
] | 1,609,804,800,000 | [
[
"Skalse",
"Joar",
""
]
] |
2101.00286 | Valentina Anita Carriero | Valentina Anita Carriero, Aldo Gangemi, Andrea Giovanni Nuzzolese,
Valentina Presutti | An Ontology Design Pattern for representing Recurrent Situations | null | null | 10.3233/SSW210013 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper, we present an Ontology Design Pattern for representing
situations that recur at regular periods and share some invariant factors,
which unify them conceptually: we refer to this set of recurring situations as
recurrent situation series. The proposed pattern appears to be foundational,
since it can be generalised for modelling the top-level domain-independent
concept of recurrence, which is strictly associated with invariance. The
pattern reuses other foundational patterns such as Collection, Description and
Situation, Classification, Sequence. Indeed, a recurrent situation series is
formalised as both a collection of situations occurring regularly over time and
unified according to some properties that are common to all the members, and a
situation itself, which provides a relational context to its members that
satisfy a reference description. Besides including some exemplifying instances
of this pattern, we show how it has been implemented and specialised to model
recurrent cultural events and ceremonies in ArCo, the Knowledge Graph of
Italian cultural heritage.
| [
{
"version": "v1",
"created": "Fri, 1 Jan 2021 18:20:13 GMT"
}
] | 1,654,560,000,000 | [
[
"Carriero",
"Valentina Anita",
""
],
[
"Gangemi",
"Aldo",
""
],
[
"Nuzzolese",
"Andrea Giovanni",
""
],
[
"Presutti",
"Valentina",
""
]
] |
2101.00675 | Mohamad Alissa | Mohamad Alissa, Issa Haddad, Jonathan Meyer, Jade Obeid, Nicolas
Wiecek, Sukrit Wongariyakavee | Sentiment Analysis for Open Domain Conversational Agent | 9 pages, 3 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The applicability of common sentiment analysis models to open domain human
robot interaction is investigated within this paper. The models are used on a
dataset specific to user interaction with the Alana system (a Alexa prize
system) in order to determine which would be more appropriate for the task of
identifying sentiment when a user interacts with a non-human driven socialbot.
With the identification of a model, various improvements are attempted and
detailed prior to integration into the Alana system. The study showed that a
Random Forest Model with 25 trees trained on the dataset specific to user
interaction with the Alana system combined with the dataset present in NLTK
Vader outperforms other models. The new system (called 'Rob') matches it's
output utterance sentiment with the user's utterance sentiment. This method is
expected to improve user experience because it builds upon the overall
sentiment detection which makes it seem that new system sympathises with user
feelings. Furthermore, the results obtained from the user feedback confirms our
expectation.
| [
{
"version": "v1",
"created": "Sun, 3 Jan 2021 18:03:52 GMT"
},
{
"version": "v2",
"created": "Thu, 15 Jul 2021 23:33:53 GMT"
}
] | 1,626,652,800,000 | [
[
"Alissa",
"Mohamad",
""
],
[
"Haddad",
"Issa",
""
],
[
"Meyer",
"Jonathan",
""
],
[
"Obeid",
"Jade",
""
],
[
"Wiecek",
"Nicolas",
""
],
[
"Wongariyakavee",
"Sukrit",
""
]
] |
2101.00692 | Guillem Franc\`es | Guillem Franc\`es, Blai Bonet, Hector Geffner | Learning General Policies from Small Examples Without Supervision | AAAI 2021, version extended with appendix containing full proofs and
experimental details | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generalized planning is concerned with the computation of general policies
that solve multiple instances of a planning domain all at once. It has been
recently shown that these policies can be computed in two steps: first, a
suitable abstraction in the form of a qualitative numerical planning problem
(QNP) is learned from sample plans, then the general policies are obtained from
the learned QNP using a planner. In this work, we introduce an alternative
approach for computing more expressive general policies which does not require
sample plans or a QNP planner. The new formulation is very simple and can be
cast in terms that are more standard in machine learning: a large but finite
pool of features is defined from the predicates in the planning examples using
a general grammar, and a small subset of features is sought for separating
"good" from "bad" state transitions, and goals from non-goals. The problems of
finding such a "separating surface" while labeling the transitions as "good" or
"bad" are jointly addressed as a single combinatorial optimization problem
expressed as a Weighted Max-SAT problem. The advantage of looking for the
simplest policy in the given feature space that solves the given examples,
possibly non-optimally, is that many domains have no general, compact policies
that are optimal. The approach yields general policies for a number of
benchmark domains.
| [
{
"version": "v1",
"created": "Sun, 3 Jan 2021 19:44:13 GMT"
},
{
"version": "v2",
"created": "Wed, 17 Feb 2021 19:52:39 GMT"
}
] | 1,613,692,800,000 | [
[
"Francès",
"Guillem",
""
],
[
"Bonet",
"Blai",
""
],
[
"Geffner",
"Hector",
""
]
] |
2101.00774 | Fengbin Zhu | Fengbin Zhu, Wenqiang Lei, Chao Wang, Jianming Zheng, Soujanya Poria,
Tat-Seng Chua | Retrieving and Reading: A Comprehensive Survey on Open-domain Question
Answering | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Open-domain Question Answering (OpenQA) is an important task in Natural
Language Processing (NLP), which aims to answer a question in the form of
natural language based on large-scale unstructured documents. Recently, there
has been a surge in the amount of research literature on OpenQA, particularly
on techniques that integrate with neural Machine Reading Comprehension (MRC).
While these research works have advanced performance to new heights on
benchmark datasets, they have been rarely covered in existing surveys on QA
systems. In this work, we review the latest research trends in OpenQA, with
particular attention to systems that incorporate neural MRC techniques.
Specifically, we begin with revisiting the origin and development of OpenQA
systems. We then introduce modern OpenQA architecture named "Retriever-Reader"
and analyze the various systems that follow this architecture as well as the
specific techniques adopted in each of the components. We then discuss key
challenges to developing OpenQA systems and offer an analysis of benchmarks
that are commonly used. We hope our work would enable researchers to be
informed of the recent advancement and also the open challenges in OpenQA
research, so as to stimulate further progress in this field.
| [
{
"version": "v1",
"created": "Mon, 4 Jan 2021 04:47:46 GMT"
},
{
"version": "v2",
"created": "Fri, 23 Apr 2021 07:25:37 GMT"
},
{
"version": "v3",
"created": "Sat, 8 May 2021 16:16:50 GMT"
}
] | 1,620,691,200,000 | [
[
"Zhu",
"Fengbin",
""
],
[
"Lei",
"Wenqiang",
""
],
[
"Wang",
"Chao",
""
],
[
"Zheng",
"Jianming",
""
],
[
"Poria",
"Soujanya",
""
],
[
"Chua",
"Tat-Seng",
""
]
] |
2101.00843 | Dennis Soemers | Cameron Browne and Dennis J. N. J. Soemers and Eric Piette | Strategic Features for General Games | Paper exactly as it appeared at KEG Workshop held at AAAI 2019 | Proceedings of the 2nd Workshop on Knowledge Extraction from Games
co-located with 33rd AAAI Conference on Artificial Intelligence (AAAI 2019) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This short paper describes an ongoing research project that requires the
automated self-play learning and evaluation of a large number of board games in
digital form. We describe the approach we are taking to determine relevant
features, for biasing MCTS playouts for arbitrary games played on arbitrary
geometries. Benefits of our approach include efficient implementation, the
potential to transfer learnt knowledge to new contexts, and the potential to
explain strategic knowledge embedded in features in human-comprehensible terms.
| [
{
"version": "v1",
"created": "Mon, 4 Jan 2021 09:30:07 GMT"
}
] | 1,609,804,800,000 | [
[
"Browne",
"Cameron",
""
],
[
"Soemers",
"Dennis J. N. J.",
""
],
[
"Piette",
"Eric",
""
]
] |
2101.01067 | Khan Md. Hasib | Md. Ashek-Al-Aziz, Sagar Mahmud, Md. Azizul Islam, Jubayer Al Mahmud,
Khan Md. Hasib | A Comparative Study of AHP and Fuzzy AHP Method for Inconsistent Data | 22 Pages, 9 Figures | International Journal of Sciences: Basic and Applied Research
(IJSBAR), Volume 54 Issue 4, Year 2020, Page - 16 -37 | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In various cases of decision analysis we use two popular methods: Analytical
Hierarchical Process (AHP) and Fuzzy based AHP or Fuzzy AHP. Both the methods
deal with stochastic data and can determine decision result through Multi
Criteria Decision Making (MCDM) process. Obviously resulting values of the two
methods are not same though same set of data is fed into them. In this research
work, we have tried to observe similarities and dissimilarities between two
methods outputs. Almost same trend or fluctuations in outputs have been seen
for both methods for same set of input data which are not consistent. Both
method outputs ups and down fluctuations are same for fifty percent cases.
| [
{
"version": "v1",
"created": "Wed, 23 Dec 2020 06:08:23 GMT"
}
] | 1,609,804,800,000 | [
[
"Ashek-Al-Aziz",
"Md.",
""
],
[
"Mahmud",
"Sagar",
""
],
[
"Islam",
"Md. Azizul",
""
],
[
"Mahmud",
"Jubayer Al",
""
],
[
"Hasib",
"Khan Md.",
""
]
] |
2101.01510 | Xiaowang Zhang | Peiyun Wu and Yunjie Wu and Linjuan Wu and Xiaowang Zhang and Zhiyong
Feng | Modeling Global Semantics for Question Answering over Knowledge Bases | 7 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Semantic parsing, as an important approach to question answering over
knowledge bases (KBQA), transforms a question into the complete query graph for
further generating the correct logical query. Existing semantic parsing
approaches mainly focus on relations matching with paying less attention to the
underlying internal structure of questions (e.g., the dependencies and
relations between all entities in a question) to select the query graph. In
this paper, we present a relational graph convolutional network (RGCN)-based
model gRGCN for semantic parsing in KBQA. gRGCN extracts the global semantics
of questions and their corresponding query graphs, including structure
semantics via RGCN and relational semantics (label representation of relations
between entities) via a hierarchical relation attention mechanism. Experiments
evaluated on benchmarks show that our model outperforms off-the-shelf models.
| [
{
"version": "v1",
"created": "Tue, 5 Jan 2021 13:51:14 GMT"
}
] | 1,609,891,200,000 | [
[
"Wu",
"Peiyun",
""
],
[
"Wu",
"Yunjie",
""
],
[
"Wu",
"Linjuan",
""
],
[
"Zhang",
"Xiaowang",
""
],
[
"Feng",
"Zhiyong",
""
]
] |
2101.01625 | Devleena Das | Devleena Das, Siddhartha Banerjee, Sonia Chernova | Explainable AI for Robot Failures: Generating Explanations that Improve
User Assistance in Fault Recovery | null | null | 10.1145/3434073.3444657 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the growing capabilities of intelligent systems, the integration of
robots in our everyday life is increasing. However, when interacting in such
complex human environments, the occasional failure of robotic systems is
inevitable. The field of explainable AI has sought to make complex-decision
making systems more interpretable but most existing techniques target domain
experts. On the contrary, in many failure cases, robots will require recovery
assistance from non-expert users. In this work, we introduce a new type of
explanation, that explains the cause of an unexpected failure during an agent's
plan execution to non-experts. In order for error explanations to be
meaningful, we investigate what types of information within a set of
hand-scripted explanations are most helpful to non-experts for failure and
solution identification. Additionally, we investigate how such explanations can
be autonomously generated, extending an existing encoder-decoder model, and
generalized across environments. We investigate such questions in the context
of a robot performing a pick-and-place manipulation task in the home
environment. Our results show that explanations capturing the context of a
failure and history of past actions, are the most effective for failure and
solution identification among non-experts. Furthermore, through a second user
evaluation, we verify that our model-generated explanations can generalize to
an unseen office environment, and are just as effective as the hand-scripted
explanations.
| [
{
"version": "v1",
"created": "Tue, 5 Jan 2021 16:16:39 GMT"
}
] | 1,628,121,600,000 | [
[
"Das",
"Devleena",
""
],
[
"Banerjee",
"Siddhartha",
""
],
[
"Chernova",
"Sonia",
""
]
] |
2101.01883 | Takahisa Imagawa | Takahisa Imagawa, Takuya Hiraoka, Yoshimasa Tsuruoka | Off-Policy Meta-Reinforcement Learning Based on Feature Embedding Spaces | 14pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Meta-reinforcement learning (RL) addresses the problem of sample inefficiency
in deep RL by using experience obtained in past tasks for a new task to be
solved.
However, most meta-RL methods require partially or fully on-policy data,
i.e., they cannot reuse the data collected by past policies, which hinders the
improvement of sample efficiency.
To alleviate this problem, we propose a novel off-policy meta-RL method,
embedding learning and evaluation of uncertainty (ELUE).
An ELUE agent is characterized by the learning of a feature embedding space
shared among tasks.
It learns a belief model over the embedding space and a belief-conditional
policy and Q-function.
Then, for a new task, it collects data by the pretrained policy, and updates
its belief based on the belief model.
Thanks to the belief update, the performance can be improved with a small
amount of data.
In addition, it updates the parameters of the neural networks to adjust the
pretrained relationships when there are enough data.
We demonstrate that ELUE outperforms state-of-the-art meta RL methods through
experiments on meta-RL benchmarks.
| [
{
"version": "v1",
"created": "Wed, 6 Jan 2021 05:51:38 GMT"
}
] | 1,609,977,600,000 | [
[
"Imagawa",
"Takahisa",
""
],
[
"Hiraoka",
"Takuya",
""
],
[
"Tsuruoka",
"Yoshimasa",
""
]
] |
2101.01953 | Alexis de Colnet | Alexis de Colnet | A Lower Bound on DNNF Encodings of Pseudo-Boolean Constraints | 8 pages, 10 pages including references | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Two major considerations when encoding pseudo-Boolean (PB) constraints into
SAT are the size of the encoding and its propagation strength, that is, the
guarantee that it has a good behaviour under unit propagation. Several
encodings with propagation strength guarantees rely upon prior compilation of
the constraints into DNNF (decomposable negation normal form), BDD (binary
decision diagram), or some other sub-variants. However it has been shown that
there exist PB-constraints whose ordered BDD (OBDD) representations, and thus
the inferred CNF encodings, all have exponential size. Since DNNFs are more
succinct than OBDDs, preferring encodings via DNNF to avoid size explosion
seems a legitimate choice. Yet in this paper, we prove the existence of
PB-constraints whose DNNFs all require exponential size.
| [
{
"version": "v1",
"created": "Wed, 6 Jan 2021 10:25:22 GMT"
}
] | 1,609,977,600,000 | [
[
"de Colnet",
"Alexis",
""
]
] |
2101.02046 | Junyi Li | Junyi Li, Tianyi Tang, Gaole He, Jinhao Jiang, Xiaoxuan Hu, Puzhao
Xie, Zhipeng Chen, Zhuohao Yu, Wayne Xin Zhao, Ji-Rong Wen | TextBox: A Unified, Modularized, and Extensible Framework for Text
Generation | 9 pages, 2 figures, 4 tables. For our GitHub page, see
https://github.com/RUCAIBox/TextBox | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we release an open-source library, called TextBox, to provide
a unified, modularized, and extensible text generation framework. TextBox aims
to support a broad set of text generation tasks and models. In our library, we
implement 21 text generation models on 9 benchmark datasets, covering the
categories of VAE, GAN, and pretrained language models. Meanwhile, our library
maintains sufficient modularity and extensibility by properly decomposing the
model architecture, inference, and learning process into highly reusable
modules, which allows users to easily incorporate new models into our
framework. The above features make TextBox specially suitable for researchers
and practitioners to quickly reproduce baseline models and develop new models.
TextBox is implemented based on PyTorch, and released under Apache License 2.0
at https://github.com/RUCAIBox/TextBox.
| [
{
"version": "v1",
"created": "Wed, 6 Jan 2021 14:02:42 GMT"
},
{
"version": "v2",
"created": "Thu, 7 Jan 2021 09:28:10 GMT"
},
{
"version": "v3",
"created": "Mon, 19 Apr 2021 08:36:14 GMT"
}
] | 1,618,876,800,000 | [
[
"Li",
"Junyi",
""
],
[
"Tang",
"Tianyi",
""
],
[
"He",
"Gaole",
""
],
[
"Jiang",
"Jinhao",
""
],
[
"Hu",
"Xiaoxuan",
""
],
[
"Xie",
"Puzhao",
""
],
[
"Chen",
"Zhipeng",
""
],
[
"Yu",
"Zhuohao",
""
],
[
"Zhao",
"Wayne Xin",
""
],
[
"Wen",
"Ji-Rong",
""
]
] |
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