id
stringlengths 9
10
| submitter
stringlengths 5
47
⌀ | authors
stringlengths 5
1.72k
| title
stringlengths 11
234
| comments
stringlengths 1
491
⌀ | journal-ref
stringlengths 4
396
⌀ | doi
stringlengths 13
97
⌀ | report-no
stringlengths 4
138
⌀ | categories
stringclasses 1
value | license
stringclasses 9
values | abstract
stringlengths 29
3.66k
| versions
listlengths 1
21
| update_date
int64 1,180B
1,718B
| authors_parsed
listlengths 1
98
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2005.13997 | Mark Keane | Mark T. Keane, Barry Smyth | Good Counterfactuals and Where to Find Them: A Case-Based Technique for
Generating Counterfactuals for Explainable AI (XAI) | 15 pages, 3 figures | 28th International Conference on Case Based Reasoning (ICCBR2020),
2020 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, a groundswell of research has identified the use of counterfactual
explanations as a potentially significant solution to the Explainable AI (XAI)
problem. It is argued that (a) technically, these counterfactual cases can be
generated by permuting problem-features until a class change is found, (b)
psychologically, they are much more causally informative than factual
explanations, (c) legally, they are GDPR-compliant. However, there are issues
around the finding of good counterfactuals using current techniques (e.g.
sparsity and plausibility). We show that many commonly-used datasets appear to
have few good counterfactuals for explanation purposes. So, we propose a new
case based approach for generating counterfactuals using novel ideas about the
counterfactual potential and explanatory coverage of a case-base. The new
technique reuses patterns of good counterfactuals, present in a case-base, to
generate analogous counterfactuals that can explain new problems and their
solutions. Several experiments show how this technique can improve the
counterfactual potential and explanatory coverage of case-bases that were
previously found wanting.
| [
{
"version": "v1",
"created": "Tue, 26 May 2020 14:05:10 GMT"
}
]
| 1,590,710,400,000 | [
[
"Keane",
"Mark T.",
""
],
[
"Smyth",
"Barry",
""
]
]
|
2005.14026 | Tajul Rosli Razak Mr | Tajul Rosli Razak, Iman Hazwam Abd Halim, Muhammad Nabil Fikri
Jamaludin, Mohammad Hafiz Ismail, Shukor Sanim Mohd Fauzi | An Exploratory Study of Hierarchical Fuzzy Systems Approach in
Recommendation System | null | Jurnal Intelek Vol 14 No 2 December 2019 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recommendation system or also known as a recommender system is a tool to help
the user in providing a suggestion of a specific dilemma. Thus, recently, the
interest in developing a recommendation system in many fields has increased.
Fuzzy Logic system (FLSs) is one of the approaches that can be used to model
the recommendation systems as it can deal with uncertainty and imprecise
information. However, one of the fundamental issues in FLS is the problem of
the curse of dimensionality. That is, the number of rules in FLSs is increasing
exponentially with the number of input variables. One effective way to overcome
this problem is by using Hierarchical Fuzzy System (HFSs). This paper aims to
explore the use of HFSs for Recommendation system. Specifically, we are
interested in exploring and comparing the HFS and FLS for the Career path
recommendation system (CPRS) based on four key criteria, namely topology, the
number of rules, the rules structures and interpretability. The findings
suggested that the HFS has advantages over FLS towards improving the
interpretability models, in the context of a recommendation system example.
This study contributes to providing an insight into the development of
interpretable HFSs in the Recommendation systems.
| [
{
"version": "v1",
"created": "Wed, 27 May 2020 09:01:44 GMT"
}
]
| 1,590,710,400,000 | [
[
"Razak",
"Tajul Rosli",
""
],
[
"Halim",
"Iman Hazwam Abd",
""
],
[
"Jamaludin",
"Muhammad Nabil Fikri",
""
],
[
"Ismail",
"Mohammad Hafiz",
""
],
[
"Fauzi",
"Shukor Sanim Mohd",
""
]
]
|
2005.14037 | Mohammad-Ali Javidian | Mohammad Ali Javidian, Marco Valtorta and Pooyan Jamshidi | Learning LWF Chain Graphs: an Order Independent Algorithm | arXiv admin note: substantial text overlap with arXiv:2002.10870,
arXiv:1910.01067; substantial text overlap with arXiv:1211.3295 by other
authors | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | LWF chain graphs combine directed acyclic graphs and undirected graphs. We
present a PC-like algorithm that finds the structure of chain graphs under the
faithfulness assumption to resolve the problem of scalability of the proposed
algorithm by Studeny (1997). We prove that our PC-like algorithm is order
dependent, in the sense that the output can depend on the order in which the
variables are given. This order dependence can be very pronounced in
high-dimensional settings. We propose two modifications of the PC-like
algorithm that remove part or all of this order dependence. Simulation results
under a variety of settings demonstrate the competitive performance of the
PC-like algorithms in comparison with the decomposition-based method, called
LCD algorithm, proposed by Ma et al. (2008) in low-dimensional settings and
improved performance in high-dimensional settings.
| [
{
"version": "v1",
"created": "Wed, 27 May 2020 01:05:49 GMT"
}
]
| 1,590,710,400,000 | [
[
"Javidian",
"Mohammad Ali",
""
],
[
"Valtorta",
"Marco",
""
],
[
"Jamshidi",
"Pooyan",
""
]
]
|
2006.00715 | Shengcai Liu | Kangfei Zhao, Shengcai Liu, Yu Rong, Jeffrey Xu Yu | Towards Feature-free TSP Solver Selection: A Deep Learning Approach | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Travelling Salesman Problem (TSP) is a classical NP-hard problem and has
broad applications in many disciplines and industries. In a large scale
location-based services system, users issue TSP queries concurrently, where a
TSP query is a TSP instance with $n$ points. In the literature, many advanced
TSP solvers are developed to find high-quality solutions. Such solvers can
solve some TSP instances efficiently but may take an extremely long time for
some other instances. Due to the diversity of TSP instances, it is well-known
that there exists no universal best solver dominating all other solvers on all
possible TSP instances. To solve TSP efficiently, in addition to developing new
TSP solvers, it needs to find a per-instance solver for each TSP instance,
which is known as the TSP solver selection problem. In this paper, for the
first time, we propose a deep learning framework, \CTAS, for TSP solver
selection in an end-to-end manner. Specifically, \CTAS exploits deep
convolutional neural networks to extract informative features from TSP
instances and involves data argumentation strategies to handle the scarcity of
labeled TSP instances. Moreover, to support large scale TSP solver selection,
we construct a challenging TSP benchmark dataset with 6,000 instances, which is
known as the largest TSP benchmark. Our \CTAS achieves over 2$\times$ speedup
of the average running time, comparing the single best solver, and outperforms
the state-of-the-art statistical models.
| [
{
"version": "v1",
"created": "Mon, 1 Jun 2020 04:48:36 GMT"
},
{
"version": "v2",
"created": "Wed, 14 Apr 2021 10:28:04 GMT"
}
]
| 1,618,444,800,000 | [
[
"Zhao",
"Kangfei",
""
],
[
"Liu",
"Shengcai",
""
],
[
"Rong",
"Yu",
""
],
[
"Yu",
"Jeffrey Xu",
""
]
]
|
2006.01011 | Mohammad Abdulaziz | Mohammad Abdulaziz and Dominik Berger | Computing Plan-Length Bounds Using Lengths of Longest Paths | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We devise a method to exactly compute the length of the longest simple path
in factored state spaces, like state spaces encountered in classical planning.
Although the complexity of this problem is NEXP-Hard, we show that our method
can be used to compute practically useful upper-bounds on lengths of plans. We
show that the computed upper-bounds are significantly (in many cases, orders of
magnitude) better than bounds produced by previous bounding techniques and that
they can be used to improve the SAT-based planning.
| [
{
"version": "v1",
"created": "Mon, 1 Jun 2020 15:16:50 GMT"
},
{
"version": "v2",
"created": "Mon, 1 Mar 2021 11:17:15 GMT"
}
]
| 1,614,643,200,000 | [
[
"Abdulaziz",
"Mohammad",
""
],
[
"Berger",
"Dominik",
""
]
]
|
2006.01195 | Konstantin Yakovlev S | Konstantin Yakovlev, Anton Andreychuk, Roni Stern | Revisiting Bounded-Suboptimal Safe Interval Path Planning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Safe-interval path planning (SIPP) is a powerful algorithm for finding a path
in the presence of dynamic obstacles. SIPP returns provably optimal solutions.
However, in many practical applications of SIPP such as path planning for
robots, one would like to trade-off optimality for shorter planning time. In
this paper we explore different ways to build a bounded-suboptimal SIPP and
discuss their pros and cons. We compare the different bounded-suboptimal
versions of SIPP experimentally. While there is no universal winner, the
results provide insights into when each method should be used.
| [
{
"version": "v1",
"created": "Mon, 1 Jun 2020 18:42:52 GMT"
}
]
| 1,591,142,400,000 | [
[
"Yakovlev",
"Konstantin",
""
],
[
"Andreychuk",
"Anton",
""
],
[
"Stern",
"Roni",
""
]
]
|
2006.01444 | Kai Sauerwald | Kai Sauerwald, Jonas Haldimann, Martin von Berg, Christoph Beierle | Descriptor Revision for Conditionals: Literal Descriptors and
Conditional Preservation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Descriptor revision by Hansson is a framework for addressing the problem of
belief change. In descriptor revision, different kinds of change processes are
dealt with in a joint framework. Individual change requirements are qualified
by specific success conditions expressed by a belief descriptor, and belief
descriptors can be combined by logical connectives. This is in contrast to the
currently dominating AGM paradigm shaped by Alchourr\'on, G\"ardenfors, and
Makinson, where different kinds of changes, like a revision or a contraction,
are dealt with separately. In this article, we investigate the realisation of
descriptor revision for a conditional logic while restricting descriptors to
the conjunction of literal descriptors. We apply the principle of conditional
preservation developed by Kern-Isberner to descriptor revision for
conditionals, show how descriptor revision for conditionals under these
restrictions can be characterised by a constraint satisfaction problem, and
implement it using constraint logic programming. Since our conditional logic
subsumes propositional logic, our approach also realises descriptor revision
for propositional logic.
| [
{
"version": "v1",
"created": "Tue, 2 Jun 2020 08:21:33 GMT"
}
]
| 1,591,142,400,000 | [
[
"Sauerwald",
"Kai",
""
],
[
"Haldimann",
"Jonas",
""
],
[
"von Berg",
"Martin",
""
],
[
"Beierle",
"Christoph",
""
]
]
|
2006.01473 | Emmanouil Rigas | Emmanouil Rigas, Panayiotis Kolios, Georgios Ellinas | Extending the Multiple Traveling Salesman Problem for Scheduling a Fleet
of Drones Performing Monitoring Missions | To appear in the 23rd IEEE International Conference on Intelligent
Transportation Systems | 23rd IEEE International Conference on Intelligent Transportation
Systems | 10.1109/ITSC45102.2020.9294568 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we schedule the travel path of a set of drones across a graph
where the nodes need to be visited multiple times at pre-defined points in
time. This is an extension of the well-known multiple traveling salesman
problem. The proposed formulation can be applied in several domains such as the
monitoring of traffic flows in a transportation network, or the monitoring of
remote locations to assist search and rescue missions. Aiming to find the
optimal schedule, the problem is formulated as an Integer Linear Program (ILP).
Given that the problem is highly combinatorial, the optimal solution scales
only for small sized problems. Thus, a greedy algorithm is also proposed that
uses a one-step look ahead heuristic search mechanism. In a detailed
evaluation, it is observed that the greedy algorithm has near-optimal
performance as it is on average at 92.06% of the optimal, while it can
potentially scale up to settings with hundreds of drones and locations.
| [
{
"version": "v1",
"created": "Tue, 2 Jun 2020 09:17:18 GMT"
}
]
| 1,609,891,200,000 | [
[
"Rigas",
"Emmanouil",
""
],
[
"Kolios",
"Panayiotis",
""
],
[
"Ellinas",
"Georgios",
""
]
]
|
2006.01503 | Loic Pauleve | Gilles Audemard (CRIL), Lo\"ic Paulev\'e (LaBRI), Laurent Simon
(LaBRI) | SAT Heritage: a community-driven effort for archiving, building and
running more than thousand SAT solvers | null | SAT 2020, The 23rd International Conference on Theory and
Applications of Satisfiability Testing, 2020, Alghero, Italy | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | SAT research has a long history of source code and binary releases, thanks to
competitions organized every year. However, since every cycle of competitions
has its own set of rules and an adhoc way of publishing source code and
binaries, compiling or even running any solver may be harder than what it
seems. Moreover, there has been more than a thousand solvers published so far,
some of them released in the early 90's. If the SAT community wants to archive
and be able to keep track of all the solvers that made its history, it urgently
needs to deploy an important effort. We propose to initiate a community-driven
effort to archive and to allow easy compilation and running of all SAT solvers
that have been released so far. We rely on the best tools for archiving and
building binaries (thanks to Docker, GitHub and Zenodo) and provide a
consistent and easy way for this. Thanks to our tool, building (or running) a
solver from its source (or from its binary) can be done in one line.
| [
{
"version": "v1",
"created": "Tue, 2 Jun 2020 10:03:56 GMT"
}
]
| 1,591,142,400,000 | [
[
"Audemard",
"Gilles",
"",
"CRIL"
],
[
"Paulevé",
"Loïc",
"",
"LaBRI"
],
[
"Simon",
"Laurent",
"",
"LaBRI"
]
]
|
2006.02256 | Catarina Moreira | Catarina Moreira and Matheus Hammes and Rasim Serdar Kurdoglu and
Peter Bruza | QuLBIT: Quantum-Like Bayesian Inference Technologies for Cognition and
Decision | null | Proceedings of the 42nd Annual Meeting of the Cognitive Science
Society, 2020 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper provides the foundations of a unified cognitive decision-making
framework (QulBIT) which is derived from quantum theory. The main advantage of
this framework is that it can cater for paradoxical and irrational human
decision making. Although quantum approaches for cognition have demonstrated
advantages over classical probabilistic approaches and bounded rationality
models, they still lack explanatory power. To address this, we introduce a
novel explanatory analysis of the decision-maker's belief space. This is
achieved by exploiting quantum interference effects as a way of both
quantifying and explaining the decision-maker's uncertainty. We detail the main
modules of the unified framework, the explanatory analysis method, and
illustrate their application in situations violating the Sure Thing Principle.
| [
{
"version": "v1",
"created": "Sat, 30 May 2020 09:02:03 GMT"
},
{
"version": "v2",
"created": "Mon, 28 Jun 2021 18:39:34 GMT"
}
]
| 1,625,011,200,000 | [
[
"Moreira",
"Catarina",
""
],
[
"Hammes",
"Matheus",
""
],
[
"Kurdoglu",
"Rasim Serdar",
""
],
[
"Bruza",
"Peter",
""
]
]
|
2006.03626 | Joseph Scott | Joseph Scott, Maysum Panju, and Vijay Ganesh | LGML: Logic Guided Machine Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce Logic Guided Machine Learning (LGML), a novel approach that
symbiotically combines machine learning (ML) and logic solvers with the goal of
learning mathematical functions from data. LGML consists of two phases, namely
a learning-phase and a logic-phase with a corrective feedback loop, such that,
the learning-phase learns symbolic expressions from input data, and the
logic-phase cross verifies the consistency of the learned expression with known
auxiliary truths. If inconsistent, the logic-phase feeds back "counterexamples"
to the learning-phase. This process is repeated until the learned expression is
consistent with auxiliary truth. Using LGML, we were able to learn expressions
that correspond to the Pythagorean theorem and the sine function, with several
orders of magnitude improvements in data efficiency compared to an approach
based on an out-of-the-box multi-layered perceptron (MLP).
| [
{
"version": "v1",
"created": "Fri, 5 Jun 2020 18:42:08 GMT"
},
{
"version": "v2",
"created": "Mon, 29 Mar 2021 19:27:23 GMT"
}
]
| 1,617,148,800,000 | [
[
"Scott",
"Joseph",
""
],
[
"Panju",
"Maysum",
""
],
[
"Ganesh",
"Vijay",
""
]
]
|
2006.04003 | Grace McFassel | Grace McFassel, Dylan A. Shell | Every Action Based Sensor | 16 pages, 7 figures, WAFR 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In studying robots and planning problems, a basic question is what is the
minimal information a robot must obtain to guarantee task completion. Erdmann's
theory of action-based sensors is a classical approach to characterizing
fundamental information requirements. That approach uses a plan to derive a
type of virtual sensor which prescribes actions that make progress toward a
goal. We show that the established theory is incomplete: the previous method
for obtaining such sensors, using backchained plans, overlooks some sensors.
Furthermore, there are plans, that are guaranteed to achieve goals, where the
existing methods are unable to provide any action-based sensor. We identify the
underlying feature common to all such plans. Then, we show how to produce
action-based sensors even for plans where the existing treatment is inadequate,
although for these cases they have no single canonical sensor. Consequently,
the approach is generalized to produce sets of sensors. Finally, we show also
that this is a complete characterization of action-based sensors for planning
problems and discuss how an action-based sensor translates into the traditional
conception of a sensor.
| [
{
"version": "v1",
"created": "Sun, 7 Jun 2020 00:30:54 GMT"
}
]
| 1,591,660,800,000 | [
[
"McFassel",
"Grace",
""
],
[
"Shell",
"Dylan A.",
""
]
]
|
2006.04042 | Sayyed Ali Hossayni | Sayyed-Ali Hossayni, Mohammad-R Akbarzadeh-T, Diego Reforgiato
Recupero, Aldo Gangemi, Esteve Del Acebo, Josep Llu\'is de la Rosa i Esteva | An Algorithm for Fuzzification of WordNets, Supported by a Mathematical
Proof | 6 pages, without figures, theoretical | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | WordNet-like Lexical Databases (WLDs) group English words into sets of
synonyms called "synsets." Although the standard WLDs are being used in many
successful Text-Mining applications, they have the limitation that word-senses
are considered to represent the meaning associated to their corresponding
synsets, to the same degree, which is not generally true. In order to overcome
this limitation, several fuzzy versions of synsets have been proposed. A common
trait of these studies is that, to the best of our knowledge, they do not aim
to produce fuzzified versions of the existing WLD's, but build new WLDs from
scratch, which has limited the attention received from the Text-Mining
community, many of whose resources and applications are based on the existing
WLDs. In this study, we present an algorithm for constructing fuzzy versions of
WLDs of any language, given a corpus of documents and a word-sense
disambiguation (WSD) system for that language. Then, using the
Open-American-National-Corpus and UKB WSD as algorithm inputs, we construct and
publish online the fuzzified version of English WordNet (FWN). We also propose
a theoretical (mathematical) proof of the validity of its results.
| [
{
"version": "v1",
"created": "Sun, 7 Jun 2020 04:47:40 GMT"
}
]
| 1,591,660,800,000 | [
[
"Hossayni",
"Sayyed-Ali",
""
],
[
"Akbarzadeh-T",
"Mohammad-R",
""
],
[
"Recupero",
"Diego Reforgiato",
""
],
[
"Gangemi",
"Aldo",
""
],
[
"Del Acebo",
"Esteve",
""
],
[
"Esteva",
"Josep Lluís de la Rosa i",
""
]
]
|
2006.04161 | Maulik Kamdar | Maulik R. Kamdar and Mark A. Musen | An Empirical Meta-analysis of the Life Sciences (Linked?) Open Data on
the Web | Under Review at Nature Scientific Data | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | While the biomedical community has published several "open data" sources in
the last decade, most researchers still endure severe logistical and technical
challenges to discover, query, and integrate heterogeneous data and knowledge
from multiple sources. To tackle these challenges, the community has
experimented with Semantic Web and linked data technologies to create the Life
Sciences Linked Open Data (LSLOD) cloud. In this paper, we extract schemas from
more than 80 publicly available biomedical linked data graphs into an LSLOD
schema graph and conduct an empirical meta-analysis to evaluate the extent of
semantic heterogeneity across the LSLOD cloud. We observe that several LSLOD
sources exist as stand-alone data sources that are not inter-linked with other
sources, use unpublished schemas with minimal reuse or mappings, and have
elements that are not useful for data integration from a biomedical
perspective. We envision that the LSLOD schema graph and the findings from this
research will aid researchers who wish to query and integrate data and
knowledge from multiple biomedical sources simultaneously on the Web.
| [
{
"version": "v1",
"created": "Sun, 7 Jun 2020 14:26:32 GMT"
}
]
| 1,591,660,800,000 | [
[
"Kamdar",
"Maulik R.",
""
],
[
"Musen",
"Mark A.",
""
]
]
|
2006.04167 | Agi Kurucz | Olga Gerasimova, Stanislav Kikot, Agi Kurucz, Vladimir Podolskii,
Michael Zakharyaschev | A tetrachotomy of ontology-mediated queries with a covering axiom | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Our concern is the problem of efficiently determining the data complexity of
answering queries mediated by description logic ontologies and constructing
their optimal rewritings to standard database queries. Originated in
ontology-based data access and datalog optimisation, this problem is known to
be computationally very complex in general, with no explicit syntactic
characterisations available. In this article, aiming to understand the
fundamental roots of this difficulty, we strip the problem to the bare bones
and focus on Boolean conjunctive queries mediated by a simple covering axiom
stating that one class is covered by the union of two other classes. We show
that, on the one hand, these rudimentary ontology-mediated queries, called
disjunctive sirups (or d-sirups), capture many features and difficulties of the
general case. For example, answering d-sirups is Pi^p_2-complete for combined
complexity and can be in AC0 or LogSpace-, NL-, P-, or coNP-complete for data
complexity (with the problem of recognising FO-rewritability of d-sirups being
2ExpTime-hard); some d-sirups only have exponential-size resolution proofs,
some only double-exponential-size positive existential FO-rewritings and
single-exponential-size nonrecursive datalog rewritings. On the other hand, we
prove a few partial sufficient and necessary conditions of FO- and
(symmetric/linear-) datalog rewritability of d-sirups. Our main technical
result is a complete and transparent syntactic AC0/NL/P/coNP tetrachotomy of
d-sirups with disjoint covering classes and a path-shaped Boolean conjunctive
query. To obtain this tetrachotomy, we develop new techniques for establishing
P- and coNP-hardness of answering non-Horn ontology-mediated queries as well as
showing that they can be answered in NL.
| [
{
"version": "v1",
"created": "Sun, 7 Jun 2020 14:47:07 GMT"
},
{
"version": "v2",
"created": "Sun, 19 Jul 2020 16:52:59 GMT"
},
{
"version": "v3",
"created": "Sat, 31 Jul 2021 15:00:18 GMT"
},
{
"version": "v4",
"created": "Thu, 5 May 2022 10:22:56 GMT"
}
]
| 1,651,795,200,000 | [
[
"Gerasimova",
"Olga",
""
],
[
"Kikot",
"Stanislav",
""
],
[
"Kurucz",
"Agi",
""
],
[
"Podolskii",
"Vladimir",
""
],
[
"Zakharyaschev",
"Michael",
""
]
]
|
2006.04387 | Laura Giordano | Laura Giordano and Daniele Theseider Dupr\'e | An ASP approach for reasoning in a concept-aware multipreferential
lightweight DL | Paper presented at the 36th International Conference on Logic
Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September
2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we develop a concept aware multi-preferential semantics for
dealing with typicality in description logics, where preferences are associated
with concepts, starting from a collection of ranked TBoxes containing
defeasible concept inclusions. Preferences are combined to define a
preferential interpretation in which defeasible inclusions can be evaluated.
The construction of the concept-aware multipreference semantics is related to
Brewka's framework for qualitative preferences. We exploit Answer Set
Programming (in particular, asprin) to achieve defeasible reasoning under the
multipreference approach for the lightweight description logic EL+bot.
The paper is under consideration for acceptance in TPLP.
| [
{
"version": "v1",
"created": "Mon, 8 Jun 2020 07:15:38 GMT"
},
{
"version": "v2",
"created": "Sat, 8 Aug 2020 07:53:44 GMT"
}
]
| 1,597,104,000,000 | [
[
"Giordano",
"Laura",
""
],
[
"Dupré",
"Daniele Theseider",
""
]
]
|
2006.04689 | Faisal Abu-Khzam | Faisal N. Abu-Khzam, Mohamed Mahmoud Abd El-Wahab and Noureldin Yosri | Graph Minors Meet Machine Learning: the Power of Obstructions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computational intractability has for decades motivated the development of a
plethora of methodologies that mainly aimed at a quality-time trade-off. The
use of Machine Learning techniques has finally emerged as one of the possible
tools to obtain approximate solutions to ${\cal NP}$-hard combinatorial
optimization problems. In a recent article, Dai et al. introduced a method for
computing such approximate solutions for instances of the Vertex Cover problem.
In this paper we consider the effectiveness of selecting a proper training
strategy by considering special problem instances called "obstructions" that we
believe carry some intrinsic properties of the problem itself. Capitalizing on
the recent work of Dai et al. on the Vertex Cover problem, and using the same
case study as well as 19 other problem instances, we show the utility of using
obstructions for training neural networks. Experiments show that training with
obstructions results in a huge reduction in number of iterations needed for
convergence, thus gaining a substantial reduction in the time needed for
training the model.
| [
{
"version": "v1",
"created": "Mon, 8 Jun 2020 15:40:04 GMT"
}
]
| 1,591,660,800,000 | [
[
"Abu-Khzam",
"Faisal N.",
""
],
[
"El-Wahab",
"Mohamed Mahmoud Abd",
""
],
[
"Yosri",
"Noureldin",
""
]
]
|
2006.04734 | Adrien Ecoffet | Adrien Ecoffet and Joel Lehman | Reinforcement Learning Under Moral Uncertainty | 28 pages, 18 figures; update adds discussion of a possible flaw of
Nash voting, discussion of further possible research into MEC, as well as a
few more references; updated to ICML version | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An ambitious goal for machine learning is to create agents that behave
ethically: The capacity to abide by human moral norms would greatly expand the
context in which autonomous agents could be practically and safely deployed,
e.g. fully autonomous vehicles will encounter charged moral decisions that
complicate their deployment. While ethical agents could be trained by rewarding
correct behavior under a specific moral theory (e.g. utilitarianism), there
remains widespread disagreement about the nature of morality. Acknowledging
such disagreement, recent work in moral philosophy proposes that ethical
behavior requires acting under moral uncertainty, i.e. to take into account
when acting that one's credence is split across several plausible ethical
theories. This paper translates such insights to the field of reinforcement
learning, proposes two training methods that realize different points among
competing desiderata, and trains agents in simple environments to act under
moral uncertainty. The results illustrate (1) how such uncertainty can help
curb extreme behavior from commitment to single theories and (2) several
technical complications arising from attempting to ground moral philosophy in
RL (e.g. how can a principled trade-off between two competing but incomparable
reward functions be reached). The aim is to catalyze progress towards
morally-competent agents and highlight the potential of RL to contribute
towards the computational grounding of moral philosophy.
| [
{
"version": "v1",
"created": "Mon, 8 Jun 2020 16:40:12 GMT"
},
{
"version": "v2",
"created": "Wed, 15 Jul 2020 00:15:50 GMT"
},
{
"version": "v3",
"created": "Mon, 19 Jul 2021 18:52:16 GMT"
}
]
| 1,626,825,600,000 | [
[
"Ecoffet",
"Adrien",
""
],
[
"Lehman",
"Joel",
""
]
]
|
2006.04856 | Julian Yarkony | Naveed Haghani, Jiaoyang Li, Sven Koenig, Gautam Kunapuli, Claudio
Contardo, Julian Yarkony | Integer Programming for Multi-Robot Planning: A Column Generation
Approach | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of coordinating a fleet of robots in a warehouse so
as to maximize the reward achieved within a time limit while respecting problem
and robot specific constraints. We formulate the problem as a weighted set
packing problem where elements are defined as being the space-time positions a
robot can occupy and the items that can be picked up and delivered. We enforce
that robots do not collide, that each item is delivered at most once, and that
the number of robots active at any time does not exceed the total number
available. Since the set of robot routes is not enumerable, we attack
optimization using column generation where pricing is a resource-constrained
shortest-path problem.
| [
{
"version": "v1",
"created": "Mon, 8 Jun 2020 18:19:14 GMT"
}
]
| 1,591,833,600,000 | [
[
"Haghani",
"Naveed",
""
],
[
"Li",
"Jiaoyang",
""
],
[
"Koenig",
"Sven",
""
],
[
"Kunapuli",
"Gautam",
""
],
[
"Contardo",
"Claudio",
""
],
[
"Yarkony",
"Julian",
""
]
]
|
2006.05219 | Amir Ahooye Atashin | Majid Mohammadi, Amir Ahooye Atashin, Wout Hofman, Yao-Hua Tan | SANOM Results for OAEI 2019 | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Simulated annealing-based ontology matching (SANOM) participates for the
second time at the ontology alignment evaluation initiative (OAEI) 2019. This
paper contains the configuration of SANOM and its results on the anatomy and
conference tracks. In comparison to the OAEI 2017, SANOM has improved
significantly, and its results are competitive with the state-of-the-art
systems. In particular, SANOM has the highest recall rate among the
participated systems in the conference track, and is competitive with AML, the
best performing system, in terms of F-measure. SANOM is also competitive with
LogMap on the anatomy track, which is the best performing system in this track
with no usage of particular biomedical background knowledge. SANOM has been
adapted to the HOBBIT platfrom and is now available for the registered users.
| [
{
"version": "v1",
"created": "Tue, 9 Jun 2020 12:33:47 GMT"
}
]
| 1,591,833,600,000 | [
[
"Mohammadi",
"Majid",
""
],
[
"Atashin",
"Amir Ahooye",
""
],
[
"Hofman",
"Wout",
""
],
[
"Tan",
"Yao-Hua",
""
]
]
|
2006.05894 | Ivan Bravi | Ivan Bravi and Simon Lucas | Rinascimento: using event-value functions for playing Splendor | To appear in IEEE Conference on Games 2019 Proceedings | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the realm of games research, Artificial General Intelligence algorithms
often use score as main reward signal for learning or playing actions. However
this has shown its severe limitations when the point rewards are very rare or
absent until the end of the game. This paper proposes a new approach based on
event logging: the game state triggers an event every time one of its features
changes. These events are processed by an Event-value Function (EF) that
assigns a value to a single action or a sequence. The experiments have shown
that such approach can mitigate the problem of scarce point rewards and improve
the AI performance. Furthermore this represents a step forward in controlling
the strategy adopted by the artificial agent, by describing a much richer and
controllable behavioural space through the EF. Tuned EF are able to neatly
synthesise the relevance of the events in the game. Agents using an EF show
more robust when playing games with several opponents.
| [
{
"version": "v1",
"created": "Wed, 10 Jun 2020 15:28:11 GMT"
}
]
| 1,591,833,600,000 | [
[
"Bravi",
"Ivan",
""
],
[
"Lucas",
"Simon",
""
]
]
|
2006.05962 | Pavel Surynek | Pavel Surynek | At-Most-One Constraints in Efficient Representations of Mutex Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The At-Most-One (AMO) constraint is a special case of cardinality constraint
that requires at most one variable from a set of Boolean variables to be set to
TRUE. AMO is important for modeling problems as Boolean satisfiability (SAT)
from domains where decision variables represent spatial or temporal placements
of some objects that cannot share the same spatial or temporal slot. The AMO
constraint can be used for more efficient representation and problem solving in
mutex networks consisting of pair-wise mutual exclusions forbidding pairs of
Boolean variable to be simultaneously TRUE. An on-line method for automated
detection of cliques for efficient representation of incremental mutex networks
where new mutexes arrive using AMOs is presented. A comparison of SAT-based
problem solving in mutex networks represented by AMO constraints using various
encodings is shown.
| [
{
"version": "v1",
"created": "Wed, 10 Jun 2020 17:21:06 GMT"
}
]
| 1,591,833,600,000 | [
[
"Surynek",
"Pavel",
""
]
]
|
2006.06054 | Zaheen Ahmad | Zaheen Farraz Ahmad, Levi H. S. Lelis, Michael Bowling | Marginal Utility for Planning in Continuous or Large Discrete Action
Spaces | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sample-based planning is a powerful family of algorithms for generating
intelligent behavior from a model of the environment. Generating good candidate
actions is critical to the success of sample-based planners, particularly in
continuous or large action spaces. Typically, candidate action generation
exhausts the action space, uses domain knowledge, or more recently, involves
learning a stochastic policy to provide such search guidance. In this paper we
explore explicitly learning a candidate action generator by optimizing a novel
objective, marginal utility. The marginal utility of an action generator
measures the increase in value of an action over previously generated actions.
We validate our approach in both curling, a challenging stochastic domain with
continuous state and action spaces, and a location game with a discrete but
large action space. We show that a generator trained with the marginal utility
objective outperforms hand-coded schemes built on substantial domain knowledge,
trained stochastic policies, and other natural objectives for generating
actions for sampled-based planners.
| [
{
"version": "v1",
"created": "Wed, 10 Jun 2020 20:24:53 GMT"
},
{
"version": "v2",
"created": "Wed, 17 Jun 2020 17:00:39 GMT"
}
]
| 1,592,438,400,000 | [
[
"Ahmad",
"Zaheen Farraz",
""
],
[
"Lelis",
"Levi H. S.",
""
],
[
"Bowling",
"Michael",
""
]
]
|
2006.06412 | Raunak Bhattacharyya | Raunak Bhattacharyya, Blake Wulfe, Derek Phillips, Alex Kuefler,
Jeremy Morton, Ransalu Senanayake, Mykel Kochenderfer | Modeling Human Driving Behavior through Generative Adversarial Imitation
Learning | 14 pages, 8 figures. To be published in the IEEE Transactions on
Intelligent Transportation Systems | null | 10.1109/TITS.2022.3227738 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An open problem in autonomous vehicle safety validation is building reliable
models of human driving behavior in simulation. This work presents an approach
to learn neural driving policies from real world driving demonstration data. We
model human driving as a sequential decision making problem that is
characterized by non-linearity and stochasticity, and unknown underlying cost
functions. Imitation learning is an approach for generating intelligent
behavior when the cost function is unknown or difficult to specify. Building
upon work in inverse reinforcement learning (IRL), Generative Adversarial
Imitation Learning (GAIL) aims to provide effective imitation even for problems
with large or continuous state and action spaces, such as modeling human
driving. This article describes the use of GAIL for learning-based driver
modeling. Because driver modeling is inherently a multi-agent problem, where
the interaction between agents needs to be modeled, this paper describes a
parameter-sharing extension of GAIL called PS-GAIL to tackle multi-agent driver
modeling. In addition, GAIL is domain agnostic, making it difficult to encode
specific knowledge relevant to driving in the learning process. This paper
describes Reward Augmented Imitation Learning (RAIL), which modifies the reward
signal to provide domain-specific knowledge to the agent. Finally, human
demonstrations are dependent upon latent factors that may not be captured by
GAIL. This paper describes Burn-InfoGAIL, which allows for disentanglement of
latent variability in demonstrations. Imitation learning experiments are
performed using NGSIM, a real-world highway driving dataset. Experiments show
that these modifications to GAIL can successfully model highway driving
behavior, accurately replicating human demonstrations and generating realistic,
emergent behavior in the traffic flow arising from the interaction between
driving agents.
| [
{
"version": "v1",
"created": "Wed, 10 Jun 2020 05:47:39 GMT"
},
{
"version": "v2",
"created": "Tue, 7 Feb 2023 12:35:19 GMT"
}
]
| 1,675,814,400,000 | [
[
"Bhattacharyya",
"Raunak",
""
],
[
"Wulfe",
"Blake",
""
],
[
"Phillips",
"Derek",
""
],
[
"Kuefler",
"Alex",
""
],
[
"Morton",
"Jeremy",
""
],
[
"Senanayake",
"Ransalu",
""
],
[
"Kochenderfer",
"Mykel",
""
]
]
|
2006.06547 | Alexander Turner | Alexander Matt Turner, Neale Ratzlaff, Prasad Tadepalli | Avoiding Side Effects in Complex Environments | Accepted as spotlight paper at NeurIPS 2020. 10 pages main paper; 19
pages with appendices | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reward function specification can be difficult. Rewarding the agent for
making a widget may be easy, but penalizing the multitude of possible negative
side effects is hard. In toy environments, Attainable Utility Preservation
(AUP) avoided side effects by penalizing shifts in the ability to achieve
randomly generated goals. We scale this approach to large, randomly generated
environments based on Conway's Game of Life. By preserving optimal value for a
single randomly generated reward function, AUP incurs modest overhead while
leading the agent to complete the specified task and avoid many side effects.
Videos and code are available at https://avoiding-side-effects.github.io/.
| [
{
"version": "v1",
"created": "Thu, 11 Jun 2020 16:02:30 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Oct 2020 15:15:46 GMT"
}
]
| 1,603,411,200,000 | [
[
"Turner",
"Alexander Matt",
""
],
[
"Ratzlaff",
"Neale",
""
],
[
"Tadepalli",
"Prasad",
""
]
]
|
2006.06630 | Alessandro Gianola | Silvio Ghilardi, Alessandro Gianola, Marco Montali, Andrey Rivkin | Petri Nets with Parameterised Data: Modelling and Verification (Extended
Version) | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | During the last decade, various approaches have been put forward to integrate
business processes with different types of data. Each of such approaches
reflects specific demands in the whole process-data integration spectrum. One
particular important point is the capability of these approaches to flexibly
accommodate processes with multiple cases that need to co-evolve. In this work,
we introduce and study an extension of coloured Petri nets, called
catalog-nets, providing two key features to capture this type of processes. On
the one hand, net transitions are equipped with guards that simultaneously
inspect the content of tokens and query facts stored in a read-only, persistent
database. On the other hand, such transitions can inject data into tokens by
extracting relevant values from the database or by generating genuinely fresh
ones. We systematically encode catalog-nets into one of the reference
frameworks for the (parameterised) verification of data and processes. We show
that fresh-value injection is a particularly complex feature to handle, and
discuss strategies to tame it. Finally, we discuss how catalog nets relate to
well-known formalisms in this area.
| [
{
"version": "v1",
"created": "Thu, 11 Jun 2020 17:26:08 GMT"
}
]
| 1,591,920,000,000 | [
[
"Ghilardi",
"Silvio",
""
],
[
"Gianola",
"Alessandro",
""
],
[
"Montali",
"Marco",
""
],
[
"Rivkin",
"Andrey",
""
]
]
|
2006.06896 | Yujia Shen | Yujia Shen, Arthur Choi, Adnan Darwiche | A New Perspective on Learning Context-Specific Independence | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Local structure such as context-specific independence (CSI) has received much
attention in the probabilistic graphical model (PGM) literature, as it
facilitates the modeling of large complex systems, as well as for reasoning
with them. In this paper, we provide a new perspective on how to learn CSIs
from data. We propose to first learn a functional and parameterized
representation of a conditional probability table (CPT), such as a neural
network. Next, we quantize this continuous function, into an arithmetic circuit
representation that facilitates efficient inference. In the first step, we can
leverage the many powerful tools that have been developed in the machine
learning literature. In the second step, we exploit more recently-developed
analytic tools from explainable AI, for the purposes of learning CSIs. Finally,
we contrast our approach, empirically and conceptually, with more traditional
variable-splitting approaches, that search for CSIs more explicitly.
| [
{
"version": "v1",
"created": "Fri, 12 Jun 2020 01:11:02 GMT"
}
]
| 1,592,179,200,000 | [
[
"Shen",
"Yujia",
""
],
[
"Choi",
"Arthur",
""
],
[
"Darwiche",
"Adnan",
""
]
]
|
2006.07532 | Tan Zhi-Xuan | Tan Zhi-Xuan, Jordyn L. Mann, Tom Silver, Joshua B. Tenenbaum, Vikash
K. Mansinghka | Online Bayesian Goal Inference for Boundedly-Rational Planning Agents | Accepted to NeurIPS 2020. 10 pages (excl. references), 6
figures/tables. (Supplement: 8 pages, 11 figures/tables). Code available at:
https://github.com/ztangent/Plinf.jl | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | People routinely infer the goals of others by observing their actions over
time. Remarkably, we can do so even when those actions lead to failure,
enabling us to assist others when we detect that they might not achieve their
goals. How might we endow machines with similar capabilities? Here we present
an architecture capable of inferring an agent's goals online from both optimal
and non-optimal sequences of actions. Our architecture models agents as
boundedly-rational planners that interleave search with execution by
replanning, thereby accounting for sub-optimal behavior. These models are
specified as probabilistic programs, allowing us to represent and perform
efficient Bayesian inference over an agent's goals and internal planning
processes. To perform such inference, we develop Sequential Inverse Plan Search
(SIPS), a sequential Monte Carlo algorithm that exploits the online replanning
assumption of these models, limiting computation by incrementally extending
inferred plans as new actions are observed. We present experiments showing that
this modeling and inference architecture outperforms Bayesian inverse
reinforcement learning baselines, accurately inferring goals from both optimal
and non-optimal trajectories involving failure and back-tracking, while
generalizing across domains with compositional structure and sparse rewards.
| [
{
"version": "v1",
"created": "Sat, 13 Jun 2020 01:48:10 GMT"
},
{
"version": "v2",
"created": "Sun, 25 Oct 2020 01:36:16 GMT"
}
]
| 1,603,756,800,000 | [
[
"Zhi-Xuan",
"Tan",
""
],
[
"Mann",
"Jordyn L.",
""
],
[
"Silver",
"Tom",
""
],
[
"Tenenbaum",
"Joshua B.",
""
],
[
"Mansinghka",
"Vikash K.",
""
]
]
|
2006.07970 | Hui Wang | Hui Wang, Mike Preuss, Michael Emmerich and Aske Plaat | Tackling Morpion Solitaire with AlphaZero-likeRanked Reward
Reinforcement Learning | 4 pages, 2 figures. the first/ongoing attempt to tackle Morpion
Solitaire using ranked reward reinforcement learning. submitted to SYNASC2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Morpion Solitaire is a popular single player game, performed with paper and
pencil. Due to its large state space (on the order of the game of Go)
traditional search algorithms, such as MCTS, have not been able to find good
solutions. A later algorithm, Nested Rollout Policy Adaptation, was able to
find a new record of 82 steps, albeit with large computational resources. After
achieving this record, to the best of our knowledge, there has been no further
progress reported, for about a decade.
In this paper we take the recent impressive performance of deep self-learning
reinforcement learning approaches from AlphaGo/AlphaZero as inspiration to
design a searcher for Morpion Solitaire. A challenge of Morpion Solitaire is
that the state space is sparse, there are few win/loss signals. Instead, we use
an approach known as ranked reward to create a reinforcement learning self-play
framework for Morpion Solitaire. This enables us to find medium-quality
solutions with reasonable computational effort. Our record is a 67 steps
solution, which is very close to the human best (68) without any other
adaptation to the problem than using ranked reward. We list many further
avenues for potential improvement.
| [
{
"version": "v1",
"created": "Sun, 14 Jun 2020 18:32:08 GMT"
}
]
| 1,592,265,600,000 | [
[
"Wang",
"Hui",
""
],
[
"Preuss",
"Mike",
""
],
[
"Emmerich",
"Michael",
""
],
[
"Plaat",
"Aske",
""
]
]
|
2006.08150 | Pascale Zarate | Sarfaraz Zolfani, Morteza Yazdani, Dragan Pamucar, Pascale Zarat\'e
(IRIT-ADRIA, IRIT, UT1) | A VIKOR and TOPSIS focused reanalysis of the MADM methods based on
logarithmic normalization | null | FACTA UNIVERSITATIS Series: Mechanical Engineering, University of
NIS, 2020 | 10.22190/FUME191129016Z | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decision and policy-makers in multi-criteria decision-making analysis take
into account some strategies in order to analyze outcomes and to finally make
an effective and more precise decision. Among those strategies, the
modification of the normalization process in the multiple-criteria
decision-making algorithm is still a question due to the confrontation of many
normalization tools. Normalization is the basic action in defining and solving
a MADM problem and a MADM model. Normalization is the first, also necessary,
step in solving, i.e. the application of a MADM method. It is a fact that the
selection of normalization methods has a direct effect on the results. One of
the latest normalization methods introduced is the Logarithmic Normalization
(LN) method. This new method has a distinguished advantage, reflecting in that
a sum of the normalized values of criteria always equals 1. This normalization
method had never been applied in any MADM methods before. This research study
is focused on the analysis of the classical MADM methods based on logarithmic
normalization. VIKOR and TOPSIS, as the two famous MADM methods, were selected
for this reanalysis research study. Two numerical examples were checked in both
methods, based on both the classical and the novel ways based on the LN. The
results indicate that there are differences between the two approaches.
Eventually, a sensitivity analysis is also designed to illustrate the
reliability of the final results.
| [
{
"version": "v1",
"created": "Mon, 15 Jun 2020 06:08:31 GMT"
}
]
| 1,592,265,600,000 | [
[
"Zolfani",
"Sarfaraz",
"",
"IRIT-ADRIA, IRIT, UT1"
],
[
"Yazdani",
"Morteza",
"",
"IRIT-ADRIA, IRIT, UT1"
],
[
"Pamucar",
"Dragan",
"",
"IRIT-ADRIA, IRIT, UT1"
],
[
"Zaraté",
"Pascale",
"",
"IRIT-ADRIA, IRIT, UT1"
]
]
|
2006.08295 | Jakub Kowalski | Jakub Kowalski, Rados{\l}aw Miernik, Maksymilian Mika, Wojciech
Pawlik, Jakub Sutowicz, Marek Szyku{\l}a, Andrzej Tkaczyk | Efficient Reasoning in Regular Boardgames | IEEE Conference on Games 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present the technical side of reasoning in Regular Boardgames (RBG)
language -- a universal General Game Playing (GGP) formalism for the class of
finite deterministic games with perfect information, encoding rules in the form
of regular expressions. RBG serves as a research tool that aims to aid in the
development of generalized algorithms for knowledge inference, analysis,
generation, learning, and playing games. In all these tasks, both generality
and efficiency are important.
In the first part, this paper describes optimizations used by the RBG
compiler. The impact of these optimizations ranges from 1.7 to even 33-fold
efficiency improvement when measuring the number of possible game playouts per
second. Then, we perform an in-depth efficiency comparison with three other
modern GGP systems (GDL, Ludii, Ai Ai). We also include our own highly
optimized game-specific reasoners to provide a point of reference of the
maximum speed. Our experiments show that RBG is currently the fastest among the
abstract general game playing languages, and its efficiency can be competitive
to common interface-based systems that rely on handcrafted game-specific
implementations. Finally, we discuss some issues and methodology of computing
benchmarks like this.
| [
{
"version": "v1",
"created": "Mon, 15 Jun 2020 11:42:08 GMT"
}
]
| 1,592,265,600,000 | [
[
"Kowalski",
"Jakub",
""
],
[
"Miernik",
"Radosław",
""
],
[
"Mika",
"Maksymilian",
""
],
[
"Pawlik",
"Wojciech",
""
],
[
"Sutowicz",
"Jakub",
""
],
[
"Szykuła",
"Marek",
""
],
[
"Tkaczyk",
"Andrzej",
""
]
]
|
2006.08343 | William Schoenberg | William Schoenberg | Automated Diagram Generation to Build Understanding and Usability | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Causal loop and stock and flow diagrams are broadly used in System Dynamics
because they help organize relationships and convey meaning. Using the
analytical work of Schoenberg (2019) to select what to include in a compressed
model, this paper demonstrates how that information can be clearly presented in
an automatically generated causal loop diagram. The diagrams are generated
using tools developed by people working in graph theory and the generated
diagrams are clear and aesthetically pleasing. This approach can also be built
upon to generate stock and flow diagrams. Automated stock and flow diagram
generation opens the door to representing models developed using only
equations, regardless or origin, in a clear and easy to understand way. Because
models can be large, the application of grouping techniques, again developed
for graph theory, can help structure the resulting diagrams in the most usable
form. This paper describes the algorithms developed for automated diagram
generation and shows a number of examples of their uses in large models. The
application of these techniques to existing, but inaccessible, equation-based
models can help broaden the knowledge base for System Dynamics modeling. The
techniques can also be used to improve layout in all, or part, of existing
models with diagrammatic informtion.
| [
{
"version": "v1",
"created": "Wed, 27 May 2020 22:32:16 GMT"
}
]
| 1,592,265,600,000 | [
[
"Schoenberg",
"William",
""
]
]
|
2006.08409 | Katerina Morozova | Alexander Gavrilenko, Katerina Morozova | Machine Common Sense | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine common sense remains a broad, potentially unbounded problem in
artificial intelligence (AI). There is a wide range of strategies that can be
employed to make progress on this challenge. This article deals with the
aspects of modeling commonsense reasoning focusing on such domain as
interpersonal interactions. The basic idea is that there are several types of
commonsense reasoning: one is manifested at the logical level of physical
actions, the other deals with the understanding of the essence of human-human
interactions. Existing approaches, based on formal logic and artificial neural
networks, allow for modeling only the first type of common sense. To model the
second type, it is vital to understand the motives and rules of human behavior.
This model is based on real-life heuristics, i.e., the rules of thumb,
developed through knowledge and experience of different generations. Such
knowledge base allows for development of an expert system with inference and
explanatory mechanisms (commonsense reasoning algorithms and personal models).
Algorithms provide tools for a situation analysis, while personal models make
it possible to identify personality traits. The system so designed should
perform the function of amplified intelligence for interactions, including
human-machine.
| [
{
"version": "v1",
"created": "Mon, 15 Jun 2020 13:59:47 GMT"
}
]
| 1,592,265,600,000 | [
[
"Gavrilenko",
"Alexander",
""
],
[
"Morozova",
"Katerina",
""
]
]
|
2006.08425 | William Schoenberg | Robert Eberlein, William Schoenberg | Finding the Loops that Matter | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Loops that Matter method (Schoenberg et. al, 2019) for understanding
model behavior provides metrics showing the contribution of the feedback loops
in a model to behavior at each point in time. To provide these metrics, it is
necessary find the set of loops on which to compute them. We show in this paper
the necessity of including loops that are important at different points in the
simulation. These important loops may not be independent of one another and
cannot be determined from static analysis of the model structure. We then
describe an algorithm that can be used to discover the most important loops in
models that are too feedback rich for exhaustive loop discovery. We demonstrate
the use of this algorithm in terms of its ability to find the most explanatory
loops, and its computational performance for large models. By using this
approach, the Loops that Matter method can be applied to models of any size or
complexity.
| [
{
"version": "v1",
"created": "Wed, 27 May 2020 22:27:58 GMT"
}
]
| 1,592,265,600,000 | [
[
"Eberlein",
"Robert",
""
],
[
"Schoenberg",
"William",
""
]
]
|
2006.08659 | James Goodman | James Goodman, Simon Lucas | Does it matter how well I know what you're thinking? Opponent Modelling
in an RTS game | Preprint of paper accepted for IEEE World Congress on Computational
Intelligence (IEEE WCCI) 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Opponent Modelling tries to predict the future actions of opponents, and is
required to perform well in multi-player games. There is a deep literature on
learning an opponent model, but much less on how accurate such models must be
to be useful. We investigate the sensitivity of Monte Carlo Tree Search (MCTS)
and a Rolling Horizon Evolutionary Algorithm (RHEA) to the accuracy of their
modelling of the opponent in a simple Real-Time Strategy game. We find that in
this domain RHEA is much more sensitive to the accuracy of an opponent model
than MCTS. MCTS generally does better even with an inaccurate model, while this
will degrade RHEA's performance. We show that faced with an unknown opponent
and a low computational budget it is better not to use any explicit model with
RHEA, and to model the opponent's actions within the tree as part of the MCTS
algorithm.
| [
{
"version": "v1",
"created": "Mon, 15 Jun 2020 18:10:22 GMT"
}
]
| 1,592,352,000,000 | [
[
"Goodman",
"James",
""
],
[
"Lucas",
"Simon",
""
]
]
|
2006.09196 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw A. K{\l}opotek | p-d-Separation -- A Concept for Expressing Dependence/Independence
Relations in Causal Networks | arXiv admin note: substantial text overlap with arXiv:1806.02373 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spirtes, Glymour and Scheines formulated a Conjecture that a direct
dependence test and a head-to-head meeting test would suffice to construe
directed acyclic graph decompositions of a joint probability distribution
(Bayesian network) for which Pearl's d-separation applies. This Conjecture was
later shown to be a direct consequence of a result of Pearl and Verma. This
paper is intended to prove this Conjecture in a new way, by exploiting the
concept of p-d-separation (partial dependency separation). While Pearl's
d-separation works with Bayesian networks, p-d-separation is intended to apply
to causal networks: that is partially oriented networks in which orientations
are given to only to those edges, that express statistically confirmed causal
influence, whereas undirected edges express existence of direct influence
without possibility of determination of direction of causation. As a
consequence of the particular way of proving the validity of this Conjecture,
an algorithm for construction of all the directed acyclic graphs (dags)
carrying the available independence information is also presented. The notion
of a partially oriented graph (pog) is introduced and within this graph the
notion of p-d-separation is defined. It is demonstrated that the p-d-separation
within the pog is equivalent to d-separation in all derived dags.
| [
{
"version": "v1",
"created": "Mon, 15 Jun 2020 09:30:12 GMT"
}
]
| 1,592,352,000,000 | [
[
"Kłopotek",
"Mieczysław A.",
""
]
]
|
2006.11309 | Tom Bewley | Tom Bewley, Jonathan Lawry, Arthur Richards | Modelling Agent Policies with Interpretable Imitation Learning | 6 pages, 3 figures; under review for the 1st TAILOR Workshop, due to
take place 29-30 August 2020 in Santiago de Compostela | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As we deploy autonomous agents in safety-critical domains, it becomes
important to develop an understanding of their internal mechanisms and
representations. We outline an approach to imitation learning for
reverse-engineering black box agent policies in MDP environments, yielding
simplified, interpretable models in the form of decision trees. As part of this
process, we explicitly model and learn agents' latent state representations by
selecting from a large space of candidate features constructed from the Markov
state. We present initial promising results from an implementation in a
multi-agent traffic environment.
| [
{
"version": "v1",
"created": "Fri, 19 Jun 2020 18:19:08 GMT"
}
]
| 1,592,870,400,000 | [
[
"Bewley",
"Tom",
""
],
[
"Lawry",
"Jonathan",
""
],
[
"Richards",
"Arthur",
""
]
]
|
2006.11560 | Helge Spieker | Helge Spieker, Arnaud Gotlieb | Learning Objective Boundaries for Constraint Optimization Problems | The 6th International Conference on machine Learning, Optimization
and Data science - LOD 2020 | In: Nicosia G. et al. (eds) Machine Learning, Optimization, and
Data Science. LOD 2020. Lecture Notes in Computer Science, vol 12566.
Springer, Cham | 10.1007/978-3-030-64580-9_33 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Constraint Optimization Problems (COP) are often considered without
sufficient knowledge on the boundaries of the objective variable to optimize.
When available, tight boundaries are helpful to prune the search space or
estimate problem characteristics. Finding close boundaries, that correctly
under- and overestimate the optimum, is almost impossible without actually
solving the COP. This paper introduces Bion, a novel approach for boundary
estimation by learning from previously solved instances of the COP. Based on
supervised machine learning, Bion is problem-specific and solver-independent
and can be applied to any COP which is repeatedly solved with different data
inputs. An experimental evaluation over seven realistic COPs shows that an
estimation model can be trained to prune the objective variables' domains by
over 80%. By evaluating the estimated boundaries with various COP solvers, we
find that Bion improves the solving process for some problems, although the
effect of closer bounds is generally problem-dependent.
| [
{
"version": "v1",
"created": "Sat, 20 Jun 2020 12:09:49 GMT"
}
]
| 1,647,993,600,000 | [
[
"Spieker",
"Helge",
""
],
[
"Gotlieb",
"Arnaud",
""
]
]
|
2006.11704 | Weihang Yuan | Weihang Yuan, H\'ector Mu\~noz-Avila | Hierarchical Reinforcement Learning for Deep Goal Reasoning: An
Expressiveness Analysis | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hierarchical DQN (h-DQN) is a two-level architecture of feedforward neural
networks where the meta level selects goals and the lower level takes actions
to achieve the goals. We show tasks that cannot be solved by h-DQN,
exemplifying the limitation of this type of hierarchical framework (HF). We
describe the recurrent hierarchical framework (RHF), generalizing architectures
that use a recurrent neural network at the meta level. We analyze the
expressiveness of HF and RHF using context-sensitive grammars. We show that RHF
is more expressive than HF. We perform experiments comparing an implementation
of RHF with two HF baselines; the results corroborate our theoretical findings.
| [
{
"version": "v1",
"created": "Sun, 21 Jun 2020 03:29:05 GMT"
}
]
| 1,592,870,400,000 | [
[
"Yuan",
"Weihang",
""
],
[
"Muñoz-Avila",
"Héctor",
""
]
]
|
2006.11814 | Michele Loi Dr. | Michele Loi and Lonneke van der Plas | A blindspot of AI ethics: anti-fragility in statistical prediction | 7th Swiss Conference on Data Science (accepted as Poster) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | With this paper, we aim to put an issue on the agenda of AI ethics that in
our view is overlooked in the current discourse. The current discussions are
dominated by topics suchas trustworthiness and bias, whereas the issue we like
to focuson is counter to the debate on trustworthiness. We fear that the
overuse of currently dominant AI systems that are driven by short-term
objectives and optimized for avoiding error leads to a society that loses its
diversity and flexibility needed for true progress. We couch our concerns in
the discourse around the term anti-fragility and show with some examples what
threats current methods used for decision making pose for society.
| [
{
"version": "v1",
"created": "Sun, 21 Jun 2020 14:46:55 GMT"
}
]
| 1,593,043,200,000 | [
[
"Loi",
"Michele",
""
],
[
"van der Plas",
"Lonneke",
""
]
]
|
2006.12020 | Stefan Sarkadi | OHAAI Collaboration: Federico Castagna, Timotheus Kampik, Atefeh
Keshavarzi Zafarghandi, Micka\"el Lafages, Jack Mumford, Christos T.
Rodosthenous, Samy S\'a, Stefan Sarkadi, Joseph Singleton, Kenneth Skiba,
Andreas Xydis | Online Handbook of Argumentation for AI: Volume 1 | editor: Federico Castagna and Francesca Mosca and Jack Mumford and
Stefan Sarkadi and Andreas Xydis | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This volume contains revised versions of the papers selected for the first
volume of the Online Handbook of Argumentation for AI (OHAAI). Previously,
formal theories of argument and argument interaction have been proposed and
studied, and this has led to the more recent study of computational models of
argument. Argumentation, as a field within artificial intelligence (AI), is
highly relevant for researchers interested in symbolic representations of
knowledge and defeasible reasoning. The purpose of this handbook is to provide
an open access and curated anthology for the argumentation research community.
OHAAI is designed to serve as a research hub to keep track of the latest and
upcoming PhD-driven research on the theory and application of argumentation in
all areas related to AI.
| [
{
"version": "v1",
"created": "Mon, 22 Jun 2020 06:07:13 GMT"
}
]
| 1,592,870,400,000 | [
[
"OHAAI Collaboration",
"",
""
],
[
"Castagna",
"Federico",
""
],
[
"Kampik",
"Timotheus",
""
],
[
"Zafarghandi",
"Atefeh Keshavarzi",
""
],
[
"Lafages",
"Mickaël",
""
],
[
"Mumford",
"Jack",
""
],
[
"Rodosthenous",
"Christos T.",
""
],
[
"Sá",
"Samy",
""
],
[
"Sarkadi",
"Stefan",
""
],
[
"Singleton",
"Joseph",
""
],
[
"Skiba",
"Kenneth",
""
],
[
"Xydis",
"Andreas",
""
]
]
|
2006.12344 | William T. Lunardi | Willian T. Lunardi, Ernesto G. Birgin, D\'ebora P. Ronconi, Holger
Voos | Metaheuristics for the Online Printing Shop Scheduling Problem | null | null | 10.1016/j.ejor.2020.12.021 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, the online printing shop scheduling problem introduced in
(Lunardi et al., Mixed Integer Linear Programming and Constraint Programming
Models for the Online Printing Shop Scheduling Problem, Computers & Operations
Research, to appear) is considered. This challenging real scheduling problem,
that emerged in the nowadays printing industry, corresponds to a flexible job
shop scheduling problem with sequencing flexibility; and it presents several
complicating specificities such as resumable operations, periods of
unavailability of the machines, sequence-dependent setup times, partial
overlapping between operations with precedence constraints, and fixed
operations, among others. A local search strategy and metaheuristic approaches
for the problem are proposed and evaluated. Based on a common representation
scheme, trajectory and populational metaheuristics are considered. Extensive
numerical experiments with large-sized instances show that the proposed methods
are suitable for solving practical instances of the problem; and that they
outperform a half-heuristic-half-exact off-the-shelf solver by a large extent.
Numerical experiments with classical instances of the flexible job shop
scheduling problem show that the introduced methods are also competitive when
applied to this particular case.
| [
{
"version": "v1",
"created": "Mon, 22 Jun 2020 15:38:00 GMT"
},
{
"version": "v2",
"created": "Sun, 20 Feb 2022 10:34:52 GMT"
}
]
| 1,645,488,000,000 | [
[
"Lunardi",
"Willian T.",
""
],
[
"Birgin",
"Ernesto G.",
""
],
[
"Ronconi",
"Débora P.",
""
],
[
"Voos",
"Holger",
""
]
]
|
2006.12632 | Benjamin Krarup | Benjamin Krarup, Senka Krivic, Felix Lindner, Derek Long | Towards Contrastive Explanations for Comparing the Ethics of Plans | Accepted to the ICRA-AGAINST-20 workshop | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The development of robotics and AI agents has enabled their wider usage in
human surroundings. AI agents are more trusted to make increasingly important
decisions with potentially critical outcomes. It is essential to consider the
ethical consequences of the decisions made by these systems. In this paper, we
present how contrastive explanations can be used for comparing the ethics of
plans. We build upon an existing ethical framework to allow users to make
suggestions to plans and receive contrastive explanations.
| [
{
"version": "v1",
"created": "Mon, 22 Jun 2020 21:38:16 GMT"
}
]
| 1,592,956,800,000 | [
[
"Krarup",
"Benjamin",
""
],
[
"Krivic",
"Senka",
""
],
[
"Lindner",
"Felix",
""
],
[
"Long",
"Derek",
""
]
]
|
2006.12692 | Lingheng Meng | Lingheng Meng, Rob Gorbet, Dana Kuli\'c | The Effect of Multi-step Methods on Overestimation in Deep Reinforcement
Learning | 7 pages, 4 figures, the 25th International Conference on Pattern
Recognition (ICPR) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-step (also called n-step) methods in reinforcement learning (RL) have
been shown to be more efficient than the 1-step method due to faster
propagation of the reward signal, both theoretically and empirically, in tasks
exploiting tabular representation of the value-function. Recently, research in
Deep Reinforcement Learning (DRL) also shows that multi-step methods improve
learning speed and final performance in applications where the value-function
and policy are represented with deep neural networks. However, there is a lack
of understanding about what is actually contributing to the boost of
performance. In this work, we analyze the effect of multi-step methods on
alleviating the overestimation problem in DRL, where multi-step experiences are
sampled from a replay buffer. Specifically building on top of Deep
Deterministic Policy Gradient (DDPG), we propose Multi-step DDPG (MDDPG), where
different step sizes are manually set, and its variant called Mixed Multi-step
DDPG (MMDDPG) where an average over different multi-step backups is used as
update target of Q-value function. Empirically, we show that both MDDPG and
MMDDPG are significantly less affected by the overestimation problem than DDPG
with 1-step backup, which consequently results in better final performance and
learning speed. We also discuss the advantages and disadvantages of different
ways to do multi-step expansion in order to reduce approximation error, and
expose the tradeoff between overestimation and underestimation that underlies
offline multi-step methods. Finally, we compare the computational resource
needs of Twin Delayed Deep Deterministic Policy Gradient (TD3), a state-of-art
algorithm proposed to address overestimation in actor-critic methods, and our
proposed methods, since they show comparable final performance and learning
speed.
| [
{
"version": "v1",
"created": "Tue, 23 Jun 2020 01:35:54 GMT"
}
]
| 1,592,956,800,000 | [
[
"Meng",
"Lingheng",
""
],
[
"Gorbet",
"Rob",
""
],
[
"Kulić",
"Dana",
""
]
]
|
2006.13473 | Chenwei Zhang | Xin Luna Dong, Xiang He, Andrey Kan, Xian Li, Yan Liang, Jun Ma, Yifan
Ethan Xu, Chenwei Zhang, Tong Zhao, Gabriel Blanco Saldana, Saurabh
Deshpande, Alexandre Michetti Manduca, Jay Ren, Surender Pal Singh, Fan Xiao,
Haw-Shiuan Chang, Giannis Karamanolakis, Yuning Mao, Yaqing Wang, Christos
Faloutsos, Andrew McCallum, Jiawei Han | AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of
Types | KDD 2020 | null | 10.1145/3394486.3403323 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Can one build a knowledge graph (KG) for all products in the world? Knowledge
graphs have firmly established themselves as valuable sources of information
for search and question answering, and it is natural to wonder if a KG can
contain information about products offered at online retail sites. There have
been several successful examples of generic KGs, but organizing information
about products poses many additional challenges, including sparsity and noise
of structured data for products, complexity of the domain with millions of
product types and thousands of attributes, heterogeneity across large number of
categories, as well as large and constantly growing number of products. We
describe AutoKnow, our automatic (self-driving) system that addresses these
challenges. The system includes a suite of novel techniques for taxonomy
construction, product property identification, knowledge extraction, anomaly
detection, and synonym discovery. AutoKnow is (a) automatic, requiring little
human intervention, (b) multi-scalable, scalable in multiple dimensions (many
domains, many products, and many attributes), and (c) integrative, exploiting
rich customer behavior logs. AutoKnow has been operational in collecting
product knowledge for over 11K product types.
| [
{
"version": "v1",
"created": "Wed, 24 Jun 2020 04:35:17 GMT"
}
]
| 1,593,043,200,000 | [
[
"Dong",
"Xin Luna",
""
],
[
"He",
"Xiang",
""
],
[
"Kan",
"Andrey",
""
],
[
"Li",
"Xian",
""
],
[
"Liang",
"Yan",
""
],
[
"Ma",
"Jun",
""
],
[
"Xu",
"Yifan Ethan",
""
],
[
"Zhang",
"Chenwei",
""
],
[
"Zhao",
"Tong",
""
],
[
"Saldana",
"Gabriel Blanco",
""
],
[
"Deshpande",
"Saurabh",
""
],
[
"Manduca",
"Alexandre Michetti",
""
],
[
"Ren",
"Jay",
""
],
[
"Singh",
"Surender Pal",
""
],
[
"Xiao",
"Fan",
""
],
[
"Chang",
"Haw-Shiuan",
""
],
[
"Karamanolakis",
"Giannis",
""
],
[
"Mao",
"Yuning",
""
],
[
"Wang",
"Yaqing",
""
],
[
"Faloutsos",
"Christos",
""
],
[
"McCallum",
"Andrew",
""
],
[
"Han",
"Jiawei",
""
]
]
|
2006.13607 | Forrest Bao | Youbiao He, Forrest Sheng Bao | Circuit Routing Using Monte Carlo Tree Search and Deep Neural Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Circuit routing is a fundamental problem in designing electronic systems such
as integrated circuits (ICs) and printed circuit boards (PCBs) which form the
hardware of electronics and computers. Like finding paths between pairs of
locations, circuit routing generates traces of wires to connect contacts or
leads of circuit components. It is challenging because finding paths between
dense and massive electronic components involves a very large search space.
Existing solutions are either manually designed with domain knowledge or
tailored to specific design rules, hence, difficult to adapt to new problems or
design needs. Therefore, a general routing approach is highly desired. In this
paper, we model the circuit routing as a sequential decision-making problem,
and solve it by Monte Carlo tree search (MCTS) with deep neural network (DNN)
guided rollout. It could be easily extended to routing cases with more routing
constraints and optimization goals. Experiments on randomly generated
single-layer circuits show the potential to route complex circuits. The
proposed approach can solve the problems that benchmark methods such as
sequential A* method and Lee's algorithm cannot solve, and can also outperform
the vanilla MCTS approach.
| [
{
"version": "v1",
"created": "Wed, 24 Jun 2020 10:34:57 GMT"
}
]
| 1,593,043,200,000 | [
[
"He",
"Youbiao",
""
],
[
"Bao",
"Forrest Sheng",
""
]
]
|
2006.14437 | V\'ictor Guti\'errez-Basulto | Yazm\'in Ib\'a\~nez-Garc\'ia, V\'ictor Guti\'errez-Basulto, Steven
Schockaert | Plausible Reasoning about EL-Ontologies using Concept Interpolation | 16 pages, 3 figures, accepted at KR 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Description logics (DLs) are standard knowledge representation languages for
modelling ontologies, i.e. knowledge about concepts and the relations between
them. Unfortunately, DL ontologies are difficult to learn from data and
time-consuming to encode manually. As a result, ontologies for broad domains
are almost inevitably incomplete. In recent years, several data-driven
approaches have been proposed for automatically extending such ontologies. One
family of methods rely on characterizations of concepts that are derived from
text descriptions. While such characterizations do not capture ontological
knowledge directly, they encode information about the similarity between
different concepts, which can be exploited for filling in the gaps in existing
ontologies. To this end, several inductive inference mechanisms have already
been proposed, but these have been defined and used in a heuristic fashion. In
this paper, we instead propose an inductive inference mechanism which is based
on a clear model-theoretic semantics, and can thus be tightly integrated with
standard deductive reasoning. We particularly focus on interpolation, a
powerful commonsense reasoning mechanism which is closely related to cognitive
models of category-based induction. Apart from the formalization of the
underlying semantics, as our main technical contribution we provide
computational complexity bounds for reasoning in EL with this interpolation
mechanism.
| [
{
"version": "v1",
"created": "Thu, 25 Jun 2020 14:19:41 GMT"
}
]
| 1,593,129,600,000 | [
[
"Ibáñez-García",
"Yazmín",
""
],
[
"Gutiérrez-Basulto",
"Víctor",
""
],
[
"Schockaert",
"Steven",
""
]
]
|
2006.14804 | Lin Guan | Lin Guan, Mudit Verma, Sihang Guo, Ruohan Zhang, Subbarao Kambhampati | Widening the Pipeline in Human-Guided Reinforcement Learning with
Explanation and Context-Aware Data Augmentation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human explanation (e.g., in terms of feature importance) has been recently
used to extend the communication channel between human and agent in interactive
machine learning. Under this setting, human trainers provide not only the
ground truth but also some form of explanation. However, this kind of human
guidance was only investigated in supervised learning tasks, and it remains
unclear how to best incorporate this type of human knowledge into deep
reinforcement learning. In this paper, we present the first study of using
human visual explanations in human-in-the-loop reinforcement learning (HRL). We
focus on the task of learning from feedback, in which the human trainer not
only gives binary evaluative "good" or "bad" feedback for queried state-action
pairs, but also provides a visual explanation by annotating relevant features
in images. We propose EXPAND (EXPlanation AugmeNted feeDback) to encourage the
model to encode task-relevant features through a context-aware data
augmentation that only perturbs irrelevant features in human salient
information. We choose five tasks, namely Pixel-Taxi and four Atari games, to
evaluate the performance and sample efficiency of this approach. We show that
our method significantly outperforms methods leveraging human explanation that
are adapted from supervised learning, and Human-in-the-loop RL baselines that
only utilize evaluative feedback.
| [
{
"version": "v1",
"created": "Fri, 26 Jun 2020 05:40:05 GMT"
},
{
"version": "v2",
"created": "Thu, 16 Jul 2020 23:08:58 GMT"
},
{
"version": "v3",
"created": "Fri, 25 Sep 2020 23:59:18 GMT"
},
{
"version": "v4",
"created": "Wed, 29 Sep 2021 18:32:45 GMT"
},
{
"version": "v5",
"created": "Tue, 26 Oct 2021 19:16:10 GMT"
}
]
| 1,635,379,200,000 | [
[
"Guan",
"Lin",
""
],
[
"Verma",
"Mudit",
""
],
[
"Guo",
"Sihang",
""
],
[
"Zhang",
"Ruohan",
""
],
[
"Kambhampati",
"Subbarao",
""
]
]
|
2006.14923 | Manfred Jaeger | Manfred Jaeger, Giorgio Bacci, Giovanni Bacci, Kim Guldstrand Larsen,
and Peter Gj{\o}l Jensen | Approximating Euclidean by Imprecise Markov Decision Processes | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Euclidean Markov decision processes are a powerful tool for modeling control
problems under uncertainty over continuous domains. Finite state imprecise,
Markov decision processes can be used to approximate the behavior of these
infinite models. In this paper we address two questions: first, we investigate
what kind of approximation guarantees are obtained when the Euclidean process
is approximated by finite state approximations induced by increasingly fine
partitions of the continuous state space. We show that for cost functions over
finite time horizons the approximations become arbitrarily precise. Second, we
use imprecise Markov decision process approximations as a tool to analyse and
validate cost functions and strategies obtained by reinforcement learning. We
find that, on the one hand, our new theoretical results validate basic design
choices of a previously proposed reinforcement learning approach. On the other
hand, the imprecise Markov decision process approximations reveal some
inaccuracies in the learned cost functions.
| [
{
"version": "v1",
"created": "Fri, 26 Jun 2020 11:58:04 GMT"
}
]
| 1,593,388,800,000 | [
[
"Jaeger",
"Manfred",
""
],
[
"Bacci",
"Giorgio",
""
],
[
"Bacci",
"Giovanni",
""
],
[
"Larsen",
"Kim Guldstrand",
""
],
[
"Jensen",
"Peter Gjøl",
""
]
]
|
2006.15811 | Jake Chandler | Jake Chandler, Richard Booth | Revision by Conditionals: From Hook to Arrow | Extended version of a paper accepted to KR 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The belief revision literature has largely focussed on the issue of how to
revise one's beliefs in the light of information regarding matters of fact.
Here we turn to an important but comparatively neglected issue: How might one
extend a revision operator to handle conditionals as input? Our approach to
this question of 'conditional revision' is distinctive insofar as it abstracts
from the controversial details of how to revise by factual sentences. We
introduce a 'plug and play' method for uniquely extending any iterated belief
revision operator to the conditional case. The flexibility of our approach is
achieved by having the result of a conditional revision by a Ramsey Test
conditional ('arrow') determined by that of a plain revision by its
corresponding material conditional ('hook'). It is shown to satisfy a number of
new constraints that are of independent interest.
| [
{
"version": "v1",
"created": "Mon, 29 Jun 2020 05:12:30 GMT"
}
]
| 1,593,475,200,000 | [
[
"Chandler",
"Jake",
""
],
[
"Booth",
"Richard",
""
]
]
|
2007.00364 | Evangelia Kyrimi | Evangelia Kyrimi, Mariana Raniere Neves, Scott McLachlan, Martin Neil,
William Marsh, Norman Fenton | Medical idioms for clinical Bayesian network development | null | null | 10.1016/j.jbi.2020.103495 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian Networks (BNs) are graphical probabilistic models that have proven
popular in medical applications. While numerous medical BNs have been
published, most are presented fait accompli without explanation of how the
network structure was developed or justification of why it represents the
correct structure for the given medical application. This means that the
process of building medical BNs from experts is typically ad hoc and offers
little opportunity for methodological improvement. This paper proposes
generally applicable and reusable medical reasoning patterns to aid those
developing medical BNs. The proposed method complements and extends the
idiom-based approach introduced by Neil, Fenton, and Nielsen in 2000. We
propose instances of their generic idioms that are specific to medical BNs. We
refer to the proposed medical reasoning patterns as medical idioms. In
addition, we extend the use of idioms to represent interventional and
counterfactual reasoning. We believe that the proposed medical idioms are
logical reasoning patterns that can be combined, reused and applied generically
to help develop medical BNs. All proposed medical idioms have been illustrated
using medical examples on coronary artery disease. The method has also been
applied to other ongoing BNs being developed with medical experts. Finally, we
show that applying the proposed medical idioms to published BN models results
in models with a clearer structure.
| [
{
"version": "v1",
"created": "Wed, 1 Jul 2020 10:10:52 GMT"
},
{
"version": "v2",
"created": "Thu, 2 Jul 2020 08:03:45 GMT"
}
]
| 1,593,734,400,000 | [
[
"Kyrimi",
"Evangelia",
""
],
[
"Neves",
"Mariana Raniere",
""
],
[
"McLachlan",
"Scott",
""
],
[
"Neil",
"Martin",
""
],
[
"Marsh",
"William",
""
],
[
"Fenton",
"Norman",
""
]
]
|
2007.00463 | Richa Verma | Richa Verma, Aniruddha Singhal, Harshad Khadilkar, Ansuma Basumatary,
Siddharth Nayak, Harsh Vardhan Singh, Swagat Kumar, Rajesh Sinha | A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing | 9 pages, 9 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving the
online 3D bin packing problem for an arbitrary number of bins and any bin size.
The focus is on producing decisions that can be physically implemented by a
robotic loading arm, a laboratory prototype used for testing the concept. The
problem considered in this paper is novel in two ways. First, unlike the
traditional 3D bin packing problem, we assume that the entire set of objects to
be packed is not known a priori. Instead, a fixed number of upcoming objects is
visible to the loading system, and they must be loaded in the order of arrival.
Second, the goal is not to move objects from one point to another via a
feasible path, but to find a location and orientation for each object that
maximises the overall packing efficiency of the bin(s). Finally, the learnt
model is designed to work with problem instances of arbitrary size without
retraining. Simulation results show that the RL-based method outperforms
state-of-the-art online bin packing heuristics in terms of empirical
competitive ratio and volume efficiency.
| [
{
"version": "v1",
"created": "Wed, 1 Jul 2020 13:02:04 GMT"
}
]
| 1,593,648,000,000 | [
[
"Verma",
"Richa",
""
],
[
"Singhal",
"Aniruddha",
""
],
[
"Khadilkar",
"Harshad",
""
],
[
"Basumatary",
"Ansuma",
""
],
[
"Nayak",
"Siddharth",
""
],
[
"Singh",
"Harsh Vardhan",
""
],
[
"Kumar",
"Swagat",
""
],
[
"Sinha",
"Rajesh",
""
]
]
|
2007.00820 | Anagha Kulkarni | Anagha Kulkarni, Sarath Sreedharan, Sarah Keren, Tathagata
Chakraborti, David Smith and Subbarao Kambhampati | Designing Environments Conducive to Interpretable Robot Behavior | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Designing robots capable of generating interpretable behavior is a
prerequisite for achieving effective human-robot collaboration. This means that
the robots need to be capable of generating behavior that aligns with human
expectations and, when required, provide explanations to the humans in the
loop. However, exhibiting such behavior in arbitrary environments could be
quite expensive for robots, and in some cases, the robot may not even be able
to exhibit the expected behavior. Given structured environments (like
warehouses and restaurants), it may be possible to design the environment so as
to boost the interpretability of the robot's behavior or to shape the human's
expectations of the robot's behavior. In this paper, we investigate the
opportunities and limitations of environment design as a tool to promote a type
of interpretable behavior -- known in the literature as explicable behavior. We
formulate a novel environment design framework that considers design over
multiple tasks and over a time horizon. In addition, we explore the
longitudinal aspect of explicable behavior and the trade-off that arises
between the cost of design and the cost of generating explicable behavior over
a time horizon.
| [
{
"version": "v1",
"created": "Thu, 2 Jul 2020 00:50:10 GMT"
},
{
"version": "v2",
"created": "Sun, 2 Aug 2020 16:34:05 GMT"
}
]
| 1,596,499,200,000 | [
[
"Kulkarni",
"Anagha",
""
],
[
"Sreedharan",
"Sarath",
""
],
[
"Keren",
"Sarah",
""
],
[
"Chakraborti",
"Tathagata",
""
],
[
"Smith",
"David",
""
],
[
"Kambhampati",
"Subbarao",
""
]
]
|
2007.01187 | Tom Bewley | Tom Bewley | Am I Building a White Box Agent or Interpreting a Black Box Agent? | 6 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rule extraction literature contains the notion of a fidelity-accuracy
dilemma: when building an interpretable model of a black box function,
optimising for fidelity is likely to reduce performance on the underlying task,
and vice versa. I reassert the relevance of this dilemma for the modern field
of explainable artificial intelligence, and highlight how it is compounded when
the black box is an agent interacting with a dynamic environment. I then
discuss two independent research directions - building white box agents and
interpreting black box agents - which are both coherent and worthy of
attention, but must not be conflated by researchers embarking on projects in
the domain of agent interpretability.
| [
{
"version": "v1",
"created": "Thu, 2 Jul 2020 15:20:43 GMT"
},
{
"version": "v2",
"created": "Fri, 3 Jul 2020 12:35:41 GMT"
},
{
"version": "v3",
"created": "Wed, 8 Jul 2020 15:06:14 GMT"
}
]
| 1,594,252,800,000 | [
[
"Bewley",
"Tom",
""
]
]
|
2007.01542 | Jeppe Theiss Kristensen | Jeppe Theiss Kristensen, Paolo Burelli | Strategies for Using Proximal Policy Optimization in Mobile Puzzle Games | 10 pages, 8 figures, to be published in 2020 Foundations of Digital
Games conference | null | 10.1145/3402942.3402944 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While traditionally a labour intensive task, the testing of game content is
progressively becoming more automated. Among the many directions in which this
automation is taking shape, automatic play-testing is one of the most promising
thanks also to advancements of many supervised and reinforcement learning (RL)
algorithms. However these type of algorithms, while extremely powerful, often
suffer in production environments due to issues with reliability and
transparency in their training and usage.
In this research work we are investigating and evaluating strategies to apply
the popular RL method Proximal Policy Optimization (PPO) in a casual mobile
puzzle game with a specific focus on improving its reliability in training and
generalization during game playing.
We have implemented and tested a number of different strategies against a
real-world mobile puzzle game (Lily's Garden from Tactile Games). We isolated
the conditions that lead to a failure in either training or generalization
during testing and we identified a few strategies to ensure a more stable
behaviour of the algorithm in this game genre.
| [
{
"version": "v1",
"created": "Fri, 3 Jul 2020 08:03:45 GMT"
}
]
| 1,625,788,800,000 | [
[
"Kristensen",
"Jeppe Theiss",
""
],
[
"Burelli",
"Paolo",
""
]
]
|
2007.01647 | Martin Stetter Ph.D. | Martin Stetter and Elmar W. Lang | Learning intuitive physics and one-shot imitation using
state-action-prediction self-organizing maps | 27 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human learning and intelligence work differently from the supervised pattern
recognition approach adopted in most deep learning architectures. Humans seem
to learn rich representations by exploration and imitation, build causal models
of the world, and use both to flexibly solve new tasks. We suggest a simple but
effective unsupervised model which develops such characteristics. The agent
learns to represent the dynamical physical properties of its environment by
intrinsically motivated exploration, and performs inference on this
representation to reach goals. For this, a set of self-organizing maps which
represent state-action pairs is combined with a causal model for sequence
prediction. The proposed system is evaluated in the cartpole environment. After
an initial phase of playful exploration, the agent can execute kinematic
simulations of the environment's future, and use those for action planning. We
demonstrate its performance on a set of several related, but different one-shot
imitation tasks, which the agent flexibly solves in an active inference style.
| [
{
"version": "v1",
"created": "Fri, 3 Jul 2020 12:29:11 GMT"
},
{
"version": "v2",
"created": "Fri, 8 Jan 2021 10:10:53 GMT"
},
{
"version": "v3",
"created": "Wed, 27 Oct 2021 09:33:07 GMT"
}
]
| 1,635,379,200,000 | [
[
"Stetter",
"Martin",
""
],
[
"Lang",
"Elmar W.",
""
]
]
|
2007.02352 | Xinrui Liu | Xinrui Liu | Mission schedule of agile satellites based on Proximal Policy
Optimization Algorithm | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mission schedule of satellites is an important part of space operation
nowadays, since the number and types of satellites in orbit are increasing
tremendously and their corresponding tasks are also becoming more and more
complicated. In this paper, a mission schedule model combined with Proximal
Policy Optimization Algorithm(PPO) is proposed. Different from the traditional
heuristic planning method, this paper incorporate reinforcement learning
algorithms into it and find a new way to describe the problem. Several
constraints including data download are considered in this paper.
| [
{
"version": "v1",
"created": "Sun, 5 Jul 2020 14:28:44 GMT"
}
]
| 1,594,080,000,000 | [
[
"Liu",
"Xinrui",
""
]
]
|
2007.02416 | Han Van Der Aa | Han van der Aa, Henrik Leopold, Matthias Weidlich | Partial Order Resolution of Event Logs for Process Conformance Checking | Accepted for publication in Decision Support Systems | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While supporting the execution of business processes, information systems
record event logs. Conformance checking relies on these logs to analyze whether
the recorded behavior of a process conforms to the behavior of a normative
specification. A key assumption of existing conformance checking techniques,
however, is that all events are associated with timestamps that allow to infer
a total order of events per process instance. Unfortunately, this assumption is
often violated in practice. Due to synchronization issues, manual event
recordings, or data corruption, events are only partially ordered. In this
paper, we put forward the problem of partial order resolution of event logs to
close this gap. It refers to the construction of a probability distribution
over all possible total orders of events of an instance. To cope with the order
uncertainty in real-world data, we present several estimators for this task,
incorporating different notions of behavioral abstraction. Moreover, to reduce
the runtime of conformance checking based on partial order resolution, we
introduce an approximation method that comes with a bounded error in terms of
accuracy. Our experiments with real-world and synthetic data reveal that our
approach improves accuracy over the state-of-the-art considerably.
| [
{
"version": "v1",
"created": "Sun, 5 Jul 2020 18:43:57 GMT"
}
]
| 1,594,080,000,000 | [
[
"van der Aa",
"Han",
""
],
[
"Leopold",
"Henrik",
""
],
[
"Weidlich",
"Matthias",
""
]
]
|
2007.02489 | Florian Richter | Florian Richter | Space of Reasons and Mathematical Model | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inferential relations govern our concept use. In order to understand a
concept it has to be located in a space of implications. There are different
kinds of conditions for statements, i.e. that the conditions represent
different kinds of explanations, e.g. causal or conceptual explanations. The
crucial questions is: How can the conditionality of language use be
represented. The conceptual background of representation in models is discussed
and in the end I propose how implications of propositional logic and conceptual
determinations can be represented in a model of a neural network.
| [
{
"version": "v1",
"created": "Mon, 6 Jul 2020 01:13:43 GMT"
}
]
| 1,594,080,000,000 | [
[
"Richter",
"Florian",
""
]
]
|
2007.02527 | Thomas Ringstrom | Thomas J. Ringstrom, Mohammadhosein Hasanbeig, Alessandro Abate | Jump Operator Planning: Goal-Conditioned Policy Ensembles and Zero-Shot
Transfer | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In Hierarchical Control, compositionality, abstraction, and task-transfer are
crucial for designing versatile algorithms which can solve a variety of
problems with maximal representational reuse. We propose a novel hierarchical
and compositional framework called Jump-Operator Dynamic Programming for
quickly computing solutions within a super-exponential space of sequential
sub-goal tasks with ordering constraints, while also providing a fast
linearly-solvable algorithm as an implementation. This approach involves
controlling over an ensemble of reusable goal-conditioned polices functioning
as temporally extended actions, and utilizes transition operators called
feasibility functions, which are used to summarize initial-to-final state
dynamics of the polices. Consequently, the added complexity of grounding a
high-level task space onto a larger ambient state-space can be mitigated by
optimizing in a lower-dimensional subspace defined by the grounding,
substantially improving the scalability of the algorithm while effecting
transferable solutions. We then identify classes of objective functions on this
subspace whose solutions are invariant to the grounding, resulting in optimal
zero-shot transfer.
| [
{
"version": "v1",
"created": "Mon, 6 Jul 2020 05:13:20 GMT"
}
]
| 1,594,080,000,000 | [
[
"Ringstrom",
"Thomas J.",
""
],
[
"Hasanbeig",
"Mohammadhosein",
""
],
[
"Abate",
"Alessandro",
""
]
]
|
2007.02742 | Vanessa Volz | Vanessa Volz and Boris Naujoks | Towards Game-Playing AI Benchmarks via Performance Reporting Standards | IEEE Conference on Games 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While games have been used extensively as milestones to evaluate game-playing
AI, there exists no standardised framework for reporting the obtained
observations. As a result, it remains difficult to draw general conclusions
about the strengths and weaknesses of different game-playing AI algorithms. In
this paper, we propose reporting guidelines for AI game-playing performance
that, if followed, provide information suitable for unbiased comparisons
between different AI approaches. The vision we describe is to build benchmarks
and competitions based on such guidelines in order to be able to draw more
general conclusions about the behaviour of different AI algorithms, as well as
the types of challenges different games pose.
| [
{
"version": "v1",
"created": "Mon, 6 Jul 2020 13:27:00 GMT"
}
]
| 1,594,080,000,000 | [
[
"Volz",
"Vanessa",
""
],
[
"Naujoks",
"Boris",
""
]
]
|
2007.02854 | Noor Akhmad Setiawan PhD | Noor Akhmad Setiawan, Paruvachi Ammasai Venkatachalam, Ahmad Fadzil M
Hani | Diagnosis of Coronary Artery Disease Using Artificial Intelligence Based
Decision Support System | null | Proceedings of the International Conference on Man Machine Systems
Batu Ferringhi Penang October 2009 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This research is about the development a fuzzy decision support system for
the diagnosis of coronary artery disease based on evidence. The coronary artery
disease data sets taken from University California Irvine (UCI) are used. The
knowledge base of fuzzy decision support system is taken by using rules
extraction method based on Rough Set Theory. The rules then are selected and
fuzzified based on information from discretization of numerical attributes.
Fuzzy rules weight is proposed using the information from support of extracted
rules. UCI heart disease data sets collected from U.S., Switzerland and
Hungary, data from Ipoh Specialist Hospital Malaysia are used to verify the
proposed system. The results show that the system is able to give the
percentage of coronary artery blocking better than cardiologists and
angiography. The results of the proposed system were verified and validated by
three expert cardiologists and are considered to be more efficient and useful.
| [
{
"version": "v1",
"created": "Mon, 6 Jul 2020 16:10:13 GMT"
}
]
| 1,594,080,000,000 | [
[
"Setiawan",
"Noor Akhmad",
""
],
[
"Venkatachalam",
"Paruvachi Ammasai",
""
],
[
"Hani",
"Ahmad Fadzil M",
""
]
]
|
2007.03102 | Ankur Deka | Ankur Deka and Katia Sycara | Natural Emergence of Heterogeneous Strategies in Artificially
Intelligent Competitive Teams | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi agent strategies in mixed cooperative-competitive environments can be
hard to craft by hand because each agent needs to coordinate with its teammates
while competing with its opponents. Learning based algorithms are appealing but
many scenarios require heterogeneous agent behavior for the team's success and
this increases the complexity of the learning algorithm. In this work, we
develop a competitive multi agent environment called FortAttack in which two
teams compete against each other. We corroborate that modeling agents with
Graph Neural Networks and training them with Reinforcement Learning leads to
the evolution of increasingly complex strategies for each team. We observe a
natural emergence of heterogeneous behavior amongst homogeneous agents when
such behavior can lead to the team's success. Such heterogeneous behavior from
homogeneous agents is appealing because any agent can replace the role of
another agent at test time. Finally, we propose ensemble training, in which we
utilize the evolved opponent strategies to train a single policy for friendly
agents.
| [
{
"version": "v1",
"created": "Mon, 6 Jul 2020 22:35:56 GMT"
}
]
| 1,594,166,400,000 | [
[
"Deka",
"Ankur",
""
],
[
"Sycara",
"Katia",
""
]
]
|
2007.03191 | Ke Ren | Hoda Bidkhori, John P Dickerson, Duncan C McElfresh, Ke Ren | Kidney Exchange with Inhomogeneous Edge Existence Uncertainty | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivated by kidney exchange, we study a stochastic cycle and chain packing
problem, where we aim to identify structures in a directed graph to maximize
the expectation of matched edge weights. All edges are subject to failure, and
the failures can have nonidentical probabilities. To the best of our knowledge,
the state-of-the-art approaches are only tractable when failure probabilities
are identical. We formulate a relevant non-convex optimization problem and
propose a tractable mixed-integer linear programming reformulation to solve it.
In addition, we propose a model that integrates both risks and the expected
utilities of the matching by incorporating conditional value at risk (CVaR)
into the objective function, providing a robust formulation for this problem.
Subsequently, we propose a sample-average-approximation (SAA) based approach to
solve this problem. We test our approaches on data from the United Network for
Organ Sharing (UNOS) and compare against state-of-the-art approaches. Our model
provides better performance with the same running time as a leading
deterministic approach (PICEF). Our CVaR extensions with an SAA-based method
improves the $\alpha \times 100\%$ ($0<\alpha\leqslant 1$) worst-case
performance substantially compared to existing models.
| [
{
"version": "v1",
"created": "Tue, 7 Jul 2020 04:08:39 GMT"
}
]
| 1,594,166,400,000 | [
[
"Bidkhori",
"Hoda",
""
],
[
"Dickerson",
"John P",
""
],
[
"McElfresh",
"Duncan C",
""
],
[
"Ren",
"Ke",
""
]
]
|
2007.03328 | Gianni De Fabritiis | Gabriele Libardi and Gianni De Fabritiis | Guided Exploration with Proximal Policy Optimization using a Single
Demonstration | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Solving sparse reward tasks through exploration is one of the major
challenges in deep reinforcement learning, especially in three-dimensional,
partially-observable environments. Critically, the algorithm proposed in this
article uses a single human demonstration to solve hard-exploration problems.
We train an agent on a combination of demonstrations and own experience to
solve problems with variable initial conditions. We adapt this idea and
integrate it with the proximal policy optimization (PPO). The agent is able to
increase its performance and to tackle harder problems by replaying its own
past trajectories prioritizing them based on the obtained reward and the
maximum value of the trajectory. We compare different variations of this
algorithm to behavioral cloning on a set of hard-exploration tasks in the
Animal-AI Olympics environment. To the best of our knowledge, learning a task
in a three-dimensional environment with comparable difficulty has never been
considered before using only one human demonstration.
| [
{
"version": "v1",
"created": "Tue, 7 Jul 2020 10:30:32 GMT"
},
{
"version": "v2",
"created": "Wed, 16 Jun 2021 21:38:35 GMT"
}
]
| 1,623,974,400,000 | [
[
"Libardi",
"Gabriele",
""
],
[
"De Fabritiis",
"Gianni",
""
]
]
|
2007.03581 | Wolfgang Dvo\v{r}\'ak | Wolfgang Dvo\v{r}\'ak and Atefeh Keshavarzi Zafarghandi and Stefan
Woltran | Expressiveness of SETAFs and Support-Free ADFs under 3-valued Semantics | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generalizing the attack structure in argumentation frameworks (AFs) has been
studied in different ways. Most prominently, the binary attack relation of Dung
frameworks has been extended to the notion of collective attacks. The resulting
formalism is often termed SETAFs. Another approach is provided via abstract
dialectical frameworks (ADFs), where acceptance conditions specify the relation
between arguments; restricting these conditions naturally allows for so-called
support-free ADFs. The aim of the paper is to shed light on the relation
between these two different approaches. To this end, we investigate and compare
the expressiveness of SETAFs and support-free ADFs under the lens of 3-valued
semantics. Our results show that it is only the presence of unsatisfiable
acceptance conditions in support-free ADFs that discriminate the two
approaches.
| [
{
"version": "v1",
"created": "Tue, 7 Jul 2020 16:03:23 GMT"
}
]
| 1,594,166,400,000 | [
[
"Dvořák",
"Wolfgang",
""
],
[
"Zafarghandi",
"Atefeh Keshavarzi",
""
],
[
"Woltran",
"Stefan",
""
]
]
|
2007.03727 | Maria In\^es Silva | Maria In\^es Silva, Roberto Henriques | TripMD: Driving patterns investigation via Motif Analysis | 14 pages, 11 figures, to be published in Expert Systems with
Applications | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Processing driving data and investigating driving behavior has been receiving
an increasing interest in the last decades, with applications ranging from car
insurance pricing to policy making. A common strategy to analyze driving
behavior is to study the maneuvers being performance by the driver. In this
paper, we propose TripMD, a system that extracts the most relevant driving
patterns from sensor recordings (such as acceleration) and provides a
visualization that allows for an easy investigation. Additionally, we test our
system using the UAH-DriveSet dataset, a publicly available naturalistic
driving dataset. We show that (1) our system can extract a rich number of
driving patterns from a single driver that are meaningful to understand driving
behaviors and (2) our system can be used to identify the driving behavior of an
unknown driver from a set of drivers whose behavior we know.
| [
{
"version": "v1",
"created": "Tue, 7 Jul 2020 18:34:31 GMT"
},
{
"version": "v2",
"created": "Fri, 4 Dec 2020 10:32:54 GMT"
},
{
"version": "v3",
"created": "Sun, 25 Apr 2021 12:03:59 GMT"
},
{
"version": "v4",
"created": "Mon, 5 Jul 2021 16:49:18 GMT"
}
]
| 1,625,529,600,000 | [
[
"Silva",
"Maria Inês",
""
],
[
"Henriques",
"Roberto",
""
]
]
|
2007.04221 | Srdjan Vesic | Leila Amgoud, Srdjan Vesic | Dung's semantics satisfy attack removal monotonicity | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that preferred, stable, complete, and grounded semantics satisfy
attack removal monotonicity. This means that if an attack from b to a is
removed, the status of a cannot worsen, e.g. if a was skeptically accepted, it
cannot become rejected.
| [
{
"version": "v1",
"created": "Wed, 8 Jul 2020 15:59:14 GMT"
}
]
| 1,594,252,800,000 | [
[
"Amgoud",
"Leila",
""
],
[
"Vesic",
"Srdjan",
""
]
]
|
2007.04477 | Nicholas Kluge Corr\^ea | Nicholas Kluge Corr\^ea and Nythamar de Oliveira | Good AI for the Present of Humanity Democratizing AI Governance | null | The AI Ethics Journal (2021) | 10.47289/AIEJ20210716-2 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | What do Cyberpunk and AI Ethics have to do with each other? Cyberpunk is a
sub-genre of science fiction that explores the post-human relationships between
human experience and technology. One similarity between AI Ethics and Cyberpunk
literature is that both seek to explore future social and ethical problems that
our technological advances may bring upon society. In recent years, an
increasing number of ethical matters involving AI have been pointed and
debated, and several ethical principles and guides have been suggested as
governance policies for the tech industry. However, would this be the role of
AI Ethics? To serve as a soft and ambiguous version of the law? We would like
to advocate in this article for a more Cyberpunk way of doing AI Ethics, with a
more democratic way of governance. In this study, we will seek to expose some
of the deficits of the underlying power structures of the AI industry, and
suggest that AI governance be subject to public opinion, so that good AI can
become good AI for all.
| [
{
"version": "v1",
"created": "Wed, 8 Jul 2020 23:50:28 GMT"
},
{
"version": "v10",
"created": "Sat, 26 Dec 2020 21:26:07 GMT"
},
{
"version": "v11",
"created": "Tue, 1 Jun 2021 18:31:11 GMT"
},
{
"version": "v12",
"created": "Sat, 17 Jul 2021 17:21:15 GMT"
},
{
"version": "v13",
"created": "Tue, 17 Aug 2021 01:27:49 GMT"
},
{
"version": "v2",
"created": "Sun, 12 Jul 2020 22:52:10 GMT"
},
{
"version": "v3",
"created": "Thu, 23 Jul 2020 22:40:36 GMT"
},
{
"version": "v4",
"created": "Fri, 7 Aug 2020 23:48:38 GMT"
},
{
"version": "v5",
"created": "Thu, 27 Aug 2020 17:26:32 GMT"
},
{
"version": "v6",
"created": "Sat, 5 Sep 2020 18:55:27 GMT"
},
{
"version": "v7",
"created": "Sun, 18 Oct 2020 04:11:19 GMT"
},
{
"version": "v8",
"created": "Sun, 25 Oct 2020 03:53:29 GMT"
},
{
"version": "v9",
"created": "Tue, 27 Oct 2020 02:42:33 GMT"
}
]
| 1,629,244,800,000 | [
[
"Corrêa",
"Nicholas Kluge",
""
],
[
"de Oliveira",
"Nythamar",
""
]
]
|
2007.04614 | Wei Li | Lei Zhang, Wei Bai, Shize Guo, Shiming Xia, Hongmei Li and Zhisong Pan | Weakness Analysis of Cyberspace Configuration Based on Reinforcement
Learning | 10 pages, 6 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we present a learning-based approach to analysis cyberspace
configuration. Unlike prior methods, our approach has the ability to learn from
past experience and improve over time. In particular, as we train over a
greater number of agents as attackers, our method becomes better at rapidly
finding attack paths for previously hidden paths, especially in multiple domain
cyberspace. To achieve these results, we pose finding attack paths as a
Reinforcement Learning (RL) problem and train an agent to find multiple domain
attack paths. To enable our RL policy to find more hidden attack paths, we
ground representation introduction an multiple domain action select module in
RL. By designing a simulated cyberspace experimental environment to verify our
method. Our objective is to find more hidden attack paths, to analysis the
weakness of cyberspace configuration. The experimental results show that our
method can find more hidden multiple domain attack paths than existing
baselines methods.
| [
{
"version": "v1",
"created": "Thu, 9 Jul 2020 07:53:35 GMT"
}
]
| 1,594,339,200,000 | [
[
"Zhang",
"Lei",
""
],
[
"Bai",
"Wei",
""
],
[
"Guo",
"Shize",
""
],
[
"Xia",
"Shiming",
""
],
[
"Li",
"Hongmei",
""
],
[
"Pan",
"Zhisong",
""
]
]
|
2007.04663 | Rushikesh Joshi | Charu Agarwal, Rushikesh K. Joshi | Automation Strategies for Unconstrained Crossword Puzzle Generation | 28 pages, 28 figures, category: cs, preprint | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An unconstrained crossword puzzle is a generalization of the constrained
crossword problem. In this problem, only the word vocabulary, and optionally
the grid dimensions are known. Hence, it not only requires the algorithm to
determine the word locations, but it also needs to come up with the grid
geometry. This paper discusses algorithmic strategies for automatic crossword
puzzle generation in such an unconstrained setting. The strategies proposed
cover the tasks of selection of words from a given vocabulary, selection of
grid sizes, grid resizing and adjustments, metrics for word fitting,
back-tracking techniques, and also clue generation. The strategies have been
formulated based on a study of the effect of word sequence permutation order on
grid fitting. An end-to-end algorithm that combines these strategies is
presented, and its performance is analyzed. The techniques have been found to
be successful in quickly producing well-packed puzzles of even large sizes.
Finally, a few example puzzles generated by our algorithm are also provided.
| [
{
"version": "v1",
"created": "Thu, 9 Jul 2020 09:45:03 GMT"
}
]
| 1,594,339,200,000 | [
[
"Agarwal",
"Charu",
""
],
[
"Joshi",
"Rushikesh K.",
""
]
]
|
2007.04862 | Lennart Bramlage | Lennart Bramlage and Aurelio Cortese | Attention or memory? Neurointerpretable agents in space and time | 8 pages, 6 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In neuroscience, attention has been shown to bidirectionally interact with
reinforcement learning (RL) processes. This interaction is thought to support
dimensionality reduction of task representations, restricting computations to
relevant features. However, it remains unclear whether these properties can
translate into real algorithmic advantages for artificial agents, especially in
dynamic environments. We design a model incorporating a self-attention
mechanism that implements task-state representations in semantic feature-space,
and test it on a battery of Atari games. To evaluate the agent's selective
properties, we add a large volume of task-irrelevant features to observations.
In line with neuroscience predictions, self-attention leads to increased
robustness to noise compared to benchmark models. Strikingly, this
self-attention mechanism is general enough, such that it can be naturally
extended to implement a transient working-memory, able to solve a partially
observable maze task. Lastly, we highlight the predictive quality of attended
stimuli. Because we use semantic observations, we can uncover not only which
features the agent elects to base decisions on, but also how it chooses to
compile more complex, relational features from simpler ones. These results
formally illustrate the benefits of attention in deep RL and provide evidence
for the interpretability of self-attention mechanisms.
| [
{
"version": "v1",
"created": "Thu, 9 Jul 2020 15:04:26 GMT"
},
{
"version": "v2",
"created": "Sun, 12 Jul 2020 15:32:16 GMT"
}
]
| 1,594,684,800,000 | [
[
"Bramlage",
"Lennart",
""
],
[
"Cortese",
"Aurelio",
""
]
]
|
2007.04908 | Rustam Rustam | Rustam and Koredianto Usman and Mudyawati Kamaruddin and Dina Chamidah
and Nopendri and Khaerudin Saleh and Yulinda Eliskar and Ismail Marzuki | Modified Possibilistic Fuzzy C-Means Algorithm for Clustering Incomplete
Data Sets | 13 pages, 13 figures, submitted to Acta Polytechnica as scientific
journal published by the Czech Technical University in Prague | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Possibilistic fuzzy c-means (PFCM) algorithm is a reliable algorithm has been
proposed to deal the weakness of two popular algorithms for clustering, fuzzy
c-means (FCM) and possibilistic c-means (PCM). PFCM algorithm deals with the
weaknesses of FCM in handling noise sensitivity and the weaknesses of PCM in
the case of coincidence clusters. However, the PFCM algorithm can be only
applied to cluster complete data sets. Therefore, in this study, we propose a
modification of the PFCM algorithm that can be applied to incomplete data sets
clustering. We modified the PFCM algorithm to OCSPFCM and NPSPFCM algorithms
and measured performance on three things: 1) accuracy percentage, 2) a number
of iterations to termination, and 3) centroid errors. Based on the results that
both algorithms have the potential for clustering incomplete data sets.
However, the performance of the NPSPFCM algorithm is better than the OCSPFCM
algorithm for clustering incomplete data sets.
| [
{
"version": "v1",
"created": "Thu, 9 Jul 2020 16:12:11 GMT"
},
{
"version": "v2",
"created": "Wed, 15 Jul 2020 23:37:09 GMT"
}
]
| 1,594,944,000,000 | [
[
"Rustam",
"",
""
],
[
"Usman",
"Koredianto",
""
],
[
"Kamaruddin",
"Mudyawati",
""
],
[
"Chamidah",
"Dina",
""
],
[
"Nopendri",
"",
""
],
[
"Saleh",
"Khaerudin",
""
],
[
"Eliskar",
"Yulinda",
""
],
[
"Marzuki",
"Ismail",
""
]
]
|
2007.04949 | Amal Nammouchi | Amal Nammouchi, Hakim Ghazzai, and Yehia Massoud | A Generative Graph Method to Solve the Travelling Salesman Problem | 5 pages, 2 figures, 2 tables, conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Travelling Salesman Problem (TSP) is a challenging graph task in
combinatorial optimization that requires reasoning about both local node
neighborhoods and global graph structure. In this paper, we propose to use the
novel Graph Learning Network (GLN), a generative approach, to approximately
solve the TSP. GLN model learns directly the pattern of TSP instances as
training dataset, encodes the graph properties, and merge the different node
embeddings to output node-to-node an optimal tour directly or via graph search
technique that validates the final tour. The preliminary results of the
proposed novel approach proves its applicability to this challenging problem
providing a low optimally gap with significant computation saving compared to
the optimal solution.
| [
{
"version": "v1",
"created": "Thu, 9 Jul 2020 17:23:55 GMT"
}
]
| 1,594,339,200,000 | [
[
"Nammouchi",
"Amal",
""
],
[
"Ghazzai",
"Hakim",
""
],
[
"Massoud",
"Yehia",
""
]
]
|
2007.05254 | Wu Qinghua | Yongliang Lu, Jin-Kao Hao, Qinghua Wu | Solving the Clustered Traveling Salesman Problem via TSP methods | 26 pages, 6 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Clustered Traveling Salesman Problem (CTSP) is a variant of the popular
Traveling Salesman Problem (TSP) arising from a number of real-life
applications. In this work, we explore a transformation approach that solves
the CTSP by converting it to the well-studied TSP. For this purpose, we first
investigate a technique to convert a CTSP instance to a TSP and then apply
powerful TSP solvers (including exact and heuristic solvers) to solve the
resulting TSP instance. We want to answer the following questions: How do
state-of-the-art TSP solvers perform on clustered instances converted from the
CTSP? Do state-of-the-art TSP solvers compete well with the best performing
methods specifically designed for the CTSP? For this purpose, we present
intensive computational experiments on various benchmark instances to draw
conclusions.
| [
{
"version": "v1",
"created": "Fri, 10 Jul 2020 08:56:06 GMT"
},
{
"version": "v2",
"created": "Tue, 15 Dec 2020 08:35:00 GMT"
},
{
"version": "v3",
"created": "Thu, 14 Apr 2022 11:34:37 GMT"
}
]
| 1,649,980,800,000 | [
[
"Lu",
"Yongliang",
""
],
[
"Hao",
"Jin-Kao",
""
],
[
"Wu",
"Qinghua",
""
]
]
|
2007.05284 | Guilherme Paulino-Passos | Guilherme Paulino-Passos, Francesca Toni | Cautious Monotonicity in Case-Based Reasoning with Abstract
Argumentation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, abstract argumentation-based models of case-based reasoning
($AA{\text -}CBR$ in short) have been proposed, originally inspired by the
legal domain, but also applicable as classifiers in different scenarios,
including image classification, sentiment analysis of text, and in predicting
the passage of bills in the UK Parliament. However, the formal properties of
$AA{\text -}CBR$ as a reasoning system remain largely unexplored. In this
paper, we focus on analysing the non-monotonicity properties of a regular
version of $AA{\text -}CBR$ (that we call $AA{\text -}CBR_{\succeq}$).
Specifically, we prove that $AA{\text -}CBR_{\succeq}$ is not cautiously
monotonic, a property frequently considered desirable in the literature of
non-monotonic reasoning. We then define a variation of $AA{\text
-}CBR_{\succeq}$ which is cautiously monotonic, and provide an algorithm for
obtaining it. Further, we prove that such variation is equivalent to using
$AA{\text -}CBR_{\succeq}$ with a restricted casebase consisting of all
"surprising" cases in the original casebase.
| [
{
"version": "v1",
"created": "Fri, 10 Jul 2020 10:08:30 GMT"
},
{
"version": "v2",
"created": "Mon, 13 Jul 2020 07:42:11 GMT"
}
]
| 1,594,684,800,000 | [
[
"Paulino-Passos",
"Guilherme",
""
],
[
"Toni",
"Francesca",
""
]
]
|
2007.05367 | Richard Evans | Richard Evans, Jose Hernandez-Orallo, Johannes Welbl, Pushmeet Kohli,
Marek Sergot | Evaluating the Apperception Engine | arXiv admin note: substantial text overlap with arXiv:1910.02227 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Apperception Engine is an unsupervised learning system. Given a sequence
of sensory inputs, it constructs a symbolic causal theory that both explains
the sensory sequence and also satisfies a set of unity conditions. The unity
conditions insist that the constituents of the theory - objects, properties,
and laws - must be integrated into a coherent whole. Once a theory has been
constructed, it can be applied to predict future sensor readings, retrodict
earlier readings, or impute missing readings.
In this paper, we evaluate the Apperception Engine in a diverse variety of
domains, including cellular automata, rhythms and simple nursery tunes,
multi-modal binding problems, occlusion tasks, and sequence induction
intelligence tests. In each domain, we test our engine's ability to predict
future sensor values, retrodict earlier sensor values, and impute missing
sensory data. The engine performs well in all these domains, significantly
outperforming neural net baselines and state of the art inductive logic
programming systems. These results are significant because neural nets
typically struggle to solve the binding problem (where information from
different modalities must somehow be combined together into different aspects
of one unified object) and fail to solve occlusion tasks (in which objects are
sometimes visible and sometimes obscured from view). We note in particular that
in the sequence induction intelligence tests, our system achieved human-level
performance. This is notable because our system is not a bespoke system
designed specifically to solve intelligence tests, but a general-purpose system
that was designed to make sense of any sensory sequence.
| [
{
"version": "v1",
"created": "Thu, 9 Jul 2020 11:54:05 GMT"
}
]
| 1,594,598,400,000 | [
[
"Evans",
"Richard",
""
],
[
"Hernandez-Orallo",
"Jose",
""
],
[
"Welbl",
"Johannes",
""
],
[
"Kohli",
"Pushmeet",
""
],
[
"Sergot",
"Marek",
""
]
]
|
2007.05411 | Koen Holtman | Koen Holtman | AGI Agent Safety by Iteratively Improving the Utility Function | Part 1 of this work is a preprint of a conference paper to appear in:
Proceedings of the 13th International Conference on Artificial General
Intelligence (AGI-20), Springer LNAI 12177 (2020). Part 2 has additional, new
research results that go beyond those in the conference paper | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While it is still unclear if agents with Artificial General Intelligence
(AGI) could ever be built, we can already use mathematical models to
investigate potential safety systems for these agents. We present an AGI safety
layer that creates a special dedicated input terminal to support the iterative
improvement of an AGI agent's utility function. The humans who switched on the
agent can use this terminal to close any loopholes that are discovered in the
utility function's encoding of agent goals and constraints, to direct the agent
towards new goals, or to force the agent to switch itself off. An AGI agent may
develop the emergent incentive to manipulate the above utility function
improvement process, for example by deceiving, restraining, or even attacking
the humans involved. The safety layer will partially, and sometimes fully,
suppress this dangerous incentive. The first part of this paper generalizes
earlier work on AGI emergency stop buttons. We aim to make the mathematical
methods used to construct the layer more accessible, by applying them to an MDP
model. We discuss two provable properties of the safety layer, and show ongoing
work in mapping it to a Causal Influence Diagram (CID). In the second part, we
develop full mathematical proofs, and show that the safety layer creates a type
of bureaucratic blindness. We then present the design of a learning agent, a
design that wraps the safety layer around either a known machine learning
system, or a potential future AGI-level learning system. The resulting agent
will satisfy the provable safety properties from the moment it is first
switched on. Finally, we show how this agent can be mapped from its model to a
real-life implementation. We review the methodological issues involved in this
step, and discuss how these are typically resolved.
| [
{
"version": "v1",
"created": "Fri, 10 Jul 2020 14:30:56 GMT"
}
]
| 1,594,598,400,000 | [
[
"Holtman",
"Koen",
""
]
]
|
2007.05423 | Mikael Zayenz Lagerkvist | Mikael Zayenz Lagerkvist and Magnus Rattfeldt | Half-checking propagators | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Propagators are central to the success of constraint programming, that is
contracting functions removing values proven not to be in any solution of a
given constraint. The literature contains numerous propagation algorithms, for
many different constraints, and common to all these propagation algorithms is
the notion of correctness: only values that appear in no solution to the
respective constraint may be removed. In this paper half-checking propagators
are introduced, for which the only requirements are that identified solutions
(by the propagators) are actual solutions (to the corresponding constraints),
and that the propagators are contracting. In particular, a half-checking
propagator may remove solutions resulting in an incomplete solving process, but
with the upside that (good) solutions may be found faster. Overall completeness
can be obtained by running half-checking propagators as one component in a
portfolio solving process. Half-checking propagators opens up a wider variety
of techniques to be used when designing propagation algorithms, compared to
what is currently available.
A formal model for half-checking propagators is introduced, together with a
detailed description of how to support such propagators in a constraint
programming system. Three general directions for creating half-checking
propagation algorithms are introduced, and used for designing new half-checking
propagators for the cost-circuit constraint as examples. The new propagators
are implemented in the Gecode system.
| [
{
"version": "v1",
"created": "Fri, 10 Jul 2020 14:54:57 GMT"
}
]
| 1,594,598,400,000 | [
[
"Lagerkvist",
"Mikael Zayenz",
""
],
[
"Rattfeldt",
"Magnus",
""
]
]
|
2007.05674 | Matthew Fontaine | Matthew C. Fontaine, Ruilin Liu, Ahmed Khalifa, Jignesh Modi, Julian
Togelius, Amy K. Hoover, Stefanos Nikolaidis | Illuminating Mario Scenes in the Latent Space of a Generative
Adversarial Network | Accepted to AAAI 2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generative adversarial networks (GANs) are quickly becoming a ubiquitous
approach to procedurally generating video game levels. While GAN generated
levels are stylistically similar to human-authored examples, human designers
often want to explore the generative design space of GANs to extract
interesting levels. However, human designers find latent vectors opaque and
would rather explore along dimensions the designer specifies, such as number of
enemies or obstacles. We propose using state-of-the-art quality diversity
algorithms designed to optimize continuous spaces, i.e. MAP-Elites with a
directional variation operator and Covariance Matrix Adaptation MAP-Elites, to
efficiently explore the latent space of a GAN to extract levels that vary
across a set of specified gameplay measures. In the benchmark domain of Super
Mario Bros, we demonstrate how designers may specify gameplay measures to our
system and extract high-quality (playable) levels with a diverse range of level
mechanics, while still maintaining stylistic similarity to human authored
examples. An online user study shows how the different mechanics of the
automatically generated levels affect subjective ratings of their perceived
difficulty and appearance.
| [
{
"version": "v1",
"created": "Sat, 11 Jul 2020 03:38:06 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Jul 2020 16:56:38 GMT"
},
{
"version": "v3",
"created": "Tue, 15 Dec 2020 23:55:41 GMT"
},
{
"version": "v4",
"created": "Mon, 21 Jun 2021 04:14:08 GMT"
}
]
| 1,624,320,000,000 | [
[
"Fontaine",
"Matthew C.",
""
],
[
"Liu",
"Ruilin",
""
],
[
"Khalifa",
"Ahmed",
""
],
[
"Modi",
"Jignesh",
""
],
[
"Togelius",
"Julian",
""
],
[
"Hoover",
"Amy K.",
""
],
[
"Nikolaidis",
"Stefanos",
""
]
]
|
2007.05961 | Elif Surer | Faruk Kucuksubasi and Elif Surer | Relational-Grid-World: A Novel Relational Reasoning Environment and An
Agent Model for Relational Information Extraction | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement learning (RL) agents are often designed specifically for a
particular problem and they generally have uninterpretable working processes.
Statistical methods-based agent algorithms can be improved in terms of
generalizability and interpretability using symbolic Artificial Intelligence
(AI) tools such as logic programming. In this study, we present a model-free RL
architecture that is supported with explicit relational representations of the
environmental objects. For the first time, we use the PrediNet network
architecture in a dynamic decision-making problem rather than image-based
tasks, and Multi-Head Dot-Product Attention Network (MHDPA) as a baseline for
performance comparisons. We tested two networks in two environments ---i.e.,
the baseline Box-World environment and our novel environment,
Relational-Grid-World (RGW). With the procedurally generated RGW environment,
which is complex in terms of visual perceptions and combinatorial selections,
it is easy to measure the relational representation performance of the RL
agents. The experiments were carried out using different configurations of the
environment so that the presented module and the environment were compared with
the baselines. We reached similar policy optimization performance results with
the PrediNet architecture and MHDPA; additionally, we achieved to extract the
propositional representation explicitly ---which makes the agent's statistical
policy logic more interpretable and tractable. This flexibility in the agent's
policy provides convenience for designing non-task-specific agent
architectures. The main contributions of this study are two-fold ---an RL agent
that can explicitly perform relational reasoning, and a new environment that
measures the relational reasoning capabilities of RL agents.
| [
{
"version": "v1",
"created": "Sun, 12 Jul 2020 11:30:48 GMT"
}
]
| 1,594,684,800,000 | [
[
"Kucuksubasi",
"Faruk",
""
],
[
"Surer",
"Elif",
""
]
]
|
2007.05971 | Liwen Li | Liwen Li, Zequn Wei, Jin-Kao Hao and Kun He | Probability Learning based Tabu Search for the Budgeted Maximum Coverage
Problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knapsack problems are classic models that can formulate a wide range of
applications. In this work, we deal with the Budgeted Maximum Coverage Problem
(BMCP), which is a generalized 0-1 knapsack problem. Given a set of items with
nonnegative weights and a set of elements with nonnegative profits, where each
item is composed of a subset of elements, BMCP aims to pack a subset of items
in a capacity-constrained knapsack such that the total weight of the selected
items does not exceed the knapsack capacity, and the total profit of the
associated elements is maximized. Note that each element is counted once even
if it is covered multiple times. BMCP is closely related to the Set-Union
Knapsack Problem (SUKP) that is well studied in recent years. As the
counterpart problem of SUKP, however, BMCP was introduced early in 1999 but
since then it has been rarely studied, especially there is no practical
algorithm proposed. By combining the reinforcement learning technique to the
local search procedure, we propose a probability learning based tabu search
(PLTS) algorithm for addressing this NP-hard problem. The proposed algorithm
iterates through two distinct phases, namely a tabu search phase and a
probability learning based perturbation phase. As there is no benchmark
instances proposed in the literature, we generate 30 benchmark instances with
varied properties. Experimental results demonstrate that our PLTS algorithm
significantly outperforms the general CPLEX solver for solving the challenging
BMCP in terms of the solution quality.
| [
{
"version": "v1",
"created": "Sun, 12 Jul 2020 12:11:59 GMT"
}
]
| 1,594,684,800,000 | [
[
"Li",
"Liwen",
""
],
[
"Wei",
"Zequn",
""
],
[
"Hao",
"Jin-Kao",
""
],
[
"He",
"Kun",
""
]
]
|
2007.06108 | Matthew Guzdial | Matthew Guzdial, Devi Acharya, Max Kreminski, Michael Cook, Mirjam
Eladhari, Antonios Liapis and Anne Sullivan | Tabletop Roleplaying Games as Procedural Content Generators | 9 pages, 2 figures, FDG Workshop on Procedural Content Generation
2020 | FDG Workshop on Procedural Content Generation 2020 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tabletop roleplaying games (TTRPGs) and procedural content generators can
both be understood as systems of rules for producing content. In this paper, we
argue that TTRPG design can usefully be viewed as procedural content generator
design. We present several case studies linking key concepts from PCG research
-- including possibility spaces, expressive range analysis, and generative
pipelines -- to key concepts in TTRPG design. We then discuss the implications
of these relationships and suggest directions for future work uniting research
in TTRPGs and PCG.
| [
{
"version": "v1",
"created": "Sun, 12 Jul 2020 22:05:17 GMT"
},
{
"version": "v2",
"created": "Wed, 15 Jul 2020 17:37:55 GMT"
}
]
| 1,594,857,600,000 | [
[
"Guzdial",
"Matthew",
""
],
[
"Acharya",
"Devi",
""
],
[
"Kreminski",
"Max",
""
],
[
"Cook",
"Michael",
""
],
[
"Eladhari",
"Mirjam",
""
],
[
"Liapis",
"Antonios",
""
],
[
"Sullivan",
"Anne",
""
]
]
|
2007.06282 | Martin Cooper | Martin C. Cooper | Strengthening neighbourhood substitution | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Domain reduction is an essential tool for solving the constraint satisfaction
problem (CSP). In the binary CSP, neighbourhood substitution consists in
eliminating a value if there exists another value which can be substituted for
it in each constraint. We show that the notion of neighbourhood substitution
can be strengthened in two distinct ways without increasing time complexity. We
also show the theoretical result that, unlike neighbourhood substitution,
finding an optimal sequence of these new operations is NP-hard.
| [
{
"version": "v1",
"created": "Mon, 13 Jul 2020 10:06:20 GMT"
}
]
| 1,594,684,800,000 | [
[
"Cooper",
"Martin C.",
""
]
]
|
2007.06850 | Natalia Criado | Jordi Ganzer, Natalia Criado, Maite Lopez-Sanchez, Simon Parsons, Juan
A. Rodriguez-Aguilar | A model to support collective reasoning: Formalization, analysis and
computational assessment | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inspired by e-participation systems, in this paper we propose a new model to
represent human debates and methods to obtain collective conclusions from them.
This model overcomes drawbacks of existing approaches by allowing users to
introduce new pieces of information into the discussion, to relate them to
existing pieces, and also to express their opinion on the pieces proposed by
other users. In addition, our model does not assume that users' opinions are
rational in order to extract information from it, an assumption that
significantly limits current approaches. Instead, we define a weaker notion of
rationality that characterises coherent opinions, and we consider different
scenarios based on the coherence of individual opinions and the level of
consensus that users have on the debate structure. Considering these two
factors, we analyse the outcomes of different opinion aggregation functions
that compute a collective decision based on the individual opinions and the
debate structure. In particular, we demonstrate that aggregated opinions can be
coherent even if there is a lack of consensus and individual opinions are not
coherent. We conclude our analysis with a computational evaluation
demonstrating that collective opinions can be computed efficiently for
real-sized debates.
| [
{
"version": "v1",
"created": "Tue, 14 Jul 2020 06:55:32 GMT"
}
]
| 1,594,771,200,000 | [
[
"Ganzer",
"Jordi",
""
],
[
"Criado",
"Natalia",
""
],
[
"Lopez-Sanchez",
"Maite",
""
],
[
"Parsons",
"Simon",
""
],
[
"Rodriguez-Aguilar",
"Juan A.",
""
]
]
|
2007.07220 | Nicolas A. Barriga | Gabriel K. Sepulveda, Felipe Besoain, and Nicolas A. Barriga | Exploring Dynamic Difficulty Adjustment in Videogames | null | null | 10.1109/CHILECON47746.2019.8988068 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Videogames are nowadays one of the biggest entertainment industries in the
world. Being part of this industry means competing against lots of other
companies and developers, thus, making fanbases of vital importance. They are a
group of clients that constantly support your company because your video games
are fun. Videogames are most entertaining when the difficulty level is a good
match for the player's skill, increasing the player engagement. However, not
all players are equally proficient, so some kind of difficulty selection is
required. In this paper, we will present Dynamic Difficulty Adjustment (DDA), a
recently arising research topic, which aims to develop an automated difficulty
selection mechanism that keeps the player engaged and properly challenged,
neither bored nor overwhelmed. We will present some recent research addressing
this issue, as well as an overview of how to implement it. Satisfactorily
solving the DDA problem directly affects the player's experience when playing
the game, making it of high interest to any game developer, from independent
ones, to 100 billion dollar businesses, because of the potential impacts in
player retention and monetization.
| [
{
"version": "v1",
"created": "Mon, 6 Jul 2020 15:05:20 GMT"
}
]
| 1,594,771,200,000 | [
[
"Sepulveda",
"Gabriel K.",
""
],
[
"Besoain",
"Felipe",
""
],
[
"Barriga",
"Nicolas A.",
""
]
]
|
2007.07549 | Jens Brunk | Jens Brunk, Matthias Stierle, Leon Papke, Kate Revoredo, Martin
Matzner, J\"org Becker | Cause vs. Effect in Context-Sensitive Prediction of Business Process
Instances | null | null | 10.1016/j.is.2020.101635 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predicting undesirable events during the execution of a business process
instance provides the process participants with an opportunity to intervene and
keep the process aligned with its goals. Few approaches for tackling this
challenge consider a multi-perspective view, where the flow perspective of the
process is combined with its surrounding context. Given the many sources of
data in today's world, context can vary widely and have various meanings. This
paper addresses the issue of context being cause or effect of the next event
and its impact on next event prediction. We leverage previous work on
probabilistic models to develop a Dynamic Bayesian Network technique.
Probabilistic models are considered comprehensible and they allow the end-user
and his or her understanding of the domain to be involved in the prediction.
Our technique models context attributes that have either a cause or effect
relationship towards the event. We evaluate our technique with two real-life
data sets and benchmark it with other techniques from the field of predictive
process monitoring. The results show that our solution achieves superior
prediction results if context information is correctly introduced into the
model.
| [
{
"version": "v1",
"created": "Wed, 15 Jul 2020 08:58:15 GMT"
},
{
"version": "v2",
"created": "Mon, 21 Sep 2020 10:17:28 GMT"
}
]
| 1,600,732,800,000 | [
[
"Brunk",
"Jens",
""
],
[
"Stierle",
"Matthias",
""
],
[
"Papke",
"Leon",
""
],
[
"Revoredo",
"Kate",
""
],
[
"Matzner",
"Martin",
""
],
[
"Becker",
"Jörg",
""
]
]
|
2007.07711 | Quentin Cohen-Solal | Quentin Cohen-Solal | Tractable Fragments of Temporal Sequences of Topological Information | null | null | 10.1007/978-3-030-58475-7_7 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we focus on qualitative temporal sequences of topological
information. We firstly consider the context of topological temporal sequences
of length greater than 3 describing the evolution of regions at consecutive
time points. We show that there is no Cartesian subclass containing all the
basic relations and the universal relation for which the algebraic closure
decides satisfiability. However, we identify some tractable subclasses, by
giving up the relations containing the non-tangential proper part relation and
not containing the tangential proper part relation. We then formalize an
alternative semantics for temporal sequences. We place ourselves in the context
of the topological temporal sequences describing the evolution of regions on a
partition of time (i.e. an alternation of instants and intervals). In this
context, we identify large tractable fragments.
| [
{
"version": "v1",
"created": "Wed, 15 Jul 2020 14:33:17 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jan 2021 14:42:01 GMT"
}
]
| 1,611,532,800,000 | [
[
"Cohen-Solal",
"Quentin",
""
]
]
|
2007.09206 | Daniel Garijo | Daniel Garijo and Maximiliano Osorio | OBA: An Ontology-Based Framework for Creating REST APIs for Knowledge
Graphs | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In recent years, Semantic Web technologies have been increasingly adopted by
researchers, industry and public institutions to describe and link data on the
Web, create web annotations and consume large knowledge graphs like Wikidata
and DBPedia. However, there is still a knowledge gap between ontology
engineers, who design, populate and create knowledge graphs; and web
developers, who need to understand, access and query these knowledge graphs but
are not familiar with ontologies, RDF or SPARQL. In this paper we describe the
Ontology-Based APIs framework (OBA), our approach to automatically create REST
APIs from ontologies while following RESTful API best practices. Given an
ontology (or ontology network) OBA uses standard technologies familiar to web
developers (OpenAPI Specification, JSON) and combines them with W3C standards
(OWL, JSON-LD frames and SPARQL) to create maintainable APIs with
documentation, units tests, automated validation of resources and clients (in
Python, Javascript, etc.) for non Semantic Web experts to access the contents
of a target knowledge graph. We showcase OBA with three examples that
illustrate the capabilities of the framework for different ontologies.
| [
{
"version": "v1",
"created": "Fri, 17 Jul 2020 19:46:18 GMT"
}
]
| 1,595,289,600,000 | [
[
"Garijo",
"Daniel",
""
],
[
"Osorio",
"Maximiliano",
""
]
]
|
2007.09288 | Han Lei | Zhiyong Yu, Lei Han, Chao Chen, Wenzhong Guo, Zhiwen Yu | Object Tracking by Least Spatiotemporal Searches | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tracking a car or a person in a city is crucial for urban safety management.
How can we complete the task with minimal number of spatiotemporal searches
from massive camera records? This paper proposes a strategy named IHMs
(Intermediate Searching at Heuristic Moments): each step we figure out which
moment is the best to search according to a heuristic indicator, then at that
moment search locations one by one in descending order of predicted appearing
probabilities, until a search hits; iterate this step until we get the object's
current location. Five searching strategies are compared in experiments, and
IHMs is validated to be most efficient, which can save up to 1/3 total costs.
This result provides an evidence that "searching at intermediate moments can
save cost".
| [
{
"version": "v1",
"created": "Sat, 18 Jul 2020 00:17:55 GMT"
},
{
"version": "v2",
"created": "Fri, 12 Mar 2021 07:25:27 GMT"
}
]
| 1,615,766,400,000 | [
[
"Yu",
"Zhiyong",
""
],
[
"Han",
"Lei",
""
],
[
"Chen",
"Chao",
""
],
[
"Guo",
"Wenzhong",
""
],
[
"Yu",
"Zhiwen",
""
]
]
|
2007.09300 | Deokgun Park | SM Mazharul Islam, Md Ashaduzzaman Rubel Mondol, Aishwarya Pothula,
Deokgun Park | An Open-World Simulated Environment for Developmental Robotics | Presented at Workshop on Learning in Artificial Open Worlds held with
ICML 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As the current trend of artificial intelligence is shifting towards
self-supervised learning, conventional norms such as highly curated
domain-specific data, application-specific learning models, extrinsic reward
based learning policies etc. might not provide with the suitable ground for
such developments. In this paper, we introduce SEDRo, a Simulated Environment
for Developmental Robotics which allows a learning agent to have similar
experiences that a human infant goes through from the fetus stage up to 12
months. A series of simulated tests based on developmental psychology will be
used to evaluate the progress of a learning model.
| [
{
"version": "v1",
"created": "Sat, 18 Jul 2020 01:16:13 GMT"
}
]
| 1,595,289,600,000 | [
[
"Islam",
"SM Mazharul",
""
],
[
"Mondol",
"Md Ashaduzzaman Rubel",
""
],
[
"Pothula",
"Aishwarya",
""
],
[
"Park",
"Deokgun",
""
]
]
|
2007.09448 | Alberto Santamaria-Pang | Alberto Santamaria-Pang, James Kubricht, Aritra Chowdhury, Chitresh
Bhushan, Peter Tu | Towards Emergent Language Symbolic Semantic Segmentation and Model
Interpretability | Accepted to Medical Image Computing and Computer Assisted
Intervention (MICCAI) 2020, 9 pages, 3 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advances in methods focused on the grounding problem have resulted in
techniques that can be used to construct a symbolic language associated with a
specific domain. Inspired by how humans communicate complex ideas through
language, we developed a generalized Symbolic Semantic ($\text{S}^2$) framework
for interpretable segmentation. Unlike adversarial models (e.g., GANs), we
explicitly model cooperation between two agents, a Sender and a Receiver, that
must cooperate to achieve a common goal. The Sender receives information from a
high layer of a segmentation network and generates a symbolic sentence derived
from a categorical distribution. The Receiver obtains the symbolic sentences
and co-generates the segmentation mask. In order for the model to converge, the
Sender and Receiver must learn to communicate using a private language. We
apply our architecture to segment tumors in the TCGA dataset. A UNet-like
architecture is used to generate input to the Sender network which produces a
symbolic sentence, and a Receiver network co-generates the segmentation mask
based on the sentence. Our Segmentation framework achieved similar or better
performance compared with state-of-the-art segmentation methods. In addition,
our results suggest direct interpretation of the symbolic sentences to
discriminate between normal and tumor tissue, tumor morphology, and other image
characteristics.
| [
{
"version": "v1",
"created": "Sat, 18 Jul 2020 15:06:12 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Aug 2020 19:12:20 GMT"
}
]
| 1,596,672,000,000 | [
[
"Santamaria-Pang",
"Alberto",
""
],
[
"Kubricht",
"James",
""
],
[
"Chowdhury",
"Aritra",
""
],
[
"Bhushan",
"Chitresh",
""
],
[
"Tu",
"Peter",
""
]
]
|
2007.09540 | Arnaud Fickinger | Arnaud Fickinger, Simon Zhuang, Dylan Hadfield-Menell, Stuart Russell | Multi-Principal Assistance Games | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Assistance games (also known as cooperative inverse reinforcement learning
games) have been proposed as a model for beneficial AI, wherein a robotic agent
must act on behalf of a human principal but is initially uncertain about the
humans payoff function. This paper studies multi-principal assistance games,
which cover the more general case in which the robot acts on behalf of N humans
who may have widely differing payoffs. Impossibility theorems in social choice
theory and voting theory can be applied to such games, suggesting that
strategic behavior by the human principals may complicate the robots task in
learning their payoffs. We analyze in particular a bandit apprentice game in
which the humans act first to demonstrate their individual preferences for the
arms and then the robot acts to maximize the sum of human payoffs. We explore
the extent to which the cost of choosing suboptimal arms reduces the incentive
to mislead, a form of natural mechanism design. In this context we propose a
social choice method that uses shared control of a system to combine preference
inference with social welfare optimization.
| [
{
"version": "v1",
"created": "Sun, 19 Jul 2020 00:23:25 GMT"
}
]
| 1,595,289,600,000 | [
[
"Fickinger",
"Arnaud",
""
],
[
"Zhuang",
"Simon",
""
],
[
"Hadfield-Menell",
"Dylan",
""
],
[
"Russell",
"Stuart",
""
]
]
|
2007.09563 | Somaiyeh MahmoudZadeh | Somaiyeh MahmoudZadeh, David MW Powers, Reza Bairam Zadeh | Autonomy and Unmanned Vehicles Augmented Reactive Mission-Motion
Planning Architecture for Autonomous Vehicles | null | Book: Springer Nature (2019), Cognitive Science and Technology,
ISBN 978-981-13-2245-7, Series ISSN: 2195-3988. 2019 | 10.1007/978-981-13-2245-7 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Advances in hardware technology have facilitated more integration of
sophisticated software toward augmenting the development of Unmanned Vehicles
(UVs) and mitigating constraints for onboard intelligence. As a result, UVs can
operate in complex missions where continuous trans-formation in environmental
condition calls for a higher level of situational responsiveness and autonomous
decision making. This book is a research monograph that aims to provide a
comprehensive survey of UVs autonomy and its related properties in internal and
external situation awareness to-ward robust mission planning in severe
conditions. An advance level of intelligence is essential to minimize the
reliance on the human supervisor, which is a main concept of autonomy. A
self-controlled system needs a robust mission management strategy to push the
boundaries towards autonomous structures, and the UV should be aware of its
internal state and capabilities to assess whether current mission goal is
achievable or find an alternative solution. In this book, the AUVs will become
the major case study thread but other cases/types of vehicle will also be
considered. In-deed the research monograph, the review chapters and the new
approaches we have developed would be appropriate for use as a reference in
upper years or postgraduate degrees for its coverage of literature and
algorithms relating to Robot/Vehicle planning, tasking, routing, and trust.
| [
{
"version": "v1",
"created": "Sun, 19 Jul 2020 02:34:48 GMT"
}
]
| 1,595,289,600,000 | [
[
"MahmoudZadeh",
"Somaiyeh",
""
],
[
"Powers",
"David MW",
""
],
[
"Zadeh",
"Reza Bairam",
""
]
]
|
2007.10018 | Mohit Kumar | Teodora Popordanoska, Mohit Kumar, and Stefano Teso | Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning | Accepted at TAILOR workshop at ECAI 2020, the 24th European
Conference on Artificial Intelligence | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent work has demonstrated the promise of combining local explanations with
active learning for understanding and supervising black-box models. Here we
show that, under specific conditions, these algorithms may misrepresent the
quality of the model being learned. The reason is that the machine illustrates
its beliefs by predicting and explaining the labels of the query instances: if
the machine is unaware of its own mistakes, it may end up choosing queries on
which it performs artificially well. This biases the "narrative" presented by
the machine to the user.We address this narrative bias by introducing
explanatory guided learning, a novel interactive learning strategy in which: i)
the supervisor is in charge of choosing the query instances, while ii) the
machine uses global explanations to illustrate its overall behavior and to
guide the supervisor toward choosing challenging, informative instances. This
strategy retains the key advantages of explanatory interaction while avoiding
narrative bias and compares favorably to active learning in terms of sample
complexity. An initial empirical evaluation with a clustering-based prototype
highlights the promise of our approach.
| [
{
"version": "v1",
"created": "Mon, 20 Jul 2020 11:51:31 GMT"
}
]
| 1,595,289,600,000 | [
[
"Popordanoska",
"Teodora",
""
],
[
"Kumar",
"Mohit",
""
],
[
"Teso",
"Stefano",
""
]
]
|
2007.10087 | Bingqing Yu | Jacopo Tagliabue and Bingqing Yu | Shopping in the Multiverse: A Counterfactual Approach to In-Session
Attribution | accepted at 2020 SIGIR Workshop On eCommerce | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We tackle the challenge of in-session attribution for on-site search engines
in eCommerce. We phrase the problem as a causal counterfactual inference, and
contrast the approach with rule-based systems from industry settings and
prediction models from the multi-touch attribution literature. We approach
counterfactuals in analogy with treatments in formal semantics, explicitly
modeling possible outcomes through alternative shopper timelines; in
particular, we propose to learn a generative browsing model over a target shop,
leveraging the latent space induced by prod2vec embeddings; we show how natural
language queries can be effectively represented in the same space and how
"search intervention" can be performed to assess causal contribution. Finally,
we validate the methodology on a synthetic dataset, mimicking important
patterns emerged in customer interviews and qualitative analysis, and we
present preliminary findings on an industry dataset from a partnering shop.
| [
{
"version": "v1",
"created": "Mon, 20 Jul 2020 13:32:02 GMT"
}
]
| 1,595,289,600,000 | [
[
"Tagliabue",
"Jacopo",
""
],
[
"Yu",
"Bingqing",
""
]
]
|
2007.10151 | Sabah Al-Fedaghi Dr. | Sabah Al-Fedaghi | Conceptual Modeling of Time for Computational Ontologies | 14 pages, 27 figures | IJCSNS International Journal of Computer Science and Network
Security, VOL.20 No.6, June 2020 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To provide a foundation for conceptual modeling, ontologies have been
introduced to specify the entities, the existences of which are acknowledged in
the model. Ontologies are essential components as mechanisms to model a portion
of reality in software engineering. In this context, a model refers to a
description of objects and processes that populate a system. Developing such a
description constrains and directs the design, development, and use of the
corresponding system, thus avoiding such difficulties as conflicts and lack of
a common understanding. In this cross-area research between modeling and
ontology, there has been a growing interest in the development and use of
domain ontologies (e.g., Resource Description Framework, Ontology Web
Language). This paper contributes to the establishment of a broad ontological
foundation for conceptual modeling in a specific domain through proposing a
workable ontology (abbreviated as TM). A TM is a one-category ontology called a
thimac (things/machines) that is used to elaborate the design and analysis of
ontological presumptions. The focus of the study is on such notions as change,
event, and time. Several current ontological difficulties are reviewed and
remodeled in the TM. TM modeling is also contrasted with time representation in
SysML. The results demonstrate that a TM is a useful tool for addressing these
ontological problems.
| [
{
"version": "v1",
"created": "Thu, 16 Jul 2020 20:11:18 GMT"
}
]
| 1,595,289,600,000 | [
[
"Al-Fedaghi",
"Sabah",
""
]
]
|
2007.11038 | Yosvany Medina Carb\'o | Ing. Yosvany Medina Carb\'o, MSc. Iracely Milagros Santana Ges, Lic.
Saily Leo Gonz\'alez | Sistema experto para el diagn\'ostico de enfermedades y plagas en los
cultivos del arroz, tabaco, tomate, pimiento, ma\'iz, pepino y frijol | in Spanish | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Agricultural production has become a complex business that requires the
accumulation and integration of knowledge, in addition to information from many
different sources. To remain competitive, the modern farmer often relies on
agricultural specialists and advisors who provide them with information for
decision making in their crops. But unfortunately, the help of the agricultural
specialist is not always available when the farmer needs it. To alleviate this
problem, expert systems have become a powerful instrument that has great
potential within agriculture. This paper presents an Expert System for the
diagnosis of diseases and pests in rice, tobacco, tomato, pepper, corn,
cucumber and bean crops. For the development of this Expert System, SWI-Prolog
was used to create the knowledge base, so it works with predicates and allows
the system to be based on production rules. This system allows a fast and
reliable diagnosis of pests and diseases that affect these crops.
| [
{
"version": "v1",
"created": "Tue, 21 Jul 2020 18:39:37 GMT"
}
]
| 1,595,462,400,000 | [
[
"Carbó",
"Ing. Yosvany Medina",
""
],
[
"Ges",
"MSc. Iracely Milagros Santana",
""
],
[
"González",
"Lic. Saily Leo",
""
]
]
|
2007.12586 | Nicolas A. Barriga | Ignacio Gajardo, Felipe Besoain, and Nicolas A. Barriga | Introduction to Behavior Algorithms for Fighting Games | in Spanish | null | 10.1109/CHILECON47746.2019.8988008 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The quality of opponent Artificial Intelligence (AI) in fighting videogames
is crucial. Some other game genres can rely on their story or visuals, but
fighting games are all about the adversarial experience. In this paper, we will
introduce standard behavior algorithms in videogames, such as Finite-State
Machines and Behavior Trees, as well as more recent developments, such as
Monte-Carlo Tree Search. We will also discuss the existing and potential
combinations of these algorithms, and how they might be used in fighting games.
Since we are at the financial peak of fighting games, both for casual players
and in tournaments, it is important to build and expand on fighting game AI, as
it is one of the pillars of this growing market.
| [
{
"version": "v1",
"created": "Mon, 6 Jul 2020 14:52:20 GMT"
}
]
| 1,595,808,000,000 | [
[
"Gajardo",
"Ignacio",
""
],
[
"Besoain",
"Felipe",
""
],
[
"Barriga",
"Nicolas A.",
""
]
]
|
2007.12904 | Zehong Cao Dr. | Zehong Cao, KaiChiu Wong, Chin-Teng Lin | Weak Human Preference Supervision For Deep Reinforcement Learning | Submitting to IEEE Transactions on Neural Networks and Learning
Systems | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The current reward learning from human preferences could be used to resolve
complex reinforcement learning (RL) tasks without access to a reward function
by defining a single fixed preference between pairs of trajectory segments.
However, the judgement of preferences between trajectories is not dynamic and
still requires human input over thousands of iterations. In this study, we
proposed a weak human preference supervision framework, for which we developed
a human preference scaling model that naturally reflects the human perception
of the degree of weak choices between trajectories and established a
human-demonstration estimator via supervised learning to generate the predicted
preferences for reducing the number of human inputs. The proposed weak human
preference supervision framework can effectively solve complex RL tasks and
achieve higher cumulative rewards in simulated robot locomotion -- MuJoCo games
-- relative to the single fixed human preferences. Furthermore, our established
human-demonstration estimator requires human feedback only for less than 0.01\%
of the agent's interactions with the environment and significantly reduces the
cost of human inputs by up to 30\% compared with the existing approaches. To
present the flexibility of our approach, we released a video
(https://youtu.be/jQPe1OILT0M) showing comparisons of the behaviours of agents
trained on different types of human input. We believe that our naturally
inspired human preferences with weakly supervised learning are beneficial for
precise reward learning and can be applied to state-of-the-art RL systems, such
as human-autonomy teaming systems.
| [
{
"version": "v1",
"created": "Sat, 25 Jul 2020 10:37:15 GMT"
},
{
"version": "v2",
"created": "Sat, 26 Dec 2020 02:02:31 GMT"
}
]
| 1,609,200,000,000 | [
[
"Cao",
"Zehong",
""
],
[
"Wong",
"KaiChiu",
""
],
[
"Lin",
"Chin-Teng",
""
]
]
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.