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1906.02138 | Wendelin B\"ohmer | Wendelin B\"ohmer, Tabish Rashid, Shimon Whiteson | Exploration with Unreliable Intrinsic Reward in Multi-Agent
Reinforcement Learning | Accepted to the 2nd Exploration in Reinforcement Learning Workshop at
the International Conference on Machine Learning 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates the use of intrinsic reward to guide exploration in
multi-agent reinforcement learning. We discuss the challenges in applying
intrinsic reward to multiple collaborative agents and demonstrate how
unreliable reward can prevent decentralized agents from learning the optimal
policy. We address this problem with a novel framework, Independent
Centrally-assisted Q-learning (ICQL), in which decentralized agents share
control and an experience replay buffer with a centralized agent. Only the
centralized agent is intrinsically rewarded, but the decentralized agents still
benefit from improved exploration, without the distraction of unreliable
incentives.
| [
{
"version": "v1",
"created": "Wed, 5 Jun 2019 16:56:54 GMT"
}
] | 1,559,779,200,000 | [
[
"Böhmer",
"Wendelin",
""
],
[
"Rashid",
"Tabish",
""
],
[
"Whiteson",
"Shimon",
""
]
] |
1906.02155 | Alessandro Saffiotti | Oscar Th\"orn, Peter F\"ogel, Peter Knudsen, Luis de Miranda and
Alessandro Saffiotti | Anticipation in collaborative music performance using fuzzy systems: a
case study | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In order to collaborate and co-create with humans, an AI system must be
capable of both reactive and anticipatory behavior. We present a case study of
such a system in the domain of musical improvisation. We consider a duo
consisting of a human pianist accompained by an off-the-shelf virtual drummer,
and we design an AI system to control the perfomance parameters of the drummer
(e.g., patterns, intensity, or complexity) as a function of what the human
pianist is playing. The AI system utilizes a model elicited from the musicians
and encoded through fuzzy logic. This paper outlines the methodology, design,
and development process of this system. An evaluation in public concerts is
upcoming. This case study is seen as a step in the broader investigation of
anticipation and creative processes in mixed human-robot, or "anthrobotic"
systems.
| [
{
"version": "v1",
"created": "Wed, 5 Jun 2019 17:26:50 GMT"
}
] | 1,559,779,200,000 | [
[
"Thörn",
"Oscar",
""
],
[
"Fögel",
"Peter",
""
],
[
"Knudsen",
"Peter",
""
],
[
"de Miranda",
"Luis",
""
],
[
"Saffiotti",
"Alessandro",
""
]
] |
1906.02578 | Peilin Chen | Peilin Chen, Hai Wan, Shaowei Cai, Weilin Luo, Jia Li | Combining Reinforcement Learning and Configuration Checking for Maximum
k-plex Problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Maximum k-plex Problem is an important combinatorial optimization problem
with increasingly wide applications. Due to its exponential time complexity,
many heuristic methods have been proposed which can return a good-quality
solution in a reasonable time. However, most of the heuristic algorithms are
memoryless and unable to utilize the experience during the search. Inspired by
the multi-armed bandit (MAB) problem in reinforcement learning (RL), we propose
a novel perturbation mechanism named BLP, which can learn online to select a
good vertex for perturbation when getting stuck in local optima. To our best of
knowledge, this is the first attempt to combine local search with RL for the
maximum $ k $-plex problem.
Besides, we also propose a novel strategy, named Dynamic-threshold
Configuration Checking (DTCC), which extends the original Configuration
Checking (CC) strategy from two aspects.
Based on the BLP and DTCC, we develop a local search algorithm named BDCC and
improve it by a hyperheuristic strategy. The experimental result shows that our
algorithms dominate on the standard DIMACS and BHOSLIB benchmarks and achieve
state-of-the-art performance on massive graphs.
| [
{
"version": "v1",
"created": "Thu, 6 Jun 2019 13:35:49 GMT"
}
] | 1,559,865,600,000 | [
[
"Chen",
"Peilin",
""
],
[
"Wan",
"Hai",
""
],
[
"Cai",
"Shaowei",
""
],
[
"Luo",
"Weilin",
""
],
[
"Li",
"Jia",
""
]
] |
1906.02912 | Nathan Sturtevant | Nathan Sturtevant and Malte Helmert | Exponential-Binary State-Space Search | This paper and another independent IJCAI 2019 submission have been
merged into a single paper that subsumes both of them (Helmert et. al.,
2019). This paper is placed here only for historical context. Please only
cite the subsuming paper | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Iterative deepening search is used in applications where the best cost bound
for state-space search is unknown. The iterative deepening process is used to
avoid overshooting the appropriate cost bound and doing too much work as a
result. However, iterative deepening search also does too much work if the cost
bound grows too slowly. This paper proposes a new framework for iterative
deepening search called exponential-binary state-space search. The approach
interleaves exponential and binary searches to find the desired cost bound,
reducing the worst-case overhead from polynomial to logarithmic.
Exponential-binary search can be used with bounded depth-first search to
improve the worst-case performance of IDA* and with breadth-first heuristic
search to improve the worst-case performance of search with inconsistent
heuristics.
| [
{
"version": "v1",
"created": "Fri, 7 Jun 2019 06:11:06 GMT"
}
] | 1,560,124,800,000 | [
[
"Sturtevant",
"Nathan",
""
],
[
"Helmert",
"Malte",
""
]
] |
1906.03253 | Victor Hansen | Victor E Hansen | Representing and Using Knowledge with the Contextual Evaluation Model | null | null | 10.13140/RG.2.2.34892.05762 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces the Contextual Evaluation Model (CEM), a novel method
for knowledge representation and manipulation. The CEM differs from existing
models in that it integrates facts, patterns and sequences into a single
contextual framework. V5, an implementation of the model is presented and
demonstrated with multiple annotated examples. The paper includes simulations
demonstrating how the model reacts to pleasure/pain stimuli. The 'thought' is
defined within the model and examples are given converting thoughts to
language, converting language to thoughts and how 'meaning' arises from
thoughts. A pattern learning algorithm is described. The algorithm is applied
to multiple problems ranging from recognizing a voice to the autonomous
learning of a simplified natural language.
| [
{
"version": "v1",
"created": "Fri, 31 May 2019 19:26:54 GMT"
}
] | 1,560,124,800,000 | [
[
"Hansen",
"Victor E",
""
]
] |
1906.03337 | Min Shu | Min Shu, Wei Zhu | Extension of Rough Set Based on Positive Transitive Relation | 9 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The application of rough set theory in incomplete information systems is a
key problem in practice since missing values almost always occur in knowledge
acquisition due to the error of data measuring, the limitation of data
collection, or the limitation of data comprehension, etc. An incomplete
information system is mainly processed by compressing the indiscernibility
relation. The existing rough set extension models based on tolerance or
symmetric similarity relations typically discard one relation among the
reflexive, symmetric and transitive relations, especially the transitive
relation. In order to overcome the limitations of the current rough set
extension models, we define a new relation called the positive transitive
relation and then propose a novel rough set extension model built upon which.
The new model holds the merit of the existing rough set extension models while
avoids their limitations of discarding transitivity or symmetry. In comparison
to the existing extension models, the proposed model has a better performance
in processing the incomplete information systems while substantially reducing
the computational complexity, taking into account the relation of tolerance and
similarity of positive transitivity, and supplementing the related theories in
accordance to the intuitive classification of incomplete information. In
summary, the positive transitive relation can improve current theoretical
analysis of incomplete information systems and the newly proposed extension
model is more suitable for processing incomplete information systems and has a
broad application prospect.
| [
{
"version": "v1",
"created": "Fri, 7 Jun 2019 21:28:53 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Jun 2019 05:23:29 GMT"
}
] | 1,560,470,400,000 | [
[
"Shu",
"Min",
""
],
[
"Zhu",
"Wei",
""
]
] |
1906.03955 | Nir Lipovetzky | Alfonso E. Gerevini, Nir Lipovetzky, Francesco Percassi, Alessandro
Saetti, Ivan Serina | Best-First Width Search for Multi Agent Privacy-preserving Planning | Accepted in ICAPS-19 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In multi-agent planning, preserving the agents' privacy has become an
increasingly popular research topic. For preserving the agents' privacy, agents
jointly compute a plan that achieves mutual goals by keeping certain
information private to the individual agents. Unfortunately, this can severely
restrict the accuracy of the heuristic functions used while searching for
solutions. It has been recently shown that, for centralized planning, the
performance of goal oriented search can be improved by combining goal oriented
search and width-based search. The combination of these techniques has been
called best-first width search. In this paper, we investigate the usage of
best-first width search in the context of (decentralised) multi-agent
privacy-preserving planning, addressing the challenges related to the agents'
privacy and performance. In particular, we show that best-first width search is
a very effective approach over several benchmark domains, even when the search
is driven by heuristics that roughly estimate the distance from goal states,
computed without using the private information of other agents. An experimental
study analyses the effectiveness of our techniques and compares them with the
state-of-the-art.
| [
{
"version": "v1",
"created": "Mon, 10 Jun 2019 13:01:07 GMT"
}
] | 1,560,211,200,000 | [
[
"Gerevini",
"Alfonso E.",
""
],
[
"Lipovetzky",
"Nir",
""
],
[
"Percassi",
"Francesco",
""
],
[
"Saetti",
"Alessandro",
""
],
[
"Serina",
"Ivan",
""
]
] |
1906.03992 | Devon Sigurdson | Devon Sigurdson, Vadim Bulitko, Sven Koenig, Carlos Hernandez, William
Yeoh | Automatic Algorithm Selection In Multi-agent Pathfinding | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In a multi-agent pathfinding (MAPF) problem, agents need to navigate from
their start to their goal locations without colliding into each other. There
are various MAPF algorithms, including Windowed Hierarchical Cooperative A*,
Flow Annotated Replanning, and Bounded Multi-Agent A*. It is often the case
that there is no a single algorithm that dominates all MAPF instances.
Therefore, in this paper, we investigate the use of deep learning to
automatically select the best MAPF algorithm from a portfolio of algorithms for
a given MAPF problem instance. Empirical results show that our automatic
algorithm selection approach, which uses an off-the-shelf convolutional neural
network, is able to outperform any individual MAPF algorithm in our portfolio.
| [
{
"version": "v1",
"created": "Mon, 10 Jun 2019 14:10:49 GMT"
},
{
"version": "v2",
"created": "Sat, 15 Jun 2019 13:55:12 GMT"
}
] | 1,560,816,000,000 | [
[
"Sigurdson",
"Devon",
""
],
[
"Bulitko",
"Vadim",
""
],
[
"Koenig",
"Sven",
""
],
[
"Hernandez",
"Carlos",
""
],
[
"Yeoh",
"William",
""
]
] |
1906.04238 | Alexander Dockhorn | Alexander Dockhorn and Sanaz Mostaghim | Introducing the Hearthstone-AI Competition | Competition Webpage:
http://www.ci.ovgu.de/Research/HearthstoneAI.html | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The Hearthstone AI framework and competition motivates the development of
artificial intelligence agents that can play collectible card games. A special
feature of those games is the high variety of cards, which can be chosen by the
players to create their own decks. In contrast to simpler card games, the value
of many cards is determined by their possible synergies. The vast amount of
possible decks, the randomness of the game, as well as the restricted
information during the player's turn offer quite a hard challenge for the
development of game-playing agents. This short paper introduces the competition
framework and goes into more detail on the problems and challenges that need to
be faced during the development process.
| [
{
"version": "v1",
"created": "Mon, 6 May 2019 12:53:36 GMT"
}
] | 1,560,297,600,000 | [
[
"Dockhorn",
"Alexander",
""
],
[
"Mostaghim",
"Sanaz",
""
]
] |
1906.04439 | Michele Alberti | Joel Niklaus, Michele Alberti, Vinaychandran Pondenkandath, Rolf
Ingold, Marcus Liwicki | Survey of Artificial Intelligence for Card Games and Its Application to
the Swiss Game Jass | null | 6th Swiss Conference on Data Science (SDS), Bern, Switzerland,
2019 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the last decades we have witnessed the success of applications of
Artificial Intelligence to playing games. In this work we address the
challenging field of games with hidden information and card games in
particular. Jass is a very popular card game in Switzerland and is closely
connected with Swiss culture. To the best of our knowledge, performances of
Artificial Intelligence agents in the game of Jass do not outperform top
players yet. Our contribution to the community is two-fold. First, we provide
an overview of the current state-of-the-art of Artificial Intelligence methods
for card games in general. Second, we discuss their application to the use-case
of the Swiss card game Jass. This paper aims to be an entry point for both
seasoned researchers and new practitioners who want to join in the Jass
challenge.
| [
{
"version": "v1",
"created": "Tue, 11 Jun 2019 08:31:21 GMT"
}
] | 1,560,297,600,000 | [
[
"Niklaus",
"Joel",
""
],
[
"Alberti",
"Michele",
""
],
[
"Pondenkandath",
"Vinaychandran",
""
],
[
"Ingold",
"Rolf",
""
],
[
"Liwicki",
"Marcus",
""
]
] |
1906.04660 | Michael Green | Michael Cerny Green, Ahmed Khalifa, Athoug Alsoughayer, Divyesh
Surana, Antonios Liapis and Julian Togelius | Two-step Constructive Approaches for Dungeon Generation | 7 pages, 4 figures, published at PCG workshop at the Foundations of
Digital Games Conference 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a two-step generative approach for creating dungeons in
the rogue-like puzzle game MiniDungeons 2. Generation is split into two steps,
initially producing the architectural layout of the level as its walls and
floor tiles, and then furnishing it with game objects representing the player's
start and goal position, challenges and rewards. Three layout creators and
three furnishers are introduced in this paper, which can be combined in
different ways in the two-step generative process for producing diverse
dungeons levels. Layout creators generate the floors and walls of a level,
while furnishers populate it with monsters, traps, and treasures. We test the
generated levels on several expressivity measures, and in simulations with
procedural persona agents.
| [
{
"version": "v1",
"created": "Tue, 11 Jun 2019 15:39:33 GMT"
}
] | 1,560,297,600,000 | [
[
"Green",
"Michael Cerny",
""
],
[
"Khalifa",
"Ahmed",
""
],
[
"Alsoughayer",
"Athoug",
""
],
[
"Surana",
"Divyesh",
""
],
[
"Liapis",
"Antonios",
""
],
[
"Togelius",
"Julian",
""
]
] |
1906.05066 | Nico Potyka | Nico Potyka and Sylwia Polberg and Anthony Hunter | Polynomial-time Updates of Epistemic States in a Fragment of
Probabilistic Epistemic Argumentation (Technical Report) | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probabilistic epistemic argumentation allows for reasoning about
argumentation problems in a way that is well founded by probability theory.
Epistemic states are represented by probability functions over possible worlds
and can be adjusted to new beliefs using update operators. While the use of
probability functions puts this approach on a solid foundational basis, it also
causes computational challenges as the amount of data to process depends
exponentially on the number of arguments. This leads to bottlenecks in
applications such as modelling opponent's beliefs for persuasion dialogues. We
show how update operators over probability functions can be related to update
operators over much more compact representations that allow polynomial-time
updates. We discuss the cognitive and probabilistic-logical plausibility of
this approach and demonstrate its applicability in computational persuasion.
| [
{
"version": "v1",
"created": "Wed, 12 Jun 2019 11:39:42 GMT"
}
] | 1,560,384,000,000 | [
[
"Potyka",
"Nico",
""
],
[
"Polberg",
"Sylwia",
""
],
[
"Hunter",
"Anthony",
""
]
] |
1906.05130 | Yunlong Liu | Yunlong Liu and Jianyang Zheng | Online Learning and Planning in Partially Observable Domains without
Prior Knowledge | arXiv admin note: text overlap with arXiv:1904.03008 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How an agent can act optimally in stochastic, partially observable domains is
a challenge problem, the standard approach to address this issue is to learn
the domain model firstly and then based on the learned model to find the (near)
optimal policy. However, offline learning the model often needs to store the
entire training data and cannot utilize the data generated in the planning
phase. Furthermore, current research usually assumes the learned model is
accurate or presupposes knowledge of the nature of the unobservable part of the
world. In this paper, for systems with discrete settings, with the benefits of
Predictive State Representations~(PSRs), a model-based planning approach is
proposed where the learning and planning phases can both be executed online and
no prior knowledge of the underlying system is required. Experimental results
show compared to the state-of-the-art approaches, our algorithm achieved a high
level of performance with no prior knowledge provided, along with theoretical
advantages of PSRs. Source code is available at
https://github.com/DMU-XMU/PSR-MCTS-Online.
| [
{
"version": "v1",
"created": "Tue, 11 Jun 2019 07:06:06 GMT"
}
] | 1,560,384,000,000 | [
[
"Liu",
"Yunlong",
""
],
[
"Zheng",
"Jianyang",
""
]
] |
1906.05160 | Michael Green | Ahmed Khalifa, Michael Cerny Green, Diego Perez-Liebana and Julian
Togelius | General Video Game Rule Generation | 8 pages, 9 listings, 1 table, 2 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce the General Video Game Rule Generation problem, and the
eponymous software framework which will be used in a new track of the General
Video Game AI (GVGAI) competition. The problem is, given a game level as input,
to generate the rules of a game that fits that level. This can be seen as the
inverse of the General Video Game Level Generation problem. Conceptualizing
these two problems as separate helps breaking the very hard problem of
generating complete games into smaller, more manageable subproblems. The
proposed framework builds on the GVGAI software and thus asks the rule
generator for rules defined in the Video Game Description Language. We describe
the API, and three different rule generators: a random, a constructive and a
search-based generator. Early results indicate that the constructive generator
generates playable and somewhat interesting game rules but has a limited
expressive range, whereas the search-based generator generates remarkably
diverse rulesets, but with an uneven quality.
| [
{
"version": "v1",
"created": "Wed, 12 Jun 2019 14:17:50 GMT"
}
] | 1,560,384,000,000 | [
[
"Khalifa",
"Ahmed",
""
],
[
"Green",
"Michael Cerny",
""
],
[
"Perez-Liebana",
"Diego",
""
],
[
"Togelius",
"Julian",
""
]
] |
1906.06436 | Maayan Shvo | Maayan Shvo, Sheila A. McIlraith | Towards Empathetic Planning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Critical to successful human interaction is a capacity for empathy - the
ability to understand and share the thoughts and feelings of another. As
Artificial Intelligence (AI) systems are increasingly required to interact with
humans in a myriad of settings, it is important to enable AI to wield empathy
as a tool to benefit those it interacts with. In this paper, we work towards
this goal by bringing together a number of important concepts: empathy, AI
planning, and reasoning in the presence of knowledge and belief. We formalize
the notion of Empathetic Planning which is informed by the beliefs and
affective state of the empathizee. We appeal to an epistemic logic framework to
represent the beliefs of the empathizee and propose AI planning-based
computational approaches to compute empathetic solutions. We illustrate the
potential benefits of our approach by conducting a study where we evaluate
participants' perceptions of the agent's empathetic abilities and assistive
capabilities.
| [
{
"version": "v1",
"created": "Fri, 14 Jun 2019 23:36:53 GMT"
}
] | 1,560,816,000,000 | [
[
"Shvo",
"Maayan",
""
],
[
"McIlraith",
"Sheila A.",
""
]
] |
1906.06455 | Edjard De Souza Mota Mota | Edjard Mota, Jacob M. Howe, Ana Schramm and Artur d'Avila Garcez | Efficient predicate invention using shared "NeMuS" | 7 pages, 5 figures, Proceedings of the 2019 International Workshop on
Neural-Symbolic Learning and Reasoning | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Amao is a cognitive agent framework that tackles the invention of predicates
with a different strategy as compared to recent advances in Inductive Logic
Programming (ILP) approaches like Meta-Intepretive Learning (MIL) technique. It
uses a Neural Multi-Space (NeMuS) graph structure to anti-unify atoms from the
Herbrand base, which passes in the inductive momentum check. Inductive Clause
Learning (ICL), as it is called, is extended here by using the weights of
logical components, already present in NeMuS, to support inductive learning by
expanding clause candidates with anti-unified atoms. An efficient invention
mechanism is achieved, including the learning of recursive hypotheses, while
restricting the shape of the hypothesis by adding bias definitions or
idiosyncrasies of the language.
| [
{
"version": "v1",
"created": "Sat, 15 Jun 2019 02:45:00 GMT"
}
] | 1,560,816,000,000 | [
[
"Mota",
"Edjard",
""
],
[
"Howe",
"Jacob M.",
""
],
[
"Schramm",
"Ana",
""
],
[
"Garcez",
"Artur d'Avila",
""
]
] |
1906.06761 | Edjard de Souza Mota | Leonardo Barreto and Edjard Mota | Self-organized inductive reasoning with NeMuS | 6 pages, 5 figures, | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural Multi-Space (NeMuS) is a weighted multi-space representation for a
portion of first-order logic designed for use with machine learning and neural
network methods. It was demonstrated that it can be used to perform reasoning
based on regions forming patterns of refutation and also in the process of
inductive learning in ILP-like style. Initial experiments were carried out to
investigate whether a self-organizing the approach is suitable to generate
similar concept regions according to the attributes that form such concepts. We
present the results and make an analysis of the suitability of the method in
the process of inductive learning with NeMuS.
| [
{
"version": "v1",
"created": "Sun, 16 Jun 2019 20:16:53 GMT"
}
] | 1,560,816,000,000 | [
[
"Barreto",
"Leonardo",
""
],
[
"Mota",
"Edjard",
""
]
] |
1906.06836 | Haibin Wang | Haibin Wang, Sujoy Sikdar, Xiaoxi Guo, Lirong Xia, Yongzhi Cao, Hanpin
Wang | Multi-type Resource Allocation with Partial Preferences | null | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | We propose multi-type probabilistic serial (MPS) and multi-type random
priority (MRP) as extensions of the well known PS and RP mechanisms to the
multi-type resource allocation problem (MTRA) with partial preferences. In our
setting, there are multiple types of divisible items, and a group of agents who
have partial order preferences over bundles consisting of one item of each
type. We show that for the unrestricted domain of partial order preferences, no
mechanism satisfies both sd-efficiency and sd-envy-freeness. Notwithstanding
this impossibility result, our main message is positive: When agents'
preferences are represented by acyclic CP-nets, MPS satisfies sd-efficiency,
sd-envy-freeness, ordinal fairness, and upper invariance, while MRP satisfies
ex-post-efficiency, sd-strategy-proofness, and upper invariance, recovering the
properties of PS and RP.
| [
{
"version": "v1",
"created": "Thu, 13 Jun 2019 08:49:21 GMT"
},
{
"version": "v2",
"created": "Tue, 19 Nov 2019 07:04:52 GMT"
},
{
"version": "v3",
"created": "Thu, 29 Oct 2020 07:15:16 GMT"
}
] | 1,604,016,000,000 | [
[
"Wang",
"Haibin",
""
],
[
"Sikdar",
"Sujoy",
""
],
[
"Guo",
"Xiaoxi",
""
],
[
"Xia",
"Lirong",
""
],
[
"Cao",
"Yongzhi",
""
],
[
"Wang",
"Hanpin",
""
]
] |
1906.07268 | Daoming Lyu | Daoming Lyu, Fangkai Yang, Bo Liu, Steven Gustafson | A Joint Planning and Learning Framework for Human-Aided Decision-Making | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conventional reinforcement learning (RL) allows an agent to learn policies
via environmental rewards only, with a long and slow learning curve, especially
at the beginning stage. On the contrary, human learning is usually much faster
because prior and general knowledge and multiple information resources are
utilized. In this paper, we propose a
\textbf{P}lanner-\textbf{A}ctor-\textbf{C}ritic architecture for
hu\textbf{MAN}-centered planning and learning (\textbf{PACMAN}), where an agent
uses prior, high-level, deterministic symbolic knowledge to plan for
goal-directed actions. PACMAN integrates Actor-Critic algorithm of RL to
fine-tune its behavior towards both environmental rewards and human feedback.
To the best our knowledge, This is the first unified framework where
knowledge-based planning, RL, and human teaching jointly contribute to the
policy learning of an agent. Our experiments demonstrate that PACMAN leads to a
significant jump-start at the early stage of learning, converges rapidly and
with small variance, and is robust to inconsistent, infrequent, and misleading
feedback.
| [
{
"version": "v1",
"created": "Mon, 17 Jun 2019 20:56:31 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Aug 2019 02:02:04 GMT"
},
{
"version": "v3",
"created": "Tue, 24 Dec 2019 17:14:02 GMT"
}
] | 1,577,232,000,000 | [
[
"Lyu",
"Daoming",
""
],
[
"Yang",
"Fangkai",
""
],
[
"Liu",
"Bo",
""
],
[
"Gustafson",
"Steven",
""
]
] |
1906.07809 | Parisa Kordjamshidi | Parisa Kordjamshidi, Dan Roth, Kristian Kersting | Declarative Learning-Based Programming as an Interface to AI Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data-driven approaches are becoming more common as problem-solving techniques
in many areas of research and industry. In most cases, machine learning models
are the key component of these solutions, but a solution involves multiple such
models, along with significant levels of reasoning with the models' output and
input. Current technologies do not make such techniques easy to use for
application experts who are not fluent in machine learning nor for machine
learning experts who aim at testing ideas and models on real-world data in the
context of the overall AI system. We review key efforts made by various AI
communities to provide languages for high-level abstractions over learning and
reasoning techniques needed for designing complex AI systems. We classify the
existing frameworks based on the type of techniques and the data and knowledge
representations they use, provide a comparative study of the way they address
the challenges of programming real-world applications, and highlight some
shortcomings and future directions.
| [
{
"version": "v1",
"created": "Tue, 18 Jun 2019 20:57:51 GMT"
}
] | 1,560,988,800,000 | [
[
"Kordjamshidi",
"Parisa",
""
],
[
"Roth",
"Dan",
""
],
[
"Kersting",
"Kristian",
""
]
] |
1906.08061 | Nir Lipovetzky | Alfonso E. Gerevini, Nir Lipovetzky, Nico Peli, Francesco Percassi,
Alessandro Saetti, Ivan Serina | Novelty Messages Filtering for Multi Agent Privacy-preserving Planning | Accepted in SOCS-19. arXiv admin note: text overlap with
arXiv:1706.06927 by other authors and arXiv:1906.03955 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In multi-agent planning, agents jointly compute a plan that achieves mutual
goals, keeping certain information private to the individual agents. Agents'
coordination is achieved through the transmission of messages. These messages
can be a source of privacy leakage as they can permit a malicious agent to
collect information about other agents' actions and search states. In this
paper, we investigate the usage of novelty techniques in the context of
(decentralised) multi-agent privacy-preserving planning, addressing the
challenges related to the agents' privacy and performance. In particular, we
show that the use of novelty based techniques can significantly reduce the
number of messages transmitted among agents, better preserving their privacy
and improving their performance. An experimental study analyses the
effectiveness of our techniques and compares them with the state-of-the-art.
Finally, we evaluate the robustness of our approach, considering different
delays in the transmission of messages as they would occur in overloaded
networks, due for example to massive attacks or critical situations.
| [
{
"version": "v1",
"created": "Tue, 18 Jun 2019 06:49:13 GMT"
}
] | 1,560,988,800,000 | [
[
"Gerevini",
"Alfonso E.",
""
],
[
"Lipovetzky",
"Nir",
""
],
[
"Peli",
"Nico",
""
],
[
"Percassi",
"Francesco",
""
],
[
"Saetti",
"Alessandro",
""
],
[
"Serina",
"Ivan",
""
]
] |
1906.08157 | Daniel Furelos-Blanco | Daniel Furelos-Blanco and Anders Jonsson | Solving Multiagent Planning Problems with Concurrent Conditional Effects | Preprint accepted for publication to the 33rd AAAI Conference on
Artificial Intelligence (AAAI-19) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work we present a novel approach to solving concurrent multiagent
planning problems in which several agents act in parallel. Our approach relies
on a compilation from concurrent multiagent planning to classical planning,
allowing us to use an off-the-shelf classical planner to solve the original
multiagent problem. The solution can be directly interpreted as a concurrent
plan that satisfies a given set of concurrency constraints, while avoiding the
exponential blowup associated with concurrent actions. Our planner is the first
to handle action effects that are conditional on what other agents are doing.
Theoretically, we show that the compilation is sound and complete. Empirically,
we show that our compilation can solve challenging multiagent planning problems
that require concurrent actions.
| [
{
"version": "v1",
"created": "Wed, 19 Jun 2019 15:34:37 GMT"
}
] | 1,560,988,800,000 | [
[
"Furelos-Blanco",
"Daniel",
""
],
[
"Jonsson",
"Anders",
""
]
] |
1906.08362 | Roberto Confalonieri | Roberto Confalonieri, Tillman Weyde, Tarek R. Besold, Ferm\'in Moscoso
del Prado Mart\'in | Trepan Reloaded: A Knowledge-driven Approach to Explaining Artificial
Neural Networks | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Explainability in Artificial Intelligence has been revived as a topic of
active research by the need of conveying safety and trust to users in the `how'
and `why' of automated decision-making. Whilst a plethora of approaches have
been developed for post-hoc explainability, only a few focus on how to use
domain knowledge, and how this influences the understandability of global
explanations from the users' perspective. In this paper, we show how ontologies
help the understandability of global post-hoc explanations, presented in the
form of symbolic models. In particular, we build on Trepan, an algorithm that
explains artificial neural networks by means of decision trees, and we extend
it to include ontologies modeling domain knowledge in the process of generating
explanations. We present the results of a user study that measures the
understandability of decision trees using a syntactic complexity measure, and
through time and accuracy of responses as well as reported user confidence and
understandability. The user study considers domains where explanations are
critical, namely, in finance and medicine. The results show that decision trees
generated with our algorithm, taking into account domain knowledge, are more
understandable than those generated by standard Trepan without the use of
ontologies.
| [
{
"version": "v1",
"created": "Wed, 19 Jun 2019 21:22:34 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Nov 2019 11:59:21 GMT"
}
] | 1,574,380,800,000 | [
[
"Confalonieri",
"Roberto",
""
],
[
"Weyde",
"Tillman",
""
],
[
"Besold",
"Tarek R.",
""
],
[
"Martín",
"Fermín Moscoso del Prado",
""
]
] |
1906.08549 | Yutaka Nagashima | Yutaka Nagashima | Designing Game of Theorems | Presented at the third Conference on Artificial Intelligence and
Theorem Proving (AITP 2018) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | "Theorem proving is similar to the game of Go. So, we can probably improve
our provers using deep learning, like DeepMind built the super-human computer
Go program, AlphaGo." Such optimism has been observed among participants of
AITP2017. But is theorem proving really similar to Go? In this paper, we first
identify the similarities and differences between them and then propose a
system in which various provers keep competing against each other and changing
themselves until they prove conjectures provided by users.
| [
{
"version": "v1",
"created": "Thu, 20 Jun 2019 10:50:15 GMT"
}
] | 1,561,075,200,000 | [
[
"Nagashima",
"Yutaka",
""
]
] |
1906.08663 | Victoria Krakovna | Tom Everitt, Ramana Kumar, Victoria Krakovna, Shane Legg | Modeling AGI Safety Frameworks with Causal Influence Diagrams | IJCAI 2019 AI Safety Workshop | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Proposals for safe AGI systems are typically made at the level of frameworks,
specifying how the components of the proposed system should be trained and
interact with each other. In this paper, we model and compare the most
promising AGI safety frameworks using causal influence diagrams. The diagrams
show the optimization objective and causal assumptions of the framework. The
unified representation permits easy comparison of frameworks and their
assumptions. We hope that the diagrams will serve as an accessible and visual
introduction to the main AGI safety frameworks.
| [
{
"version": "v1",
"created": "Thu, 20 Jun 2019 14:35:03 GMT"
}
] | 1,561,075,200,000 | [
[
"Everitt",
"Tom",
""
],
[
"Kumar",
"Ramana",
""
],
[
"Krakovna",
"Victoria",
""
],
[
"Legg",
"Shane",
""
]
] |
1906.09094 | Shushman Choudhury | Shushman Choudhury and Mykel J. Kochenderfer | Hybrid Planning for Dynamic Multimodal Stochastic Shortest Paths | 20 pages, 5 figures, 5 tables; Under Review | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sequential decision problems in applications such as manipulation in
warehouses, multi-step meal preparation, and routing in autonomous vehicle
networks often involve reasoning about uncertainty, planning over discrete
modes as well as continuous states, and reacting to dynamic updates. To
formalize such problems generally, we introduce a class of Markov Decision
Processes (MDPs) called Dynamic Multimodal Stochastic Shortest Paths (DMSSPs).
Much of the work in these domains solves deterministic variants, which can
yield poor results when the uncertainty has downstream effects. We develop a
Hybrid Stochastic Planning (HSP) algorithm, which uses domain-agnostic
abstractions to efficiently unify heuristic search for planning over discrete
modes, approximate dynamic programming for stochastic planning over continuous
states, and hierarchical interleaved planning and execution. In the domain of
autonomous multimodal routing, HSP obtains significantly higher quality
solutions than a state-of-the-art Upper Confidence Trees algorithm and a
two-level Receding Horizon Control algorithm.
| [
{
"version": "v1",
"created": "Fri, 21 Jun 2019 12:41:19 GMT"
}
] | 1,561,334,400,000 | [
[
"Choudhury",
"Shushman",
""
],
[
"Kochenderfer",
"Mykel J.",
""
]
] |
1906.09136 | Sayan Sarkar | Arushi Majha, Sayan Sarkar and Davide Zagami | Categorizing Wireheading in Partially Embedded Agents | Accepted at the AI Safety Workshop in IJCAI 2019 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | $\textit{Embedded agents}$ are not explicitly separated from their
environment, lacking clear I/O channels. Such agents can reason about and
modify their internal parts, which they are incentivized to shortcut or
$\textit{wirehead}$ in order to achieve the maximal reward. In this paper, we
provide a taxonomy of ways by which wireheading can occur, followed by a
definition of wirehead-vulnerable agents. Starting from the fully dualistic
universal agent AIXI, we introduce a spectrum of partially embedded agents and
identify wireheading opportunities that such agents can exploit, experimentally
demonstrating the results with the GRL simulation platform AIXIjs. We
contextualize wireheading in the broader class of all misalignment problems -
where the goals of the agent conflict with the goals of the human designer -
and conjecture that the only other possible type of misalignment is
specification gaming. Motivated by this taxonomy, we define wirehead-vulnerable
agents as embedded agents that choose to behave differently from fully
dualistic agents lacking access to their internal parts.
| [
{
"version": "v1",
"created": "Fri, 21 Jun 2019 13:38:35 GMT"
}
] | 1,561,334,400,000 | [
[
"Majha",
"Arushi",
""
],
[
"Sarkar",
"Sayan",
""
],
[
"Zagami",
"Davide",
""
]
] |
1906.09575 | Jian-Ya Ding | Jian-Ya Ding, Chao Zhang, Lei Shen, Shengyin Li, Bing Wang, Yinghui
Xu, Le Song | Accelerating Primal Solution Findings for Mixed Integer Programs Based
on Solution Prediction | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mixed Integer Programming (MIP) is one of the most widely used modeling
techniques for combinatorial optimization problems. In many applications, a
similar MIP model is solved on a regular basis, maintaining remarkable
similarities in model structures and solution appearances but differing in
formulation coefficients. This offers the opportunity for machine learning
methods to explore the correlations between model structures and the resulting
solution values. To address this issue, we propose to represent an MIP instance
using a tripartite graph, based on which a Graph Convolutional Network (GCN) is
constructed to predict solution values for binary variables. The predicted
solutions are used to generate a local branching type cut which can be either
treated as a global (invalid) inequality in the formulation resulting in a
heuristic approach to solve the MIP, or as a root branching rule resulting in
an exact approach. Computational evaluations on 8 distinct types of MIP
problems show that the proposed framework improves the primal solution finding
performance significantly on a state-of-the-art open-source MIP solver.
| [
{
"version": "v1",
"created": "Sun, 23 Jun 2019 10:07:47 GMT"
},
{
"version": "v2",
"created": "Mon, 9 Sep 2019 06:21:09 GMT"
}
] | 1,568,073,600,000 | [
[
"Ding",
"Jian-Ya",
""
],
[
"Zhang",
"Chao",
""
],
[
"Shen",
"Lei",
""
],
[
"Li",
"Shengyin",
""
],
[
"Wang",
"Bing",
""
],
[
"Xu",
"Yinghui",
""
],
[
"Song",
"Le",
""
]
] |
1906.10106 | Brendan Juba | Vaishak Belle and Brendan Juba | Implicitly Learning to Reason in First-Order Logic | In Fourth International Workshop on Declarative Learning Based
Programming (DeLBP 2019) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of answering queries about formulas of first-order
logic based on background knowledge partially represented explicitly as other
formulas, and partially represented as examples independently drawn from a
fixed probability distribution. PAC semantics, introduced by Valiant, is one
rigorous, general proposal for learning to reason in formal languages: although
weaker than classical entailment, it allows for a powerful model theoretic
framework for answering queries while requiring minimal assumptions about the
form of the distribution in question. To date, however, the most significant
limitation of that approach, and more generally most machine learning
approaches with robustness guarantees, is that the logical language is
ultimately essentially propositional, with finitely many atoms. Indeed, the
theoretical findings on the learning of relational theories in such generality
have been resoundingly negative. This is despite the fact that first-order
logic is widely argued to be most appropriate for representing human knowledge.
In this work, we present a new theoretical approach to robustly learning to
reason in first-order logic, and consider universally quantified clauses over a
countably infinite domain. Our results exploit symmetries exhibited by
constants in the language, and generalize the notion of implicit learnability
to show how queries can be computed against (implicitly) learned first-order
background knowledge.
| [
{
"version": "v1",
"created": "Mon, 24 Jun 2019 17:48:27 GMT"
}
] | 1,561,420,800,000 | [
[
"Belle",
"Vaishak",
""
],
[
"Juba",
"Brendan",
""
]
] |
1906.10118 | Brendan Juba | Brendan Juba | Query-driven PAC-Learning for Reasoning | In Fourth International Workshop on Declarative Learning Based
Programming (DeLBP 2019) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of learning rules from a data set that support a
proof of a given query, under Valiant's PAC-Semantics. We show how any backward
proof search algorithm that is sufficiently oblivious to the contents of its
knowledge base can be modified to learn such rules while it searches for a
proof using those rules. We note that this gives such algorithms for standard
logics such as chaining and resolution.
| [
{
"version": "v1",
"created": "Mon, 24 Jun 2019 17:59:19 GMT"
}
] | 1,561,420,800,000 | [
[
"Juba",
"Brendan",
""
]
] |
1906.10120 | Humberto Jos\'e Longo | Carlos Alexandre X. Silva and Les Foulds and Humberto J. Longo | Assembly line balancing with task division | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In a commonly-used version of the Simple Assembly Line Balancing Problem
(SALBP-1) tasks are assigned to stations along an assembly line with a fixed
cycle time in order to minimize the required number of stations. It has
traditionally been assumed that the total work needed for each product unit has
been partitioned into economically indivisible tasks. However, in practice, it
is sometimes possible to divide particular tasks in limited ways at additional
time penalty cost. Despite the penalties, task division where possible, now and
then leads to a reduction in the minimum number of stations. Deciding which
allowable tasks to divide creates a new assembly line balancing problem, TDALBP
(Task Division Assembly Line Balancing Problem). We propose a mathematical
model of the TDALBP, an exact solution procedure for it and present promising
computational results for the adaptation of some classical SALBP instances from
the research literature. The results demonstrate that the TDALBP sometimes has
the potential to significantly improve assembly line performance.
| [
{
"version": "v1",
"created": "Sat, 22 Jun 2019 14:02:05 GMT"
}
] | 1,561,507,200,000 | [
[
"Silva",
"Carlos Alexandre X.",
""
],
[
"Foulds",
"Les",
""
],
[
"Longo",
"Humberto J.",
""
]
] |
1906.10450 | Anat Goldstein | Anat Goldstein, Lior Fink and Gilad Ravid | A Framework for Evaluating Agricultural Ontologies | 18 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An ontology is a formal representation of domain knowledge, which can be
interpreted by machines. In recent years, ontologies have become a major tool
for domain knowledge representation and a core component of many knowledge
management systems, decision support systems and other intelligent systems,
inter alia, in the context of agriculture. A review of the existing literature
on agricultural ontologies, however, reveals that most of the studies, which
propose agricultural ontologies, are lacking an explicit evaluation procedure.
This is undesired because without well-structured evaluation processes, it is
difficult to consider the value of ontologies to research and practice.
Moreover, it is difficult to rely on such ontologies and share them on the
Semantic Web or between semantic aware applications. With the growing number of
ontology-based agricultural systems and the increasing popularity of the
Semantic Web, it becomes essential that such development and evaluation methods
are put forward to guide future efforts of ontology development. Our work
contributes to the literature on agricultural ontologies, by presenting a
method for evaluating agricultural ontologies, which seems to be missing from
most existing studies on agricultural ontologies. The framework supports the
matching of appropriate evaluation methods for a given ontology based on the
ontology's purpose.
| [
{
"version": "v1",
"created": "Tue, 25 Jun 2019 10:59:38 GMT"
},
{
"version": "v2",
"created": "Wed, 26 Jun 2019 02:48:20 GMT"
}
] | 1,561,593,600,000 | [
[
"Goldstein",
"Anat",
""
],
[
"Fink",
"Lior",
""
],
[
"Ravid",
"Gilad",
""
]
] |
1906.10536 | Roman Yampolskiy | James D. Miller and Roman Yampolskiy | An AGI with Time-Inconsistent Preferences | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper reveals a trap for artificial general intelligence (AGI) theorists
who use economists' standard method of discounting. This trap is implicitly and
falsely assuming that a rational AGI would have time-consistent preferences. An
agent with time-inconsistent preferences knows that its future self will
disagree with its current self concerning intertemporal decision making. Such
an agent cannot automatically trust its future self to carry out plans that its
current self considers optimal.
| [
{
"version": "v1",
"created": "Sun, 23 Jun 2019 21:22:19 GMT"
}
] | 1,561,507,200,000 | [
[
"Miller",
"James D.",
""
],
[
"Yampolskiy",
"Roman",
""
]
] |
1906.10562 | Wennan Zhu | Ben Abramowitz, Elliot Anshelevich, Wennan Zhu | Awareness of Voter Passion Greatly Improves the Distortion of Metric
Social Choice | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop new voting mechanisms for the case when voters and candidates are
located in an arbitrary unknown metric space, and the goal is to choose a
candidate minimizing social cost: the total distance from the voters to this
candidate. Previous work has often assumed that only ordinal preferences of the
voters are known (instead of their true costs), and focused on minimizing
distortion: the quality of the chosen candidate as compared with the best
possible candidate. In this paper, we instead assume that a (very small) amount
of information is known about the voter preference strengths, not just about
their ordinal preferences. We provide mechanisms with much better distortion
when this extra information is known as compared to mechanisms which use only
ordinal information. We quantify tradeoffs between the amount of information
known about preference strengths and the achievable distortion. We further
provide advice about which type of information about preference strengths seems
to be the most useful. Finally, we conclude by quantifying the ideal candidate
distortion, which compares the quality of the chosen outcome with the best
possible candidate that could ever exist, instead of only the best candidate
that is actually in the running.
| [
{
"version": "v1",
"created": "Tue, 25 Jun 2019 14:25:12 GMT"
}
] | 1,561,507,200,000 | [
[
"Abramowitz",
"Ben",
""
],
[
"Anshelevich",
"Elliot",
""
],
[
"Zhu",
"Wennan",
""
]
] |
1906.10689 | Jamal Toutouh | Jamal Toutouh, Diego Rossit, and Sergio Nesmachnow | Soft computing methods for multiobjective location of garbage
accumulation points in smart cities | null | Annals of Mathematics and Artificial Intelligence, 2019 | 10.1007/s10472-019-09647-5 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article describes the application of soft computing methods for solving
the problem of locating garbage accumulation points in urban scenarios. This is
a relevant problem in modern smart cities, in order to reduce negative
environmental and social impacts in the waste management process, and also to
optimize the available budget from the city administration to install waste
bins. A specific problem model is presented, which accounts for reducing the
investment costs, enhance the number of citizens served by the installed bins,
and the accessibility to the system. A family of single- and multi-objective
heuristics based on the PageRank method and two mutiobjective evolutionary
algorithms are proposed. Experimental evaluation performed on real scenarios on
the cities of Montevideo (Uruguay) and Bahia Blanca (Argentina) demonstrates
the effectiveness of the proposed approaches. The methods allow computing
plannings with different trade-off between the problem objectives. The computed
results improve over the current planning in Montevideo and provide a
reasonable budget cost and quality of service for Bahia Blanca.
| [
{
"version": "v1",
"created": "Tue, 25 Jun 2019 16:21:16 GMT"
}
] | 1,561,593,600,000 | [
[
"Toutouh",
"Jamal",
""
],
[
"Rossit",
"Diego",
""
],
[
"Nesmachnow",
"Sergio",
""
]
] |
1906.11068 | Aladdin Ayesh | Aladdin Ayesh | Turing Test Revisited: A Framework for an Alternative | early complete draft | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper aims to question the suitability of the Turing Test, for testing
machine intelligence, in the light of advances made in the last 60 years in
science, medicine, and philosophy of mind. While the main concept of the test
may seem sound and valid, a detailed analysis of what is required to pass the
test highlights a significant flow. Once the analysis of the test is presented,
a systematic approach is followed in analysing what is needed to devise a test
or tests for intelligent machines. The paper presents a plausible generic
framework based on categories of factors implied by subjective perception of
intelligence. An evaluative discussion concludes the paper highlighting some of
the unaddressed issues within this generic framework.
| [
{
"version": "v1",
"created": "Wed, 26 Jun 2019 13:06:33 GMT"
}
] | 1,561,593,600,000 | [
[
"Ayesh",
"Aladdin",
""
]
] |
1906.11409 | Fuyuan Xiao | Fuyuan Xiao | Generalization of Dempster-Shafer theory: A complex belief function | 9 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dempster-Shafer evidence theory has been widely used in various fields of
applications, because of the flexibility and effectiveness in modeling
uncertainties without prior information. However, the existing evidence theory
is insufficient to consider the situations where it has no capability to
express the fluctuations of data at a given phase of time during their
execution, and the uncertainty and imprecision which are inevitably involved in
the data occur concurrently with changes to the phase or periodicity of the
data. In this paper, therefore, a generalized Dempster-Shafer evidence theory
is proposed. To be specific, a mass function in the generalized Dempster-Shafer
evidence theory is modeled by a complex number, called as a complex basic
belief assignment, which has more powerful ability to express uncertain
information. Based on that, a generalized Dempster's combination rule is
exploited. In contrast to the classical Dempster's combination rule, the
condition in terms of the conflict coefficient between the evidences K<1 is
released in the generalized Dempster's combination rule. Hence, it is more
general and applicable than the classical Dempster's combination rule. When the
complex mass function is degenerated from complex numbers to real numbers, the
generalized Dempster's combination rule degenerates to the classical evidence
theory under the condition that the conflict coefficient between the evidences
K is less than 1. In a word, this generalized Dempster-Shafer evidence theory
provides a promising way to model and handle more uncertain information.
| [
{
"version": "v1",
"created": "Thu, 27 Jun 2019 01:52:04 GMT"
}
] | 1,561,680,000,000 | [
[
"Xiao",
"Fuyuan",
""
]
] |
1906.11583 | Sander Beckers | Sander Beckers, Frederick Eberhardt, Joseph Y. Halpern | Approximate Causal Abstraction | Appears in UAI-2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Scientific models describe natural phenomena at different levels of
abstraction. Abstract descriptions can provide the basis for interventions on
the system and explanation of observed phenomena at a level of granularity that
is coarser than the most fundamental account of the system. Beckers and Halpern
(2019), building on work of Rubenstein et al. (2017), developed an account of
abstraction for causal models that is exact. Here we extend this account to the
more realistic case where an abstract causal model offers only an approximation
of the underlying system. We show how the resulting account handles the
discrepancy that can arise between low- and high-level causal models of the
same system, and in the process provide an account of how one causal model
approximates another, a topic of independent interest. Finally, we extend the
account of approximate abstractions to probabilistic causal models, indicating
how and where uncertainty can enter into an approximate abstraction.
| [
{
"version": "v1",
"created": "Thu, 27 Jun 2019 12:14:57 GMT"
},
{
"version": "v2",
"created": "Sat, 29 Jun 2019 13:28:50 GMT"
}
] | 1,562,025,600,000 | [
[
"Beckers",
"Sander",
""
],
[
"Eberhardt",
"Frederick",
""
],
[
"Halpern",
"Joseph Y.",
""
]
] |
1906.12249 | Adam Amos-Binks | Adam Amos-Binks and Dustin Dannenhauer | Anticipatory Thinking: A Metacognitive Capability | Submitted to 2019 Goal Reasoning Workshop at Advances in Cognitive
Systems | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Anticipatory thinking is a complex cognitive process for assessing and
managing risk in many contexts. Humans use anticipatory thinking to identify
potential future issues and proactively take actions to manage their risks. In
this paper we define a cognitive systems approach to anticipatory thinking as a
metacognitive goal reasoning mechanism. The contributions of this paper include
(1) defining anticipatory thinking in the MIDCA cognitive architecture, (2)
operationalizing anticipatory thinking as a three step process for managing
risk in plans, and (3) a numeric risk assessment calculating an expected
cost-benefit ratio for modifying a plan with anticipatory actions.
| [
{
"version": "v1",
"created": "Fri, 28 Jun 2019 14:45:41 GMT"
}
] | 1,561,939,200,000 | [
[
"Amos-Binks",
"Adam",
""
],
[
"Dannenhauer",
"Dustin",
""
]
] |
1906.12314 | Ian Gent | Charlie Blake and Ian P. Gent | The Winnability of Klondike Solitaire and Many Other Patience Games | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Our ignorance of the winnability percentage of the game in the Windows
Solitaire program, more properly called 'Klondike', has been described as "one
of the embarrassments of applied mathematics". Klondike is just one of many
single-player card games, generically called 'patience' or 'solitaire' games,
for which players have long wanted to know how likely a particular game is to
be winnable. A number of different games have been studied empirically in the
academic literature and by non-academic enthusiasts. Here we show that a single
general purpose Artificial Intelligence program, called "Solvitaire", can be
used to determine the winnability percentage of 45 different single-player card
games with a 95% confidence interval of +/- 0.1% or better. For example, we
report the winnability of Klondike as 81.956% +/- 0.096% (in the 'thoughtful'
variant where the player knows the location of all cards), a 30-fold reduction
in confidence interval over the best previous result. Almost all our results
are either entirely new or represent significant improvements on previous
knowledge.
| [
{
"version": "v1",
"created": "Fri, 28 Jun 2019 17:19:36 GMT"
},
{
"version": "v2",
"created": "Mon, 23 Sep 2019 15:42:30 GMT"
},
{
"version": "v3",
"created": "Wed, 6 Nov 2019 15:21:49 GMT"
},
{
"version": "v4",
"created": "Tue, 10 Jan 2023 17:03:35 GMT"
}
] | 1,673,395,200,000 | [
[
"Blake",
"Charlie",
""
],
[
"Gent",
"Ian P.",
""
]
] |
1907.00240 | Dennis Soemers | Matthew Stephenson, \'Eric Piette, Dennis J. N. J. Soemers, Cameron
Browne | An Overview of the Ludii General Game System | Accepted at the IEEE Conference on Games (CoG) 2019 (Demo paper) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Digital Ludeme Project (DLP) aims to reconstruct and analyse over 1000
traditional strategy games using modern techniques. One of the key aspects of
this project is the development of Ludii, a general game system that will be
able to model and play the complete range of games required by this project.
Such an undertaking will create a wide range of possibilities for new AI
challenges. In this paper we describe many of the features of Ludii that can be
used. This includes designing and modifying games using the Ludii game
description language, creating agents capable of playing these games, and
several advantages the system has over prior general game software.
| [
{
"version": "v1",
"created": "Sat, 29 Jun 2019 17:16:27 GMT"
}
] | 1,562,025,600,000 | [
[
"Stephenson",
"Matthew",
""
],
[
"Piette",
"Éric",
""
],
[
"Soemers",
"Dennis J. N. J.",
""
],
[
"Browne",
"Cameron",
""
]
] |
1907.00244 | Dennis Soemers | \'Eric Piette, Matthew Stephenson, Dennis J. N. J. Soemers, Cameron
Browne | An Empirical Evaluation of Two General Game Systems: Ludii and RBG | Accepted at the IEEE Conference on Games (CoG) 2019 (Short paper) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although General Game Playing (GGP) systems can facilitate useful research in
Artificial Intelligence (AI) for game-playing, they are often computationally
inefficient and somewhat specialised to a specific class of games. However,
since the start of this year, two General Game Systems have emerged that
provide efficient alternatives to the academic state of the art -- the Game
Description Language (GDL). In order of publication, these are the Regular
Boardgames language (RBG), and the Ludii system. This paper offers an
experimental evaluation of Ludii. Here, we focus mainly on a comparison between
the two new systems in terms of two key properties for any GGP system:
simplicity/clarity (e.g. human-readability), and efficiency.
| [
{
"version": "v1",
"created": "Sat, 29 Jun 2019 17:21:40 GMT"
}
] | 1,562,025,600,000 | [
[
"Piette",
"Éric",
""
],
[
"Stephenson",
"Matthew",
""
],
[
"Soemers",
"Dennis J. N. J.",
""
],
[
"Browne",
"Cameron",
""
]
] |
1907.00245 | Dennis Soemers | C\'edric Piette, \'Eric Piette, Matthew Stephenson, Dennis J. N. J.
Soemers, Cameron Browne | Ludii and XCSP: Playing and Solving Logic Puzzles | Accepted at the IEEE Conference on Games (CoG) 2019 (Short paper) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many of the famous single-player games, commonly called puzzles, can be shown
to be NP-Complete. Indeed, this class of complexity contains hundreds of
puzzles, since people particularly appreciate completing an intractable puzzle,
such as Sudoku, but also enjoy the ability to check their solution easily once
it's done. For this reason, using constraint programming is naturally suited to
solve them. In this paper, we focus on logic puzzles described in the Ludii
general game system and we propose using the XCSP formalism in order to solve
them with any CSP solver.
| [
{
"version": "v1",
"created": "Sat, 29 Jun 2019 17:28:27 GMT"
}
] | 1,562,025,600,000 | [
[
"Piette",
"Cédric",
""
],
[
"Piette",
"Éric",
""
],
[
"Stephenson",
"Matthew",
""
],
[
"Soemers",
"Dennis J. N. J.",
""
],
[
"Browne",
"Cameron",
""
]
] |
1907.00246 | Dennis Soemers | Matthew Stephenson, \'Eric Piette, Dennis J. N. J. Soemers, Cameron
Browne | Ludii as a Competition Platform | Accepted at the IEEE Conference on Games (CoG) 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ludii is a general game system being developed as part of the ERC-funded
Digital Ludeme Project (DLP). While its primary aim is to model, play, and
analyse the full range of traditional strategy games, Ludii also has the
potential to support a wide range of AI research topics and competitions. This
paper describes some of the future competitions and challenges that we intend
to run using the Ludii system, highlighting some of its most important aspects
that can potentially lead to many algorithm improvements and new avenues of
research. We compare and contrast our proposed competition motivations, goals
and frameworks against those of existing general game playing competitions,
addressing the strengths and weaknesses of each platform.
| [
{
"version": "v1",
"created": "Sat, 29 Jun 2019 17:33:12 GMT"
}
] | 1,562,025,600,000 | [
[
"Stephenson",
"Matthew",
""
],
[
"Piette",
"Éric",
""
],
[
"Soemers",
"Dennis J. N. J.",
""
],
[
"Browne",
"Cameron",
""
]
] |
1907.00313 | Stefanos Nikolaidis | Houston Claure, Yifang Chen, Jignesh Modi, Malte Jung, Stefanos
Nikolaidis | Multi-Armed Bandits with Fairness Constraints for Distributing Resources
to Human Teammates | null | Proceedings of the 2020 ACM/IEEE International Conference on
Human-Robot Interaction | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How should a robot that collaborates with multiple people decide upon the
distribution of resources (e.g. social attention, or parts needed for an
assembly)? People are uniquely attuned to how resources are distributed. A
decision to distribute more resources to one team member than another might be
perceived as unfair with potentially detrimental effects for trust. We
introduce a multi-armed bandit algorithm with fairness constraints, where a
robot distributes resources to human teammates of different skill levels. In
this problem, the robot does not know the skill level of each human teammate,
but learns it by observing their performance over time. We define fairness as a
constraint on the minimum rate that each human teammate is selected throughout
the task. We provide theoretical guarantees on performance and perform a
large-scale user study, where we adjust the level of fairness in our algorithm.
Results show that fairness in resource distribution has a significant effect on
users' trust in the system.
| [
{
"version": "v1",
"created": "Sun, 30 Jun 2019 03:41:05 GMT"
},
{
"version": "v2",
"created": "Mon, 8 Jul 2019 02:06:54 GMT"
},
{
"version": "v3",
"created": "Mon, 7 Dec 2020 05:32:38 GMT"
}
] | 1,607,385,600,000 | [
[
"Claure",
"Houston",
""
],
[
"Chen",
"Yifang",
""
],
[
"Modi",
"Jignesh",
""
],
[
"Jung",
"Malte",
""
],
[
"Nikolaidis",
"Stefanos",
""
]
] |
1907.00430 | Nadisha-Marie Aliman | Nadisha-Marie Aliman and Leon Kester | Requisite Variety in Ethical Utility Functions for AI Value Alignment | IJCAI 2019 AI Safety Workshop | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Being a complex subject of major importance in AI Safety research, value
alignment has been studied from various perspectives in the last years.
However, no final consensus on the design of ethical utility functions
facilitating AI value alignment has been achieved yet. Given the urgency to
identify systematic solutions, we postulate that it might be useful to start
with the simple fact that for the utility function of an AI not to violate
human ethical intuitions, it trivially has to be a model of these intuitions
and reflect their variety $ - $ whereby the most accurate models pertaining to
human entities being biological organisms equipped with a brain constructing
concepts like moral judgements, are scientific models. Thus, in order to better
assess the variety of human morality, we perform a transdisciplinary analysis
applying a security mindset to the issue and summarizing variety-relevant
background knowledge from neuroscience and psychology. We complement this
information by linking it to augmented utilitarianism as a suitable ethical
framework. Based on that, we propose first practical guidelines for the design
of approximate ethical goal functions that might better capture the variety of
human moral judgements. Finally, we conclude and address future possible
challenges.
| [
{
"version": "v1",
"created": "Sun, 30 Jun 2019 18:55:31 GMT"
}
] | 1,562,025,600,000 | [
[
"Aliman",
"Nadisha-Marie",
""
],
[
"Kester",
"Leon",
""
]
] |
1907.00716 | Fuyuan Xiao | Fuyuan Xiao | Evidential distance measure in complex belief function theory | 4 pages, 2 figures. arXiv admin note: text overlap with
arXiv:1906.11409 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, an evidential distance measure is proposed which can measure
the difference or dissimilarity between complex basic belief assignments
(CBBAs), in which the CBBAs are composed of complex numbers. When the CBBAs are
degenerated from complex numbers to real numbers, i.e., BBAs, the proposed
distance will degrade into the Jousselme et al.'s distance. Therefore, the
proposed distance provides a promising way to measure the differences between
evidences in a more general framework of complex plane space.
| [
{
"version": "v1",
"created": "Thu, 27 Jun 2019 02:36:22 GMT"
}
] | 1,562,025,600,000 | [
[
"Xiao",
"Fuyuan",
""
]
] |
1907.01047 | Maen Alzubi | Maen Alzubi, Szilvester Kov\'acs | Investigating The Piece-Wise Linearity And Benchmark Related To
Koczy-Hirota Fuzzy Linear Interpolation | null | Journal of Theoretical and Applied Information Technology 15th
June 2019. Vol.97. No 11 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fuzzy Rule Interpolation (FRI) reasoning methods have been introduced to
address sparse fuzzy rule bases and reduce complexity. The first FRI method was
the Koczy and Hirota (KH) proposed "Linear Interpolation". Besides, several
conditions and criteria have been suggested for unifying the common
requirements FRI methods have to satisfy. One of the most conditions is
restricted the fuzzy set of the conclusion must preserve a Piece-Wise Linearity
(PWL) if all antecedents and consequents of the fuzzy rules are preserving on
PWL sets at {\alpha}-cut levels. The KH FRI is one of FRI methods which cannot
satisfy this condition. Therefore, the goal of this paper is to investigate
equations and notations related to PWL property, which is aimed to highlight
the problematic properties of the KH FRI method to prove its efficiency with
PWL condition. In addition, this paper is focusing on constructing benchmark
examples to be a baseline for testing other FRI methods against situations that
are not satisfied with the linearity condition for KH FRI.
| [
{
"version": "v1",
"created": "Mon, 1 Jul 2019 20:08:48 GMT"
},
{
"version": "v2",
"created": "Tue, 12 Nov 2019 16:41:36 GMT"
}
] | 1,573,603,200,000 | [
[
"Alzubi",
"Maen",
""
],
[
"Kovács",
"Szilvester",
""
]
] |
1907.01224 | Jake Chandler | Jake Chandler and Richard Booth | Elementary Iterated Revision and the Levi Identity | Extended version of a paper accepted to LORI 2019 (22 pages) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent work has considered the problem of extending to the case of iterated
belief change the so-called `Harper Identity' (HI), which defines single-shot
contraction in terms of single-shot revision. The present paper considers the
prospects of providing a similar extension of the Levi Identity (LI), in which
the direction of definition runs the other way. We restrict our attention here
to the three classic iterated revision operators--natural, restrained and
lexicographic, for which we provide here the first collective characterisation
in the literature, under the appellation of `elementary' operators. We consider
two prima facie plausible ways of extending (LI). The first proposal involves
the use of the rational closure operator to offer a `reductive' account of
iterated revision in terms of iterated contraction. The second, which doesn't
commit to reductionism, was put forward some years ago by Nayak et al. We
establish that, for elementary revision operators and under mild assumptions
regarding contraction, Nayak's proposal is equivalent to a new set of
postulates formalising the claim that contraction by $\neg A$ should be
considered to be a kind of `mild' revision by $A$. We then show that these, in
turn, under slightly weaker assumptions, jointly amount to the conjunction of a
pair of constraints on the extension of (HI) that were recently proposed in the
literature. Finally, we consider the consequences of endorsing both suggestions
and show that this would yield an identification of rational revision with
natural revision. We close the paper by discussing the general prospects for
defining iterated revision in terms of iterated contraction.
| [
{
"version": "v1",
"created": "Tue, 2 Jul 2019 08:14:38 GMT"
}
] | 1,562,112,000,000 | [
[
"Chandler",
"Jake",
""
],
[
"Booth",
"Richard",
""
]
] |
1907.01682 | Kinzang Chhogyal | Kinzang Chhogyal, Abhaya Nayak, Aditya Ghose, Mehmet Orgun and Hoa Dam | On Conforming and Conflicting Values | AI for Social Good Workshop, IJCAI 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Values are things that are important to us. Actions activate values - they
either go against our values or they promote our values. Values themselves can
either be conforming or conflicting depending on the action that is taken. In
this short paper, we argue that values may be classified as one of two types -
conflicting and inherently conflicting values. They are distinguished by the
fact that the latter in some sense can be thought of as being independent of
actions. This allows us to do two things: i) check whether a set of values is
consistent and ii) check whether it is in conflict with other sets of values.
| [
{
"version": "v1",
"created": "Tue, 2 Jul 2019 23:40:31 GMT"
},
{
"version": "v2",
"created": "Mon, 8 Jul 2019 01:59:05 GMT"
}
] | 1,562,630,400,000 | [
[
"Chhogyal",
"Kinzang",
""
],
[
"Nayak",
"Abhaya",
""
],
[
"Ghose",
"Aditya",
""
],
[
"Orgun",
"Mehmet",
""
],
[
"Dam",
"Hoa",
""
]
] |
1907.02548 | Levi Lelis | D\^amaris S. Bento, Andr\'e G. Pereira and Levi H. S. Lelis | Procedural Generation of Initial States of Sokoban | Accepted for publication at IJCAI'19 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Procedural generation of initial states of state-space search problems have
applications in human and machine learning as well as in the evaluation of
planning systems. In this paper we deal with the task of generating hard and
solvable initial states of Sokoban puzzles. We propose hardness metrics based
on pattern database heuristics and the use of novelty to improve the
exploration of search methods in the task of generating initial states. We then
present a system called Beta that uses our hardness metrics and novelty to
generate initial states. Experiments show that Beta is able to generate initial
states that are harder to solve by a specialized solver than those designed by
human experts.
| [
{
"version": "v1",
"created": "Thu, 4 Jul 2019 18:06:25 GMT"
}
] | 1,562,544,000,000 | [
[
"Bento",
"Dâmaris S.",
""
],
[
"Pereira",
"André G.",
""
],
[
"Lelis",
"Levi H. S.",
""
]
] |
1907.04269 | Shuai Ma | Shuai Ma, Jia Yuan Yu, Ahmet Satir | A Scheme for Dynamic Risk-Sensitive Sequential Decision Making | 20 pages, 3 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a scheme for sequential decision making with a risk-sensitive
objective and constraints in a dynamic environment. A neural network is trained
as an approximator of the mapping from parameter space to space of risk and
policy with risk-sensitive constraints. For a given risk-sensitive problem, in
which the objective and constraints are, or can be estimated by, functions of
the mean and variance of return, we generate a synthetic dataset as training
data. Parameters defining a targeted process might be dynamic, i.e., they might
vary over time, so we sample them within specified intervals to deal with these
dynamics. We show that: i). Most risk measures can be estimated using return
variance; ii). By virtue of the state-augmentation transformation, practical
problems modeled by Markov decision processes with stochastic rewards can be
solved in a risk-sensitive scenario; and iii). The proposed scheme is validated
by a numerical experiment.
| [
{
"version": "v1",
"created": "Tue, 9 Jul 2019 16:12:21 GMT"
}
] | 1,562,716,800,000 | [
[
"Ma",
"Shuai",
""
],
[
"Yu",
"Jia Yuan",
""
],
[
"Satir",
"Ahmet",
""
]
] |
1907.04659 | Ravi Kashyap | Ravi Kashyap | Artificial Intelligence: A Child's Play | null | Technological Forecasting and Social Change, 166, May 2021, 120555 | 10.1016/j.techfore.2020.120555 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We discuss the objectives of any endeavor in creating artificial
intelligence, AI, and provide a possible alternative. Intelligence might be an
unintended consequence of curiosity left to roam free, best exemplified by a
frolicking infant. This suggests that our attempts at AI could have been
misguided. What we actually need to strive for can be termed artificial
curiosity, AC, and intelligence happens as a consequence of those efforts. For
this unintentional yet welcome aftereffect to set in a foundational list of
guiding principles needs to be present. We start with the intuition for this
line of reasoning and formalize it with a series of definitions, assumptions,
ingredients, models and iterative improvements that will be necessary to make
the incubation of intelligence a reality. Our discussion provides conceptual
modifications to the Turing Test and to Searle's Chinese room argument. We
discuss the future implications for society as AI becomes an integral part of
life.
We provide a road-map for creating intelligence with the technical parts
relegated to the appendix so that the article is accessible to a wide audience.
The central techniques in our formal approach to creating intelligence draw
upon tools and concepts widely used in physics, cognitive science, psychology,
evolutionary biology, statistics, linguistics, communication systems, pattern
recognition, marketing, economics, finance, information science and
computational theory highlighting that solutions for creating artificial
intelligence have to transcend the artificial barriers between various fields
and be highly multi-disciplinary.
| [
{
"version": "v1",
"created": "Mon, 1 Jul 2019 04:46:07 GMT"
},
{
"version": "v2",
"created": "Thu, 18 Jun 2020 11:50:24 GMT"
},
{
"version": "v3",
"created": "Sat, 30 Jan 2021 15:05:12 GMT"
}
] | 1,612,224,000,000 | [
[
"Kashyap",
"Ravi",
""
]
] |
1907.04679 | Javier Navarro | Javier Navarro, Christian Wagner | Measuring Inter-group Agreement on zSlice Based General Type-2 Fuzzy
Sets | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, there has been much research into modelling of uncertainty in human
perception through Fuzzy Sets (FSs). Most of this research has focused on
allowing respondents to express their (intra) uncertainty using intervals.
Here, depending on the technique used and types of uncertainties being modelled
different types of FSs can be obtained (e.g., Type-1, Interval Type-2, General
Type-2). Arguably, one of the most flexible techniques is the Interval
Agreement Approach (IAA) as it allows to model the perception of all
respondents without making assumptions such as outlier removal or predefined
membership function types (e.g. Gaussian). A key aspect in the analysis of
interval-valued data and indeed, IAA based agreement models of said data, is to
determine the position and strengths of agreement across all the
sources/participants. While previously, the Agreement Ratio was proposed to
measure the strength of agreement in fuzzy set based models of interval data,
said measure has only been applicable to type-1 fuzzy sets. In this paper, we
extend the Agreement Ratio to capture the degree of inter-group agreement
modelled by a General Type-2 Fuzzy Set when using the IAA. This measure relies
on using a similarity measure to quantitatively express the relation between
the different levels of agreement in a given FS. Synthetic examples are
provided in order to demonstrate both behaviour and calculation of the measure.
Finally, an application to real-world data is provided in order to show the
potential of this measure to assess the divergence of opinions for ambiguous
concepts when heterogeneous groups of participants are involved.
| [
{
"version": "v1",
"created": "Tue, 9 Jul 2019 16:36:36 GMT"
}
] | 1,562,803,200,000 | [
[
"Navarro",
"Javier",
""
],
[
"Wagner",
"Christian",
""
]
] |
1907.04719 | Fuyuan Xiao | Fuyuan Xiao | Generalized Belief Function: A new concept for uncertainty modelling and
processing | 10 pages. arXiv admin note: substantial text overlap with
arXiv:1907.00716, arXiv:1906.11409 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we generalize the belief function on complex plane from
another point of view. We first propose a new concept of complex mass function
based on the complex number, called complex basic belief assignment, which is a
generalization of the traditional mass function in Dempster-Shafer evidence
theory. On the basis of the de nition of complex mass function, the belief
function and plausibility function are generalized. In particular, when the
complex mass function is degenerated from complex numbers to real numbers, the
generalized belief and plausibility functions degenerate into the traditional
belief and plausibility functions in DSE theory, respectively.
| [
{
"version": "v1",
"created": "Wed, 3 Jul 2019 06:42:35 GMT"
}
] | 1,562,803,200,000 | [
[
"Xiao",
"Fuyuan",
""
]
] |
1907.05390 | Guojun Wu | Guojun Wu, Yanhua Li, Zhenming Liu, Jie Bao, Yu Zheng, Jieping Ye, Jun
Luo | Reward Advancement: Transforming Policy under Maximum Causal Entropy
Principle | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Many real-world human behaviors can be characterized as a sequential decision
making processes, such as urban travelers choices of transport modes and routes
(Wu et al. 2017). Differing from choices controlled by machines, which in
general follows perfect rationality to adopt the policy with the highest
reward, studies have revealed that human agents make sub-optimal decisions
under bounded rationality (Tao, Rohde, and Corcoran 2014). Such behaviors can
be modeled using maximum causal entropy (MCE) principle (Ziebart 2010). In this
paper, we define and investigate a general reward trans-formation problem
(namely, reward advancement): Recovering the range of additional reward
functions that transform the agent's policy from original policy to a
predefined target policy under MCE principle. We show that given an MDP and a
target policy, there are infinite many additional reward functions that can
achieve the desired policy transformation. Moreover, we propose an algorithm to
further extract the additional rewards with minimum "cost" to implement the
policy transformation.
| [
{
"version": "v1",
"created": "Thu, 11 Jul 2019 17:11:57 GMT"
}
] | 1,562,889,600,000 | [
[
"Wu",
"Guojun",
""
],
[
"Li",
"Yanhua",
""
],
[
"Liu",
"Zhenming",
""
],
[
"Bao",
"Jie",
""
],
[
"Zheng",
"Yu",
""
],
[
"Ye",
"Jieping",
""
],
[
"Luo",
"Jun",
""
]
] |
1907.05575 | Sydney Katz | Sydney M. Katz, Anne-Claire Le Bihan, Mykel J. Kochenderfer | Learning an Urban Air Mobility Encounter Model from Expert Preferences | 8 pages, 7 figures, submitted to 2019 Digital Avionics Systems
Conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Airspace models have played an important role in the development and
evaluation of aircraft collision avoidance systems for both manned and unmanned
aircraft. As Urban Air Mobility (UAM) systems are being developed, we need new
encounter models that are representative of their operational environment.
Developing such models is challenging due to the lack of data on UAM behavior
in the airspace. While previous encounter models for other aircraft types rely
on large datasets to produce realistic trajectories, this paper presents an
approach to encounter modeling that instead relies on expert knowledge. In
particular, recent advances in preference-based learning are extended to tune
an encounter model from expert preferences. The model takes the form of a
stochastic policy for a Markov decision process (MDP) in which the reward
function is learned from pairwise queries of a domain expert. We evaluate the
performance of two querying methods that seek to maximize the information
obtained from each query. Ultimately, we demonstrate a method for generating
realistic encounter trajectories with only a few minutes of an expert's time.
| [
{
"version": "v1",
"created": "Fri, 12 Jul 2019 04:44:10 GMT"
}
] | 1,563,148,800,000 | [
[
"Katz",
"Sydney M.",
""
],
[
"Bihan",
"Anne-Claire Le",
""
],
[
"Kochenderfer",
"Mykel J.",
""
]
] |
1907.05688 | Alexantrou Serb | A. Serb, I. Kobyzev, J. Wang, T. Prodromakis | A semi-holographic hyperdimensional representation system for
hardware-friendly cognitive computing | 9 pages, 2 figures, 3 tables Submitted version | null | 10.1098/rsta.2019.0162 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the main, long-term objectives of artificial intelligence is the
creation of thinking machines. To that end, substantial effort has been placed
into designing cognitive systems; i.e. systems that can manipulate
semantic-level information. A substantial part of that effort is oriented
towards designing the mathematical machinery underlying cognition in a way that
is very efficiently implementable in hardware. In this work we propose a
'semi-holographic' representation system that can be implemented in hardware
using only multiplexing and addition operations, thus avoiding the need for
expensive multiplication. The resulting architecture can be readily constructed
by recycling standard microprocessor elements and is capable of performing two
key mathematical operations frequently used in cognition, superposition and
binding, within a budget of below 6 pJ for 64- bit operands. Our proposed
'cognitive processing unit' (CoPU) is intended as just one (albeit crucial)
part of much larger cognitive systems where artificial neural networks of all
kinds and associative memories work in concord to give rise to intelligence.
| [
{
"version": "v1",
"created": "Fri, 12 Jul 2019 11:56:29 GMT"
},
{
"version": "v2",
"created": "Mon, 15 Jul 2019 15:51:27 GMT"
}
] | 1,615,939,200,000 | [
[
"Serb",
"A.",
""
],
[
"Kobyzev",
"I.",
""
],
[
"Wang",
"J.",
""
],
[
"Prodromakis",
"T.",
""
]
] |
1907.05861 | Thomy Phan | Thomy Phan, Thomas Gabor, Robert M\"uller, Christoph Roch, Claudia
Linnhoff-Popien | Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning | Accepted to IJCAI 2019. arXiv admin note: substantial text overlap
with arXiv:1905.04020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose Stable Yet Memory Bounded Open-Loop (SYMBOL) planning, a general
memory bounded approach to partially observable open-loop planning. SYMBOL
maintains an adaptive stack of Thompson Sampling bandits, whose size is bounded
by the planning horizon and can be automatically adapted according to the
underlying domain without any prior domain knowledge beyond a generative model.
We empirically test SYMBOL in four large POMDP benchmark problems to
demonstrate its effectiveness and robustness w.r.t. the choice of
hyperparameters and evaluate its adaptive memory consumption. We also compare
its performance with other open-loop planning algorithms and POMCP.
| [
{
"version": "v1",
"created": "Thu, 11 Jul 2019 09:42:47 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Dec 2023 01:18:03 GMT"
}
] | 1,703,808,000,000 | [
[
"Phan",
"Thomy",
""
],
[
"Gabor",
"Thomas",
""
],
[
"Müller",
"Robert",
""
],
[
"Roch",
"Christoph",
""
],
[
"Linnhoff-Popien",
"Claudia",
""
]
] |
1907.06096 | Navya Singh | Ms. Navya Singh, Mr. Anshul Dhull, Mr. Barath Mohan.S, Mr. Bhavish
Pahwa, Ms. Komal Sharma | Automated Gaming Pommerman: FFA | 5 pages , 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Our game Pommerman is based on the console game Bommerman. The game starts on
an 11 by 11 platform. Pommerman is a multi-agent environment and is made up of
a set of different situations and contains four agents.
| [
{
"version": "v1",
"created": "Sat, 13 Jul 2019 15:20:19 GMT"
}
] | 1,563,235,200,000 | [
[
"Singh",
"Ms. Navya",
""
],
[
"Dhull",
"Mr. Anshul",
""
],
[
"S",
"Mr. Barath Mohan.",
""
],
[
"Pahwa",
"Mr. Bhavish",
""
],
[
"Sharma",
"Ms. Komal",
""
]
] |
1907.06386 | Claudio Di Ciccio | Anton Yeshchenko and Claudio Di Ciccio and Jan Mendling and Artem
Polyvyanyy | Comprehensive Process Drift Detection with Visual Analytics | Accepted for publication at the 38th International Conference on
Conceptual Modeling (ER 2019), http://www.inf.ufrgs.br/er2019/ | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent research has introduced ideas from concept drift into process mining
to enable the analysis of changes in business processes over time. This stream
of research, however, has not yet addressed the challenges of drift
categorization, drilling-down, and quantification. In this paper, we propose a
novel technique for managing process drifts, called Visual Drift Detection
(VDD), which fulfills these requirements. The technique starts by clustering
declarative process constraints discovered from recorded logs of executed
business processes based on their similarity and then applies change point
detection on the identified clusters to detect drifts. VDD complements these
features with detailed visualizations and explanations of drifts. Our
evaluation, both on synthetic and real-world logs, demonstrates all the
aforementioned capabilities of the technique.
| [
{
"version": "v1",
"created": "Mon, 15 Jul 2019 09:24:45 GMT"
}
] | 1,563,235,200,000 | [
[
"Yeshchenko",
"Anton",
""
],
[
"Di Ciccio",
"Claudio",
""
],
[
"Mendling",
"Jan",
""
],
[
"Polyvyanyy",
"Artem",
""
]
] |
1907.06562 | Fernando de Mesentier Silva | Amy K. Hoover, Julian Togelius, Scott Lee and Fernando de Mesentier
Silva | The Many AI Challenges of Hearthstone | 12 pages. Journal paper | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Games have benchmarked AI methods since the inception of the field, with
classic board games such as Chess and Go recently leaving room for video games
with related yet different sets of challenges. The set of AI problems
associated with video games has in recent decades expanded from simply playing
games to win, to playing games in particular styles, generating game content,
modeling players etc. Different games pose very different challenges for AI
systems, and several different AI challenges can typically be posed by the same
game. In this article we analyze the popular collectible card game Hearthstone
(Blizzard 2014) and describe a varied set of interesting AI challenges posed by
this game. Collectible card games are relatively understudied in the AI
community, despite their popularity and the interesting challenges they pose.
Analyzing a single game in-depth in the manner we do here allows us to see the
entire field of AI and Games through the lens of a single game, discovering a
few new variations on existing research topics.
| [
{
"version": "v1",
"created": "Mon, 15 Jul 2019 16:06:41 GMT"
}
] | 1,563,235,200,000 | [
[
"Hoover",
"Amy K.",
""
],
[
"Togelius",
"Julian",
""
],
[
"Lee",
"Scott",
""
],
[
"Silva",
"Fernando de Mesentier",
""
]
] |
1907.06570 | Fernando de Mesentier Silva | Luvneesh Mugrai, Fernando de Mesentier Silva, Christoffer Holmg{\aa}rd
and Julian Togelius | Automated Playtesting of Matching Tile Games | 7 pages. IEEE Conference On Games (COG) 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Matching tile games are an extremely popular game genre. Arguably the most
popular iteration, Match-3 games, are simple to understand puzzle games, making
them great benchmarks for research. In this paper, we propose developing
different procedural personas for Match-3 games in order to approximate
different human playstyles to create an automated playtesting system. The
procedural personas are realized through evolving the utility function for the
Monte Carlo Tree Search agent. We compare the performance and results of the
evolution agents with the standard Vanilla Monte Carlo Tree Search
implementation as well as to a random move-selection agent. We then observe the
impacts on both the game's design and the game design process. Lastly, a user
study is performed to compare the agents to human play traces.
| [
{
"version": "v1",
"created": "Mon, 15 Jul 2019 16:24:43 GMT"
}
] | 1,563,235,200,000 | [
[
"Mugrai",
"Luvneesh",
""
],
[
"Silva",
"Fernando de Mesentier",
""
],
[
"Holmgård",
"Christoffer",
""
],
[
"Togelius",
"Julian",
""
]
] |
1907.08194 | Robin Manhaeve | Robin Manhaeve, Sebastijan Duman\v{c}i\'c, Angelika Kimmig, Thomas
Demeester, Luc De Raedt | Neural Probabilistic Logic Programming in DeepProbLog | Extended version of DeepProbLog: Neural Probabilistic Logic
Programming (previously published at NeurIPS 2018). arXiv admin note: text
overlap with arXiv:1805.10872 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce DeepProbLog, a neural probabilistic logic programming language
that incorporates deep learning by means of neural predicates. We show how
existing inference and learning techniques of the underlying probabilistic
logic programming language ProbLog can be adapted for the new language. We
theoretically and experimentally demonstrate that DeepProbLog supports (i) both
symbolic and subsymbolic representations and inference, (ii) program induction,
(iii) probabilistic (logic) programming, and (iv) (deep) learning from
examples. To the best of our knowledge, this work is the first to propose a
framework where general-purpose neural networks and expressive
probabilistic-logical modeling and reasoning are integrated in a way that
exploits the full expressiveness and strengths of both worlds and can be
trained end-to-end based on examples.
| [
{
"version": "v1",
"created": "Thu, 18 Jul 2019 11:14:01 GMT"
},
{
"version": "v2",
"created": "Mon, 23 Sep 2019 18:34:22 GMT"
}
] | 1,569,456,000,000 | [
[
"Manhaeve",
"Robin",
""
],
[
"Dumančić",
"Sebastijan",
""
],
[
"Kimmig",
"Angelika",
""
],
[
"Demeester",
"Thomas",
""
],
[
"De Raedt",
"Luc",
""
]
] |
1907.08352 | Zhanhao Xiao | Zhanhao Xiao, Hai Wan, Hankui Hankz Zhuo, Jinxia Lin, Yanan Liu | Representation Learning for Classical Planning from Partially Observed
Traces | 11 pages, 6 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Specifying a complete domain model is time-consuming, which has been a
bottleneck of AI planning technique application in many real-world scenarios.
Most classical domain-model learning approaches output a domain model in the
form of the declarative planning language, such as STRIPS or PDDL, and solve
new planning instances by invoking an existing planner. However, planning in
such a representation is sensitive to the accuracy of the learned domain model
which probably cannot be used to solve real planning problems. In this paper,
to represent domain models in a vectorization representation way, we propose a
novel framework based on graph neural network (GNN) integrating model-free
learning and model-based planning, called LP-GNN. By embedding propositions and
actions in a graph, the latent relationship between them is explored to form a
domain-specific heuristics. We evaluate our approach on five classical planning
domains, comparing with the classical domain-model learner ARMS. The
experimental results show that the domain models learned by our approach are
much more effective on solving real planning problems.
| [
{
"version": "v1",
"created": "Fri, 19 Jul 2019 02:53:09 GMT"
}
] | 1,563,753,600,000 | [
[
"Xiao",
"Zhanhao",
""
],
[
"Wan",
"Hai",
""
],
[
"Zhuo",
"Hankui Hankz",
""
],
[
"Lin",
"Jinxia",
""
],
[
"Liu",
"Yanan",
""
]
] |
1907.08424 | Mario Alviano | Mario Alviano, Nicola Leone, Pierfrancesco Veltri, Jessica Zangari | Enhancing magic sets with an application to ontological reasoning | Paper presented at the 35th International Conference on Logic
Programming (ICLP 2019), Las Cruces, New Mexico, USA, 20-25 September 2019,
16 pages | Theory and Practice of Logic Programming 19 (2019) 654-670 | 10.1017/S1471068419000115 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Magic sets are a Datalog to Datalog rewriting technique to optimize query
answering. The rewritten program focuses on a portion of the stable model(s) of
the input program which is sufficient to answer the given query. However, the
rewriting may introduce new recursive definitions, which can involve even
negation and aggregations, and may slow down program evaluation. This paper
enhances the magic set technique by preventing the creation of (new) recursive
definitions in the rewritten program. It turns out that the new version of
magic sets is closed for Datalog programs with stratified negation and
aggregations, which is very convenient to obtain efficient computation of the
stable model of the rewritten program. Moreover, the rewritten program is
further optimized by the elimination of subsumed rules and by the efficient
handling of the cases where binding propagation is lost. The research was
stimulated by a challenge on the exploitation of Datalog/\textsc{dlv} for
efficient reasoning on large ontologies. All proposed techniques have been
hence implemented in the \textsc{dlv} system, and tested for ontological
reasoning, confirming their effectiveness.
Under consideration for publication in Theory and Practice of Logic
Programming.
| [
{
"version": "v1",
"created": "Fri, 19 Jul 2019 09:31:26 GMT"
}
] | 1,582,070,400,000 | [
[
"Alviano",
"Mario",
""
],
[
"Leone",
"Nicola",
""
],
[
"Veltri",
"Pierfrancesco",
""
],
[
"Zangari",
"Jessica",
""
]
] |
1907.08584 | Arthur Szlam | Jonathan Gray, Kavya Srinet, Yacine Jernite, Haonan Yu, Zhuoyuan Chen,
Demi Guo, Siddharth Goyal, C. Lawrence Zitnick, Arthur Szlam | CraftAssist: A Framework for Dialogue-enabled Interactive Agents | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes an implementation of a bot assistant in Minecraft, and
the tools and platform allowing players to interact with the bot and to record
those interactions. The purpose of building such an assistant is to facilitate
the study of agents that can complete tasks specified by dialogue, and
eventually, to learn from dialogue interactions.
| [
{
"version": "v1",
"created": "Fri, 19 Jul 2019 17:25:07 GMT"
}
] | 1,563,753,600,000 | [
[
"Gray",
"Jonathan",
""
],
[
"Srinet",
"Kavya",
""
],
[
"Jernite",
"Yacine",
""
],
[
"Yu",
"Haonan",
""
],
[
"Chen",
"Zhuoyuan",
""
],
[
"Guo",
"Demi",
""
],
[
"Goyal",
"Siddharth",
""
],
[
"Zitnick",
"C. Lawrence",
""
],
[
"Szlam",
"Arthur",
""
]
] |
1907.08647 | Daniel Karapetyan Dr | Olegs Nalivajevs and Daniel Karapetyan | Conditional Markov Chain Search for the Generalised Travelling Salesman
Problem for Warehouse Order Picking | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Generalised Travelling Salesman Problem (GTSP) is a well-known problem
that, among other applications, arises in warehouse order picking, where each
stock is distributed between several locations -- a typical approach in large
modern warehouses. However, the instances commonly used in the literature have
a completely different structure, and the methods are designed with those
instances in mind. In this paper, we give a new pseudo-random instance
generator that reflects the warehouse order picking and publish new benchmark
testbeds. We also use the Conditional Markov Chain Search framework to
automatically generate new GTSP metaheuristics trained specifically for
warehouse order picking. Finally, we report the computational results of our
metaheuristics to enable further competition between solvers.
| [
{
"version": "v1",
"created": "Fri, 19 Jul 2019 18:53:26 GMT"
},
{
"version": "v2",
"created": "Fri, 9 Aug 2019 17:15:19 GMT"
}
] | 1,565,568,000,000 | [
[
"Nalivajevs",
"Olegs",
""
],
[
"Karapetyan",
"Daniel",
""
]
] |
1907.08739 | Taoan Huang | Taoan Huang, Bohui Fang, Xiaohui Bei, Fei Fang | Dynamic Trip-Vehicle Dispatch with Scheduled and On-Demand Requests | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Transportation service providers that dispatch drivers and vehicles to riders
start to support both on-demand ride requests posted in real time and rides
scheduled in advance, leading to new challenges which, to the best of our
knowledge, have not been addressed by existing works. To fill the gap, we
design novel trip-vehicle dispatch algorithms to handle both types of requests
while taking into account an estimated request distribution of on-demand
requests. At the core of the algorithms is the newly proposed Constrained
Spatio-Temporal value function (CST-function), which is polynomial-time
computable and represents the expected value a vehicle could gain with the
constraint that it needs to arrive at a specific location at a given time.
Built upon CST-function, we design a randomized best-fit algorithm for
scheduled requests and an online planning algorithm for on-demand requests
given the scheduled requests as constraints. We evaluate the algorithms through
extensive experiments on a real-world dataset of an online ride-hailing
platform.
| [
{
"version": "v1",
"created": "Sat, 20 Jul 2019 02:45:24 GMT"
}
] | 1,563,840,000,000 | [
[
"Huang",
"Taoan",
""
],
[
"Fang",
"Bohui",
""
],
[
"Bei",
"Xiaohui",
""
],
[
"Fang",
"Fei",
""
]
] |
1907.10054 | Benoit Vuillemin | Benoit Vuillemin (LIRIS), Lionel Delphin-Poulat (FTR&D), Rozenn Nicol,
La\"etitia Matignon (SMA), Salima Hassas (MSI) | TSRuleGrowth : Extraction de r\`egles de pr\'ediction semi-ordonn\'ees
\`a partir d'une s\'erie temporelle d'\'el\'ements discrets, application dans
un contexte d'intelligence ambiante | in French. Conf\'erence Nationale sur les Applications Pratiques de
l'Intelligence Artificielle (APIA), Jul 2019, Toulouse, France | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a new algorithm: TSRuleGrowth, looking for
partially-ordered rules over a time series. This algorithm takes principles
from the state of the art of rule mining and applies them to time series via a
new notion of support. We apply this algorithm to real data from a connected
environment, which extract user habits through different connected objects.
| [
{
"version": "v1",
"created": "Tue, 23 Jul 2019 09:17:47 GMT"
}
] | 1,564,012,800,000 | [
[
"Vuillemin",
"Benoit",
"",
"LIRIS"
],
[
"Delphin-Poulat",
"Lionel",
"",
"FTR&D"
],
[
"Nicol",
"Rozenn",
"",
"SMA"
],
[
"Matignon",
"Laëtitia",
"",
"SMA"
],
[
"Hassas",
"Salima",
"",
"MSI"
]
] |
1907.11971 | Per-Arne Andersen | Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo | Towards Model-based Reinforcement Learning for Industry-near
Environments | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep reinforcement learning has over the past few years shown great potential
in learning near-optimal control in complex simulated environments with little
visible information. Rainbow (Q-Learning) and PPO (Policy Optimisation) have
shown outstanding performance in a variety of tasks, including Atari 2600,
MuJoCo, and Roboschool test suite. While these algorithms are fundamentally
different, both suffer from high variance, low sample efficiency, and
hyperparameter sensitivity that in practice, make these algorithms a no-go for
critical operations in the industry.
On the other hand, model-based reinforcement learning focuses on learning the
transition dynamics between states in an environment. If these environment
dynamics are adequately learned, a model-based approach is perhaps the most
sample efficient method for learning agents to act in an environment optimally.
The traits of model-based reinforcement are ideal for real-world environments
where sampling is slow and for mission-critical operations. In the warehouse
industry, there is an increasing motivation to minimise time and to maximise
production. Currently, autonomous agents act suboptimally using handcrafted
policies for significant portions of the state-space.
In this paper, we present The Dreaming Variational Autoencoder v2 (DVAE-2), a
model-based reinforcement learning algorithm that increases sample efficiency,
hence enable algorithms with low sample efficiency function better in
real-world environments. We introduce Deep Warehouse, a simulated environment
for industry-near testing of autonomous agents in grid-based warehouses.
Finally, we illustrate that DVAE-2 improves the sample efficiency for the Deep
Warehouse compared to model-free methods.
| [
{
"version": "v1",
"created": "Sat, 27 Jul 2019 20:05:52 GMT"
}
] | 1,564,444,800,000 | [
[
"Andersen",
"Per-Arne",
""
],
[
"Goodwin",
"Morten",
""
],
[
"Granmo",
"Ole-Christoffer",
""
]
] |
1907.12047 | Avi Segal | Avi Segal, Kobi Gal, Guy Shani, Bracha Shapira | A difficulty ranking approach to personalization in E-learning | null | International Journal of Human-Computer Studies, Volume 130,
October 2019, Pages 261-272 | 10.1016/j.ijhcs.2019.07.002 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The prevalence of e-learning systems and on-line courses has made educational
material widely accessible to students of varying abilities and backgrounds.
There is thus a growing need to accommodate for individual differences in
e-learning systems. This paper presents an algorithm called EduRank for
personalizing educational content to students that combines a collaborative
filtering algorithm with voting methods. EduRank constructs a difficulty
ranking for each student by aggregating the rankings of similar students using
different aspects of their performance on common questions. These aspects
include grades, number of retries, and time spent solving questions. It infers
a difficulty ranking directly over the questions for each student, rather than
ordering them according to the student's predicted score. The EduRank algorithm
was tested on two data sets containing thousands of students and a million
records. It was able to outperform the state-of-the-art ranking approaches as
well as a domain expert. EduRank was used by students in a classroom activity,
where a prior model was incorporated to predict the difficulty rankings of
students with no prior history in the system. It was shown to lead students to
solve more difficult questions than an ordering by a domain expert, without
reducing their performance.
| [
{
"version": "v1",
"created": "Sun, 28 Jul 2019 08:54:06 GMT"
}
] | 1,564,444,800,000 | [
[
"Segal",
"Avi",
""
],
[
"Gal",
"Kobi",
""
],
[
"Shani",
"Guy",
""
],
[
"Shapira",
"Bracha",
""
]
] |
1907.12344 | Paul Ogris | Thomas Eiter, Paul Ogris, Konstantin Schekotihin | A Distributed Approach to LARS Stream Reasoning (System paper) | 16 pages. Under consideration for acceptance in TPLP | Theory and Practice of Logic Programming 19 (2019) 974-989 | 10.1017/S1471068419000309 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stream reasoning systems are designed for complex decision-making from
possibly infinite, dynamic streams of data. Modern approaches to stream
reasoning are usually performing their computations using stand-alone solvers,
which incrementally update their internal state and return results as the new
portions of data streams are pushed. However, the performance of such
approaches degrades quickly as the rates of the input data and the complexity
of decision problems are growing. This problem was already recognized in the
area of stream processing, where systems became distributed in order to
allocate vast computing resources provided by clouds. In this paper we propose
a distributed approach to stream reasoning that can efficiently split
computations among different solvers communicating their results over data
streams. Moreover, in order to increase the throughput of the distributed
system, we suggest an interval-based semantics for the LARS language, which
enables significant reductions of network traffic. Performed evaluations
indicate that the distributed stream reasoning significantly outperforms
existing stand-alone LARS solvers when the complexity of decision problems and
the rate of incoming data are increasing. Under consideration for acceptance in
Theory and Practice of Logic Programming.
| [
{
"version": "v1",
"created": "Mon, 29 Jul 2019 11:39:05 GMT"
}
] | 1,582,070,400,000 | [
[
"Eiter",
"Thomas",
""
],
[
"Ogris",
"Paul",
""
],
[
"Schekotihin",
"Konstantin",
""
]
] |
1907.12501 | Matthias Knorr | Matti Berthold, Ricardo Gon\c{c}alves, Matthias Knorr, Jo\~ao Leite | A Syntactic Operator for Forgetting that Satisfies Strong Persistence | Paper presented at the 35th International Conference on Logic
Programming (ICLP 2019), Las Cruces, New Mexico, USA, 20-25 September 2019,
16 pages | Theory and Practice of Logic Programming 19 (2019) 1038-1055 | 10.1017/S1471068419000346 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Whereas the operation of forgetting has recently seen a considerable amount
of attention in the context of Answer Set Programming (ASP), most of it has
focused on theoretical aspects, leaving the practical issues largely untouched.
Recent studies include results about what sets of properties operators should
satisfy, as well as the abstract characterization of several operators and
their theoretical limits. However, no concrete operators have been
investigated.
In this paper, we address this issue by presenting the first concrete
operator that satisfies strong persistence - a property that seems to best
capture the essence of forgetting in the context of ASP - whenever this is
possible, and many other important properties. The operator is syntactic,
limiting the computation of the forgetting result to manipulating the rules in
which the atoms to be forgotten occur, naturally yielding a forgetting result
that is close to the original program.
This paper is under consideration for acceptance in TPLP.
| [
{
"version": "v1",
"created": "Mon, 29 Jul 2019 16:03:48 GMT"
},
{
"version": "v2",
"created": "Wed, 31 Jul 2019 11:32:06 GMT"
}
] | 1,582,070,400,000 | [
[
"Berthold",
"Matti",
""
],
[
"Gonçalves",
"Ricardo",
""
],
[
"Knorr",
"Matthias",
""
],
[
"Leite",
"João",
""
]
] |
1907.13275 | Mohan Sridharan | Rocio Gomez, Mohan Sridharan, Heather Riley | Towards a Theory of Intentions for Human-Robot Collaboration | 25 pages, 4 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The architecture described in this paper encodes a theory of intentions based
on the the key principles of non-procrastination, persistence, and
automatically limiting reasoning to relevant knowledge and observations. The
architecture reasons with transition diagrams of any given domain at two
different resolutions, with the fine-resolution description defined as a
refinement of, and hence tightly-coupled to, a coarse-resolution description.
Non-monotonic logical reasoning with the coarse-resolution description computes
an activity (i.e., plan) comprising abstract actions for any given goal. Each
abstract action is implemented as a sequence of concrete actions by
automatically zooming to and reasoning with the part of the fine-resolution
transition diagram relevant to the current coarse-resolution transition and the
goal. Each concrete action in this sequence is executed using probabilistic
models of the uncertainty in sensing and actuation, and the corresponding
fine-resolution outcomes are used to infer coarse-resolution observations that
are added to the coarse-resolution history. The architecture's capabilities are
evaluated in the context of a simulated robot assisting humans in an office
domain, on a physical robot (Baxter) manipulating tabletop objects, and on a
wheeled robot (Turtlebot) moving objects to particular places or people. The
experimental results indicate improvements in reliability and computational
efficiency compared with an architecture that does not include the theory of
intentions, and an architecture that does not include zooming for
fine-resolution reasoning.
| [
{
"version": "v1",
"created": "Wed, 31 Jul 2019 01:31:04 GMT"
}
] | 1,564,617,600,000 | [
[
"Gomez",
"Rocio",
""
],
[
"Sridharan",
"Mohan",
""
],
[
"Riley",
"Heather",
""
]
] |
1907.13305 | EPTCS | Jos\'e Luis Vilchis Medina (LIS), Pierre Siegel (LIS), Vincent Risch
(LIS), Andrei Doncescu (LAAS) | An Implementation of a Non-monotonic Logic in an Embedded Computer for a
Motor-glider | In Proceedings ICLP 2019, arXiv:1909.07646 | EPTCS 306, 2019, pp. 323-329 | 10.4204/EPTCS.306.37 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article we present an implementation of non-monotonic reasoning in an
embedded system. As a part of an autonomous motor-glider, it simulates piloting
decisions of an airplane. A real pilot must take care not only about the
information arising from the cockpit (airspeed, altitude, variometer,
compass...) but also from outside the cabin. Throughout a flight, a pilot is
constantly in communication with the control tower to follow orders, because
there is an airspace regulation to respect. In addition, if the control tower
sends orders while the pilot has an emergency, he may have to violate these
orders and airspace regulations to solve his problem (e.g. emergency landing).
On the other hand, climate changes constantly (wind, snow, hail..) and can
affect the sensors. All these cases easily lead to contradictions. Switching to
reasoning under uncertainty, a pilot must make decisions to carry out a flight.
The objective of this implementation is to validate a non-monotonic model which
allows to solve the question of incomplete and contradictory information. We
formalize the problem using default logic, a non-monotonic logic which allows
to find fixed-points in the face of contradictions. For the implementation, the
Prolog language is used in an embedded computer running at 1 GHz single core
with 512 Mb of RAM and 0.8 watts of energy consumption.
| [
{
"version": "v1",
"created": "Wed, 31 Jul 2019 04:48:56 GMT"
},
{
"version": "v2",
"created": "Fri, 20 Sep 2019 11:08:16 GMT"
}
] | 1,569,196,800,000 | [
[
"Medina",
"José Luis Vilchis",
"",
"LIS"
],
[
"Siegel",
"Pierre",
"",
"LIS"
],
[
"Risch",
"Vincent",
"",
"LIS"
],
[
"Doncescu",
"Andrei",
"",
"LAAS"
]
] |
1907.13482 | Joohyung Lee | Yi Wang and Shiqi Zhang and Joohyung Lee | Bridging Commonsense Reasoning and Probabilistic Planning via a
Probabilistic Action Language | Paper presented at the 35th International Conference on Logic
Programming (ICLP 2019), Las Cruces, New Mexico, USA, 20-25 September 2019,
16 pages. arXiv admin note: text overlap with arXiv:1904.00512 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To be responsive to dynamically changing real-world environments, an
intelligent agent needs to perform complex sequential decision-making tasks
that are often guided by commonsense knowledge. The previous work on this line
of research led to the framework called "interleaved commonsense reasoning and
probabilistic planning" (icorpp), which used P-log for representing
commmonsense knowledge and Markov Decision Processes (MDPs) or Partially
Observable MDPs (POMDPs) for planning under uncertainty. A main limitation of
icorpp is that its implementation requires non-trivial engineering efforts to
bridge the commonsense reasoning and probabilistic planning formalisms. In this
paper, we present a unified framework to integrate icorpp's reasoning and
planning components. In particular, we extend probabilistic action language
pBC+ to express utility, belief states, and observation as in POMDP models.
Inheriting the advantages of action languages, the new action language provides
an elaboration tolerant representation of POMDP that reflects commonsense
knowledge. The idea led to the design of the system pbcplus2pomdp, which
compiles a pBC+ action description into a POMDP model that can be directly
processed by off-the-shelf POMDP solvers to compute an optimal policy of the
pBC+ action description. Our experiments show that it retains the advantages of
icorpp while avoiding the manual efforts in bridging the commonsense reasoner
and the probabilistic planner.
| [
{
"version": "v1",
"created": "Wed, 31 Jul 2019 15:29:44 GMT"
}
] | 1,564,617,600,000 | [
[
"Wang",
"Yi",
""
],
[
"Zhang",
"Shiqi",
""
],
[
"Lee",
"Joohyung",
""
]
] |
1908.00112 | Jia-Huai You | David Spies, Jia-Huai You, Ryan Hayward | Domain-Independent Cost-Optimal Planning in ASP | Paper presented at the 35th International Conference on Logic
Programming (ICLP 2019), Las Cruces, New Mexico, USA, 20-25 September 2019,
16 pages | Theory and Practice of Logic Programming 19 (2019) 1124-1142 | 10.1017/S1471068419000395 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the problem of cost-optimal planning in ASP. Current ASP
planners can be trivially extended to a cost-optimal one by adding weak
constraints, but only for a given makespan (number of steps). It is desirable
to have a planner that guarantees global optimality. In this paper, we present
two approaches to addressing this problem. First, we show how to engineer a
cost-optimal planner composed of two ASP programs running in parallel. Using
lessons learned from this, we then develop an entirely new approach to
cost-optimal planning, stepless planning, which is completely free of makespan.
Experiments to compare the two approaches with the only known cost-optimal
planner in SAT reveal good potentials for stepless planning in ASP. The paper
is under consideration for acceptance in TPLP.
| [
{
"version": "v1",
"created": "Wed, 31 Jul 2019 21:42:24 GMT"
}
] | 1,582,070,400,000 | [
[
"Spies",
"David",
""
],
[
"You",
"Jia-Huai",
""
],
[
"Hayward",
"Ryan",
""
]
] |
1908.00409 | Holger Ingmar Meinhardt | Holger I. Meinhardt | Deduction Theorem: The Problematic Nature of Common Practice in Game
Theory | 14 pages, 4 figures, 2 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the Deduction Theorem used in the literature of game theory to
run a purported proof by contradiction. In the context of game theory, it is
stated that if we have a proof of $\phi \vdash \varphi$, then we also have a
proof of $\phi \Rightarrow \varphi$. Hence, the proof of $\phi \Rightarrow
\varphi$ is deduced from a previously known statement. However, we argue that
one has to manage to establish that a proof exists for the clauses $\phi$ and
$\varphi$, i.e., they are known true statements in order to show that $\phi
\vdash \varphi$ is provable, and that therefore $\phi \Rightarrow \varphi$ is
provable as well. Thus, we are not allowed to assume that the clause $\phi$ or
$\varphi$ is a true statement. This leads immediately to a wrong conclusion.
Apart from this, we stress to other facts why the Deduction Theorem is not
applicable to run a proof by contradiction. Finally, we present an example from
industrial cooperation where the Deduction Theorem is not correctly applied
with the consequence that the obtained result contradicts the well-known
aggregation issue.
| [
{
"version": "v1",
"created": "Wed, 31 Jul 2019 11:49:44 GMT"
},
{
"version": "v2",
"created": "Sun, 29 Aug 2021 14:28:03 GMT"
}
] | 1,630,368,000,000 | [
[
"Meinhardt",
"Holger I.",
""
]
] |
1908.01362 | Sam Toyer | Sam Toyer, Felipe Trevizan, Sylvie Thi\'ebaux, Lexing Xie | ASNets: Deep Learning for Generalised Planning | Journal extension of AAAI'18 paper (arXiv:1709.04271) | Journal of Artificial Intelligence Research 68 (2020) 1-68 | 10.1613/jair.1.11633 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper, we discuss the learning of generalised policies for
probabilistic and classical planning problems using Action Schema Networks
(ASNets). The ASNet is a neural network architecture that exploits the
relational structure of (P)PDDL planning problems to learn a common set of
weights that can be applied to any problem in a domain. By mimicking the
actions chosen by a traditional, non-learning planner on a handful of small
problems in a domain, ASNets are able to learn a generalised reactive policy
that can quickly solve much larger instances from the domain. This work extends
the ASNet architecture to make it more expressive, while still remaining
invariant to a range of symmetries that exist in PPDDL problems. We also
present a thorough experimental evaluation of ASNets, including a comparison
with heuristic search planners on seven probabilistic and deterministic
domains, an extended evaluation on over 18,000 Blocksworld instances, and an
ablation study. Finally, we show that sparsity-inducing regularisation can
produce ASNets that are compact enough for humans to understand, yielding
insights into how the structure of ASNets allows them to generalise across a
domain.
| [
{
"version": "v1",
"created": "Sun, 4 Aug 2019 15:37:13 GMT"
},
{
"version": "v2",
"created": "Tue, 5 May 2020 15:19:06 GMT"
}
] | 1,588,723,200,000 | [
[
"Toyer",
"Sam",
""
],
[
"Trevizan",
"Felipe",
""
],
[
"Thiébaux",
"Sylvie",
""
],
[
"Xie",
"Lexing",
""
]
] |
1908.01417 | Alexander Zook | Alexander Zook, Eric Fruchter, Mark O. Riedl | Automatic Playtesting for Game Parameter Tuning via Active Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Game designers use human playtesting to gather feedback about game design
elements when iteratively improving a game. Playtesting, however, is expensive:
human testers must be recruited, playtest results must be aggregated and
interpreted, and changes to game designs must be extrapolated from these
results. Can automated methods reduce this expense? We show how active learning
techniques can formalize and automate a subset of playtesting goals.
Specifically, we focus on the low-level parameter tuning required to balance a
game once the mechanics have been chosen. Through a case study on a
shoot-`em-up game we demonstrate the efficacy of active learning to reduce the
amount of playtesting needed to choose the optimal set of game parameters for
two classes of (formal) design objectives. This work opens the potential for
additional methods to reduce the human burden of performing playtesting for a
variety of relevant design concerns.
| [
{
"version": "v1",
"created": "Sun, 4 Aug 2019 22:48:16 GMT"
}
] | 1,565,049,600,000 | [
[
"Zook",
"Alexander",
""
],
[
"Fruchter",
"Eric",
""
],
[
"Riedl",
"Mark O.",
""
]
] |
1908.01420 | Alexander Zook | Alexander Zook and Mark O. Riedl | Automatic Game Design via Mechanic Generation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Game designs often center on the game mechanics---rules governing the logical
evolution of the game. We seek to develop an intelligent system that generates
computer games. As first steps towards this goal we present a composable and
cross-domain representation for game mechanics that draws from AI planning
action representations. We use a constraint solver to generate mechanics
subject to design requirements on the form of those mechanics---what they do in
the game. A planner takes a set of generated mechanics and tests whether those
mechanics meet playability requirements---controlling how mechanics function in
a game to affect player behavior. We demonstrate our system by modeling and
generating mechanics in a role-playing game, platformer game, and combined
role-playing-platformer game.
| [
{
"version": "v1",
"created": "Sun, 4 Aug 2019 23:12:16 GMT"
}
] | 1,565,049,600,000 | [
[
"Zook",
"Alexander",
""
],
[
"Riedl",
"Mark O.",
""
]
] |
1908.01423 | Alexander Zook | Alexander Zook, Brent Harrison, Mark O. Riedl | Monte-Carlo Tree Search for Simulation-based Strategy Analysis | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Games are often designed to shape player behavior in a desired way; however,
it can be unclear how design decisions affect the space of behaviors in a game.
Designers usually explore this space through human playtesting, which can be
time-consuming and of limited effectiveness in exhausting the space of possible
behaviors. In this paper, we propose the use of automated planning agents to
simulate humans of varying skill levels to generate game playthroughs. Metrics
can then be gathered from these playthroughs to evaluate the current game
design and identify its potential flaws. We demonstrate this technique in two
games: the popular word game Scrabble and a collectible card game of our own
design named Cardonomicon. Using these case studies, we show how using
simulated agents to model humans of varying skill levels allows us to extract
metrics to describe game balance (in the case of Scrabble) and highlight
potential design flaws (in the case of Cardonomicon).
| [
{
"version": "v1",
"created": "Sun, 4 Aug 2019 23:21:00 GMT"
}
] | 1,565,049,600,000 | [
[
"Zook",
"Alexander",
""
],
[
"Harrison",
"Brent",
""
],
[
"Riedl",
"Mark O.",
""
]
] |
1908.01766 | Pavel Kraikivski | Pavel Kraikivski | Seeding the Singularity for A.I | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The singularity refers to an idea that once a machine having an artificial
intelligence surpassing the human intelligence capacity is created, it will
trigger explosive technological and intelligence growth. I propose to test the
hypothesis that machine intelligence capacity can grow autonomously starting
with an intelligence comparable to that of bacteria - microbial intelligence.
The goal will be to demonstrate that rapid growth in intelligence capacity can
be realized at all in artificial computing systems. I propose the following
three properties that may allow an artificial intelligence to exhibit a steady
growth in its intelligence capacity: (i) learning with the ability to modify
itself when exposed to more data, (ii) acquiring new functionalities (skills),
and (iii) expanding or replicating itself. The algorithms must demonstrate a
rapid growth in skills of dataprocessing and analysis and gain qualitatively
different functionalities, at least until the current computing technology
supports their scalable development. The existing algorithms that already
encompass some of these or similar properties, as well as missing abilities
that must yet be implemented, will be reviewed in this work. Future
computational tests could support or oppose the hypothesis that artificial
intelligence can potentially grow to the level of superintelligence which
overcomes the limitations in hardware by producing necessary processing
resources or by changing the physical realization of computation from using
chip circuits to using quantum computing principles.
| [
{
"version": "v1",
"created": "Sun, 4 Aug 2019 16:47:56 GMT"
}
] | 1,565,136,000,000 | [
[
"Kraikivski",
"Pavel",
""
]
] |
1908.02002 | Elad Farhi | Elad I. Farhi and Vadim Indelman | Bayesian Incremental Inference Update by Re-using Calculations from
Belief Space Planning: A New Paradigm | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inference and decision making under uncertainty are key processes in every
autonomous system and numerous robotic problems. In recent years, the
similarities between inference and decision making triggered much work, from
developing unified computational frameworks to pondering about the duality
between the two. In spite of these efforts, inference and control, as well as
inference and belief space planning (BSP) are still treated as two separate
processes. In this paper we propose a paradigm shift, a novel approach which
deviates from conventional Bayesian inference and utilizes the similarities
between inference and BSP. We make the key observation that inference can be
efficiently updated using predictions made during the decision making stage,
even in light of inconsistent data association between the two. We developed a
two staged process that implements our novel approach and updates inference
using calculations from the precursory planning phase. Using autonomous
navigation in an unknown environment along with iSAM2 efficient methodologies
as a test case, we benchmarked our novel approach against standard Bayesian
inference, both with synthetic and real-world data (KITTI dataset). Results
indicate that not only our approach improves running time by at least a factor
of two while providing the same estimation accuracy, but it also alleviates the
computational burden of state dimensionality and loop closures.
| [
{
"version": "v1",
"created": "Tue, 6 Aug 2019 08:06:06 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Jan 2021 12:53:21 GMT"
}
] | 1,609,891,200,000 | [
[
"Farhi",
"Elad I.",
""
],
[
"Indelman",
"Vadim",
""
]
] |
1908.04683 | Marin Toromanoff | Marin Toromanoff, Emilie Wirbel, Fabien Moutarde | Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the
playing field | Paper currently in review | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Consistent and reproducible evaluation of Deep Reinforcement Learning (DRL)
is not straightforward. In the Arcade Learning Environment (ALE), small changes
in environment parameters such as stochasticity or the maximum allowed play
time can lead to very different performance. In this work, we discuss the
difficulties of comparing different agents trained on ALE. In order to take a
step further towards reproducible and comparable DRL, we introduce SABER, a
Standardized Atari BEnchmark for general Reinforcement learning algorithms. Our
methodology extends previous recommendations and contains a complete set of
environment parameters as well as train and test procedures. We then use SABER
to evaluate the current state of the art, Rainbow. Furthermore, we introduce a
human world records baseline, and argue that previous claims of expert or
superhuman performance of DRL might not be accurate. Finally, we propose
Rainbow-IQN by extending Rainbow with Implicit Quantile Networks (IQN) leading
to new state-of-the-art performance. Source code is available for
reproducibility.
| [
{
"version": "v1",
"created": "Tue, 13 Aug 2019 14:55:09 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Aug 2019 09:17:13 GMT"
},
{
"version": "v3",
"created": "Thu, 22 Aug 2019 10:04:58 GMT"
},
{
"version": "v4",
"created": "Tue, 24 Sep 2019 15:13:32 GMT"
},
{
"version": "v5",
"created": "Fri, 8 Nov 2019 10:37:59 GMT"
}
] | 1,573,430,400,000 | [
[
"Toromanoff",
"Marin",
""
],
[
"Wirbel",
"Emilie",
""
],
[
"Moutarde",
"Fabien",
""
]
] |
1908.04698 | Andreas Vogelsang | Mathias Blumreiter, Joel Greenyer, Francisco Javier Chiyah Garcia,
Verena Kl\"os, Maike Schwammberger, Christoph Sommer, Andreas Vogelsang,
Andreas Wortmann | Towards Self-Explainable Cyber-Physical Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the increasing complexity of CPSs, their behavior and decisions become
increasingly difficult to understand and comprehend for users and other
stakeholders. Our vision is to build self-explainable systems that can, at
run-time, answer questions about the system's past, current, and future
behavior. As hitherto no design methodology or reference framework exists for
building such systems, we propose the MAB-EX framework for building
self-explainable systems that leverage requirements- and explainability models
at run-time. The basic idea of MAB-EX is to first Monitor and Analyze a certain
behavior of a system, then Build an explanation from explanation models and
convey this EXplanation in a suitable way to a stakeholder. We also take into
account that new explanations can be learned, by updating the explanation
models, should new and yet un-explainable behavior be detected by the system.
| [
{
"version": "v1",
"created": "Tue, 13 Aug 2019 15:17:13 GMT"
}
] | 1,566,777,600,000 | [
[
"Blumreiter",
"Mathias",
""
],
[
"Greenyer",
"Joel",
""
],
[
"Garcia",
"Francisco Javier Chiyah",
""
],
[
"Klös",
"Verena",
""
],
[
"Schwammberger",
"Maike",
""
],
[
"Sommer",
"Christoph",
""
],
[
"Vogelsang",
"Andreas",
""
],
[
"Wortmann",
"Andreas",
""
]
] |
1908.05059 | Senka Krivic | Michael Cashmore, Anna Collins, Benjamin Krarup, Senka Krivic, Daniele
Magazzeni, David Smith | Towards Explainable AI Planning as a Service | 2nd ICAPS Workshop on Explainable Planning (XAIP-2019) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Explainable AI is an important area of research within which Explainable
Planning is an emerging topic. In this paper, we argue that Explainable
Planning can be designed as a service -- that is, as a wrapper around an
existing planning system that utilises the existing planner to assist in
answering contrastive questions. We introduce a prototype framework to
facilitate this, along with some examples of how a planner can be used to
address certain types of contrastive questions. We discuss the main advantages
and limitations of such an approach and we identify open questions for
Explainable Planning as a service that identify several possible research
directions.
| [
{
"version": "v1",
"created": "Wed, 14 Aug 2019 10:25:42 GMT"
}
] | 1,565,827,200,000 | [
[
"Cashmore",
"Michael",
""
],
[
"Collins",
"Anna",
""
],
[
"Krarup",
"Benjamin",
""
],
[
"Krivic",
"Senka",
""
],
[
"Magazzeni",
"Daniele",
""
],
[
"Smith",
"David",
""
]
] |
1908.05472 | Liudmyla Nechepurenko | Viktor Voss, Liudmyla Nechepurenko, Dr. Rudi Schaefer and Steffen
Bauer | Playing a Strategy Game with Knowledge-Based Reinforcement Learning | preprint | null | 10.1007/s42979-020-0087-8 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents Knowledge-Based Reinforcement Learning (KB-RL) as a
method that combines a knowledge-based approach and a reinforcement learning
(RL) technique into one method for intelligent problem solving. The proposed
approach focuses on multi-expert knowledge acquisition, with the reinforcement
learning being applied as a conflict resolution strategy aimed at integrating
the knowledge of multiple exerts into one knowledge base.
The article describes the KB-RL approach in detail and applies the reported
method to one of the most challenging problems of current Artificial
Intelligence (AI) research, namely playing a strategy game. The results show
that the KB-RL system is able to play and complete the full FreeCiv game, and
to win against the computer players in various game settings. Moreover, with
more games played, the system improves the gameplay by shortening the number of
rounds that it takes to win the game.
Overall, the reported experiment supports the idea that, based on human
knowledge and empowered by reinforcement learning, the KB-RL system can deliver
a strong solution to the complex, multi-strategic problems, and, mainly, to
improve the solution with increased experience.
| [
{
"version": "v1",
"created": "Thu, 15 Aug 2019 09:52:51 GMT"
}
] | 1,583,193,600,000 | [
[
"Voss",
"Viktor",
""
],
[
"Nechepurenko",
"Liudmyla",
""
],
[
"Schaefer",
"Dr. Rudi",
""
],
[
"Bauer",
"Steffen",
""
]
] |
1908.05632 | Santiago Ontanon | Pavan Kantharaju, Katelyn Alderfer, Jichen Zhu, Bruce Char, Brian
Smith and Santiago Onta\~n\'on | Tracing Player Knowledge in a Parallel Programming Educational Game | 7 pages, 2 figures, published at AIIDE 2018 conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper focuses on "tracing player knowledge" in educational games.
Specifically, given a set of concepts or skills required to master a game, the
goal is to estimate the likelihood with which the current player has mastery of
each of those concepts or skills. The main contribution of the paper is an
approach that integrates machine learning and domain knowledge rules to find
when the player applied a certain skill and either succeeded or failed. This is
then given as input to a standard knowledge tracing module (such as those from
Intelligent Tutoring Systems) to perform knowledge tracing. We evaluate our
approach in the context of an educational game called "Parallel" to teach
parallel and concurrent programming with data collected from real users,
showing our approach can predict students skills with a low mean-squared error.
| [
{
"version": "v1",
"created": "Thu, 15 Aug 2019 16:46:03 GMT"
}
] | 1,565,913,600,000 | [
[
"Kantharaju",
"Pavan",
""
],
[
"Alderfer",
"Katelyn",
""
],
[
"Zhu",
"Jichen",
""
],
[
"Char",
"Bruce",
""
],
[
"Smith",
"Brian",
""
],
[
"Ontañón",
"Santiago",
""
]
] |
1908.05907 | Sven L\"offler | Sven L\"offler, Ke Liu, and Petra Hofstedt | The Regularization of Small Sub-Constraint Satisfaction Problems | Part of DECLARE 19 proceedings (arXiv:hep-lat/2795508) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a new approach on optimization of constraint
satisfaction problems (CSPs) by means of substituting sub-CSPs with locally
consistent regular membership constraints. The purpose of this approach is to
reduce the number of fails in the resolution process, to improve the inferences
made during search by the constraint solver by strengthening constraint
propagation, and to maintain the level of propagation while reducing the cost
of propagating the constraints. Our experimental results show improvements in
terms of the resolution speed compared to the original CSPs and a
competitiveness to the recent tabulation approach. Besides, our approach can be
realized in a preprocessing step, and therefore wouldn't collide with
redundancy constraints or parallel computing if implemented.
| [
{
"version": "v1",
"created": "Fri, 16 Aug 2019 09:24:45 GMT"
}
] | 1,566,172,800,000 | [
[
"Löffler",
"Sven",
""
],
[
"Liu",
"Ke",
""
],
[
"Hofstedt",
"Petra",
""
]
] |
1908.06003 | Ke Liu | Ke Liu, Sven L\"offler, and Petra Hofstedt | Exploring Properties of Icosoku by Constraint Satisfaction Approach | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Icosoku is a challenging and interesting puzzle that exhibits highly
symmetrical and combinatorial nature. In this paper, we pose the questions
derived from the puzzle, but with more difficulty and generality. In addition,
we also present a constraint programming model for the proposed questions,
which can provide the answers to our first two questions. The purpose of this
paper is to share our preliminary result and problems to encourage researchers
in both group theory and constraint communities to consider this topic further.
| [
{
"version": "v1",
"created": "Fri, 16 Aug 2019 15:08:37 GMT"
}
] | 1,566,172,800,000 | [
[
"Liu",
"Ke",
""
],
[
"Löffler",
"Sven",
""
],
[
"Hofstedt",
"Petra",
""
]
] |
1908.06183 | Anthony Rhodes | Anthony D. Rhodes | Search Algorithms for Mastermind | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | his paper presents two novel approaches to solving the classic board game
mastermind, including a variant of simulated annealing (SA) and a technique we
term maximum expected reduction in consistency (MERC). In addition, we compare
search results for these algorithms to two baseline search methods: a random,
uninformed search and the method of minimizing maximum query partition sets as
originally developed by both Donald Knuth and Peter Norvig.
| [
{
"version": "v1",
"created": "Fri, 16 Aug 2019 21:26:14 GMT"
}
] | 1,566,259,200,000 | [
[
"Rhodes",
"Anthony D.",
""
]
] |
1908.07784 | Carlo Taticchi | Stafano Bistarelli, Francesco Faloci and Carlo Taticchi | Implementing Ranking-Based Semantics in ConArg: a Preliminary Report | 10 pages, 10 figures, 4 tables | Proceedings of ICTAI 2019 | 10.1109/ICTAI.2019.00163 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | ConArg is a suite of tools that offers a wide series of applications for
dealing with argumentation problems. In this work, we present the advances we
made in implementing a ranking-based semantics, based on computational choice
power indexes, within ConArg. Such kind of semantics represents a method for
sorting the arguments of an abstract argumentation framework, according to some
preference relation. The ranking-based semantics we implement relies on
Shapley, Banzhaf, Deegan-Packel and Johnston power index, transferring well
know properties from computational social choice to argumentation framework
ranking-based semantics.
| [
{
"version": "v1",
"created": "Wed, 21 Aug 2019 10:42:19 GMT"
},
{
"version": "v2",
"created": "Tue, 27 Aug 2019 17:23:11 GMT"
}
] | 1,686,873,600,000 | [
[
"Bistarelli",
"Stafano",
""
],
[
"Faloci",
"Francesco",
""
],
[
"Taticchi",
"Carlo",
""
]
] |
1908.07827 | Suttinee Sawadsitang | Suttinee Sawadsitang, Dusit Niyato, Kongrath Suankaewmanee, Puay Siew
Tan | Re-route Package Pickup and Delivery Planning with Random Demands | 6 pages, 4 figures, 2 tables | 2019 IEEE 90th Vehicular Technology Conference: VTC2019-Fall | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, a higher competition in logistics business introduces new
challenges to the vehicle routing problem (VRP). Re-route planning, also known
as dynamic VRP, is one of the important challenges. The re-route planning has
to be performed when new customers request for deliveries while the delivery
vehicles, i.e., trucks, are serving other customers. While the re-route
planning has been studied in the literature, most of the existing works do not
consider different uncertainties. Therefore, in this paper, we propose two
systems, i.e., (i) an offline package pickup and delivery planning with
stochastic demands (PDPSD) and (ii) a re-route package pickup and delivery
planning with stochastic demands (Re-route PDPSD). Accordingly, we formulate
the PDPSD system as a two-stage stochastic optimization. We then extend the
PDPSD system to the Re-route PDPSD system with a re-route algorithm.
Furthermore, we evaluate performance of the proposed systems by using the
dataset from Solomon Benchmark suite and a real data from a Singapore logistics
1company. The results show that the PDPSD system can achieve the lower cost
than that of the baseline model. In addition, the Re-route PDPSD system can
help the supplier efficiently and successfully to serve more customers while
the trucks are already on the road.
| [
{
"version": "v1",
"created": "Wed, 24 Jul 2019 05:40:00 GMT"
}
] | 1,566,432,000,000 | [
[
"Sawadsitang",
"Suttinee",
""
],
[
"Niyato",
"Dusit",
""
],
[
"Suankaewmanee",
"Kongrath",
""
],
[
"Tan",
"Puay Siew",
""
]
] |
1908.08494 | Jonatas Chagas | Jonatas B. C. Chagas, T\'ulio A. M. Toffolo, Marcone J. F. Souza,
Manuel Iori | The double traveling salesman problem with partial last-in-first-out
loading constraints | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we introduce the Double Traveling Salesman Problem with
Partial Last-In-First-Out Loading Constraints (DTSPPL). It is a
pickup-and-delivery single-vehicle routing problem, where all pickup operations
must be performed before any delivery one because the pickup and delivery areas
are geographically separated. The vehicle collects items in the pickup area and
loads them into its container, a horizontal stack. After performing all pickup
operations, the vehicle begins delivering the items in the delivery area.
Loading and unloading operations must obey a partial Last-In-First-Out (LIFO)
policy, i.e., a version of the LIFO policy that may be violated within a given
reloading depth. The objective of the DTSPPL is to minimize the total cost,
which involves the total distance traveled by the vehicle and the number of
items that are unloaded and then reloaded due to violations of the standard
LIFO policy. We formally describe the DTSPPL through two Integer Linear
Programming (ILP) formulations and propose a heuristic algorithm based on the
Biased Random-Key Genetic Algorithm (BRKGA) to find high-quality solutions. The
performance of the proposed solution approaches is assessed over a broad set of
instances. Computational results have shown that both ILP formulations have
been able to solve only the smaller instances, whereas the BRKGA obtained good
quality solutions for almost all instances, requiring short computational
times.
| [
{
"version": "v1",
"created": "Thu, 22 Aug 2019 17:02:13 GMT"
},
{
"version": "v2",
"created": "Sat, 5 Sep 2020 15:10:39 GMT"
}
] | 1,599,523,200,000 | [
[
"Chagas",
"Jonatas B. C.",
""
],
[
"Toffolo",
"Túlio A. M.",
""
],
[
"Souza",
"Marcone J. F.",
""
],
[
"Iori",
"Manuel",
""
]
] |
1908.09800 | Hankz Hankui Zhuo | Hankz Hankui Zhuo, Jing Peng, Subbarao Kambhampati | Learning Action Models from Disordered and Noisy Plan Traces | 8 pages | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | There is increasing awareness in the planning community that the burden of
specifying complete domain models is too high, which impedes the applicability
of planning technology in many real-world domains. Although there have many
learning systems that help automatically learning domain models, most existing
work assumes that the input traces are completely correct. A more realistic
situation is that the plan traces are disordered and noisy, such as plan traces
described by natural language. In this paper we propose and evaluate an
approach for doing this. Our approach takes as input a set of plan traces with
disordered actions and noise and outputs action models that can best explain
the plan traces. We use a MAX-SAT framework for learning, where the constraints
are derived from the given plan traces. Unlike traditional action models
learners, the states in plan traces can be partially observable and noisy as
well as the actions in plan traces can be disordered and parallel. We
demonstrate the effectiveness of our approach through a systematic empirical
evaluation with both IPC domains and the real-world dataset extracted from
natural language documents.
| [
{
"version": "v1",
"created": "Mon, 26 Aug 2019 17:00:32 GMT"
},
{
"version": "v2",
"created": "Mon, 9 Sep 2019 08:09:00 GMT"
}
] | 1,568,073,600,000 | [
[
"Zhuo",
"Hankz Hankui",
""
],
[
"Peng",
"Jing",
""
],
[
"Kambhampati",
"Subbarao",
""
]
] |
1908.10255 | Andreas Gerken | Andreas Gerken, Michael Spranger | Continuous Value Iteration (CVI) Reinforcement Learning and Imaginary
Experience Replay (IER) for learning multi-goal, continuous action and state
space controllers | Published in 2019 International Conference on Robotics and Automation
(ICRA) 20-24 May 2019 | null | 10.1109/ICRA.2019.8794347 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a novel model-free Reinforcement Learning algorithm for
learning behavior in continuous action, state, and goal spaces. The algorithm
approximates optimal value functions using non-parametric estimators. It is
able to efficiently learn to reach multiple arbitrary goals in deterministic
and nondeterministic environments. To improve generalization in the goal space,
we propose a novel sample augmentation technique. Using these methods, robots
learn faster and overall better controllers. We benchmark the proposed
algorithms using simulation and a real-world voltage controlled robot that
learns to maneuver in a non-observable Cartesian task space.
| [
{
"version": "v1",
"created": "Tue, 27 Aug 2019 15:00:53 GMT"
}
] | 1,566,950,400,000 | [
[
"Gerken",
"Andreas",
""
],
[
"Spranger",
"Michael",
""
]
] |
1908.10345 | Yingjie Hu | Yingjie Hu, Wenwen Li, Dawn Wright, Orhun Aydin, Daniel Wilson, Omar
Maher, Mansour Raad | Artificial Intelligence Approaches | 12 pages, 5 figures | Artificial Intelligence Approaches. The Geographic Information
Science & Technology Body of Knowledge (3rd Quarter 2019 Edition), John P.
Wilson (ed.) | 10.22224/gistbok/2019.3.4 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial Intelligence (AI) has received tremendous attention from academia,
industry, and the general public in recent years. The integration of geography
and AI, or GeoAI, provides novel approaches for addressing a variety of
problems in the natural environment and our human society. This entry briefly
reviews the recent development of AI with a focus on machine learning and deep
learning approaches. We discuss the integration of AI with geography and
particularly geographic information science, and present a number of GeoAI
applications and possible future directions.
| [
{
"version": "v1",
"created": "Tue, 27 Aug 2019 17:36:27 GMT"
}
] | 1,566,950,400,000 | [
[
"Hu",
"Yingjie",
""
],
[
"Li",
"Wenwen",
""
],
[
"Wright",
"Dawn",
""
],
[
"Aydin",
"Orhun",
""
],
[
"Wilson",
"Daniel",
""
],
[
"Maher",
"Omar",
""
],
[
"Raad",
"Mansour",
""
]
] |
1908.11494 | Xinyang Gu | Jingbin Liu, Xinyang Gu, Shuai Liu | Reinforcement learning with world model | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nowadays, model-free reinforcement learning algorithms have achieved
remarkable performance on many decision making and control tasks, but high
sample complexity and low sample efficiency still hinder the wide use of
model-free reinforcement learning algorithms. In this paper, we argue that if
we intend to design an intelligent agent that learns fast and transfers well,
the agent must be able to reflect key elements of intelligence, like intuition,
Memory, PredictionandCuriosity. We propose an agent framework that integrates
off-policy reinforcement learning with world model learning, so as to embody
the important features of intelligence in our algorithm design. We adopt the
state-of-art model-free reinforcement learning algorithm, Soft Actor-Critic, as
the agent intuition, and world model learning through RNN to endow the agent
with memory, curiosity, and the ability to predict. We show that these ideas
can work collaboratively with each other and our agent (RMC) can give new
state-of-art results while maintaining sample efficiency and training
stability. Moreover, our agent framework can be easily extended from MDP to
POMDP problems without performance loss.
| [
{
"version": "v1",
"created": "Fri, 30 Aug 2019 00:29:32 GMT"
},
{
"version": "v2",
"created": "Tue, 3 Sep 2019 04:25:25 GMT"
},
{
"version": "v3",
"created": "Wed, 11 Sep 2019 02:31:44 GMT"
},
{
"version": "v4",
"created": "Mon, 26 Oct 2020 05:52:25 GMT"
}
] | 1,603,756,800,000 | [
[
"Liu",
"Jingbin",
""
],
[
"Gu",
"Xinyang",
""
],
[
"Liu",
"Shuai",
""
]
] |
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