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Agent-Centered Search
2001
The AI Magazine
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I In this article, I describe agent-centered search (also called real-time search or local search) and illustrate this planning paradigm with examples. ...
I discuss the design and properties of several agent-centered search methods, focusing on robot exploration and localization. Articles ...
In this article, I concentrate on agent-centered search in single-agent domains. ...
doi:10.1609/aimag.v22i4.1596
dblp:journals/aim/Koenig01
fatcat:nfhb67ts2jawri2qmk3l35y7mi
Mean-based Heuristic Search for Real-Time Planning
[article]
2018
arXiv
pre-print
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In this paper, we introduce a new heuristic search algorithm based on mean values for real-time planning, called MHSP. ...
It consists in associating the principles of UCT, a bandit-based algorithm which gave very good results in computer games, and especially in Computer Go, with heuristic search in order to obtain a real-time ...
Real-Time Search RTS is considered in the context of agent-centered search. ...
arXiv:1810.09150v1
fatcat:d2mu6oh6pzajpnraokw7fhdtpm
Scrubbing During Learning In Real-time Heuristic Search
2016
The Journal of Artificial Intelligence Research
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In this paper we study best-case performance more generally and derive theoretical lower bounds for reaching the goal using LRTA*, a canonical example of a real-time agent-centered heuristic search algorithm ...
Real-time agent-centered heuristic search is a well-studied problem where an agent that can only reason locally about the world must travel to a goal location using bounded computation and memory at each ...
Introduction The framework of real-time agent-centered heuristic search models an agent with locally limited sensing and perception that is trying to reach a goal while interleaving planning and movement ...
doi:10.1613/jair.4908
fatcat:qcgurb7yqzgzrifqqyjkcsjmaa
Learning in Real-Time Search: A Unifying Framework
2006
The Journal of Artificial Intelligence Research
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In such simultaneous planning and learning problems, the agent has to select its actions in a limited amount of time, while sensing only a local part of the environment centered at the agent's current ...
Real-time heuristic search agents select actions using a limited lookahead search and evaluating the frontier states with a heuristic function. ...
We are grateful for the support from NSERC, the University of Alberta, the Alberta Ingenuity Centre for Machine Learning, and Jonathan Schaeffer. ...
doi:10.1613/jair.1789
fatcat:rksq27w5cbcyrmo2th3kyiemwi
Dyna-H: a heuristic planning reinforcement learning algorithm applied to role-playing-game strategy decision systems
[article]
2011
arXiv
pre-print
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In this paper, we propose a heuristic planning strategy to incorporate the ability of heuristic-search in path-finding into a Dyna agent. ...
The proposal was evaluated against the one-step Q-Learning and Dyna-Q algorithms obtaining excellent experimental results: Dyna-H significantly overcomes both methods in all experiments. ...
In this case study, the euclidian distance is used for the heuristic (H) planning module. ...
arXiv:1101.4003v3
fatcat:h2h2w57sencp7lnpjvv5eeklcm
On Backtracking in Real-time Heuristic Search
[article]
2009
arXiv
pre-print
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Real-time heuristic search algorithms are suitable for situated agents that need to make their decisions in constant time. ...
In this paper, we present the first entirely theoretical study of backtracking in real-time heuristic search. ...
We are grateful for financial support from the National Science and Engineering Research Council, the University of Alberta, the Alberta Ingenuity Centre for Machine Learning, and the Informatics Circles ...
arXiv:0912.3228v1
fatcat:sn7y25zmdvfa5irb2zg4ep2hhq
Abalearn: A Risk-Sensitive Approach to Self-play Learning in Abalone
[chapter]
2003
Lecture Notes in Computer Science
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Our approach is based on a reinforcement learning algorithm that is riskseeking, since defensive players in Abalone tend to never end a game. ...
We evaluate our approach using a fixed heuristic opponent as a benchmark, pitting our agents against human players online and comparing samples of our agents at different times of training. ...
The heuristic function sums the distance to the center of the board of each stone (subtracts if it's an opponent stone). ...
doi:10.1007/978-3-540-39857-8_6
fatcat:hib5wsl3vrgz5jmispttzxdx3a
Cooperative Detection of Multiple Targets by the Group of Mobile Agents
2020
Entropy
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Sensor fusion in each agent and over the agents is implemented using a general Bayesian scheme. ...
The agents are equipped with sensors with positive and non-negligible probabilities of detecting the targets at different distances. ...
agent's distance with respect to the Koopman's exponential random search Formula [2] , { | } = exp − , , , (2) where , , represents the search effort applied to the cell with respect to the distance ...
doi:10.3390/e22050512
pmid:33286284
fatcat:n3evdxnyorh23e7tselg2pgeh4
Comparing real-time and incremental heuristic search for real-time situated agents
2008
Autonomous Agents and Multi-Agent Systems
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We first develop a competitive real-time heuristic search method. LSS-LRTA* is a version of Learning Real-Time A* that uses A* to determine its local search spaces and learns quickly. ...
In this article, we compare two classes of fast heuristic search methods for these navigation tasks that speed up A* searches in different ways, namely real-time heuristic search and incremental heuristic ...
The views and conclusions contained in this document are those of the author(s) and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations ...
doi:10.1007/s10458-008-9061-x
fatcat:msfy72fqrfdgfphf6ybvanidym
Combining Deep Learning and Qualitative Spatial Reasoning to Learn Complex Structures from Sparse Examples with Noise
[article]
2018
arXiv
pre-print
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We present a novel approach to introducing new spatial structures to an AI agent, combining deep learning over qualitative spatial relations with various heuristic search algorithms. ...
the most likely set of remaining moves needed to complete it, and recommends one using heuristic search. ...
Since we are interested in the lowest heuristic distance between two sets, we use the Jaccard distance or 1 − J. 3. Levenshtein distance (LD). ...
arXiv:1811.11064v1
fatcat:yoenruc7qje3njocy5tez5ho2a
Combining Deep Learning and Qualitative Spatial Reasoning to Learn Complex Structures from Sparse Examples with Noise
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
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We present a novel approach to introducing new spatial structures to an AI agent, combining deep learning over qualitative spatial relations with various heuristic search algorithms. ...
the most likely set of remaining moves needed to complete it, and recommends one using heuristic search. ...
Since we are interested in the lowest heuristic distance between two sets, we use the Jaccard distance or 1 − J. 3. Levenshtein distance (LD). ...
doi:10.1609/aaai.v33i01.33012911
fatcat:bmns6eq67zbunfucpjgc2x7yym
A new heuristic for the quadratic assignment problem
2002
Journal of Applied Mathematics and Decision Sciences
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We propose a new heuristic for the solution of the quadratic assignment problem. The heuristic combines ideas from tabu search and genetic algorithms. ...
Run times are very short compared with other heuristic procedures. The heuristic performed very well on a set of test problems. ...
We search the solution space starting from the center solution, and each step we search solutions with increasing distance from the center solution, until we reach a prespecified search depth d ≤ n. ...
doi:10.1155/s1173912602000093
fatcat:u65tr22dnnbctd4trxulruddta
A New Heuristic for the Quadratic Assignment Problem
2002
Journal of Applied Mathematics and Decision Sciences
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We propose a new heuristic for the solution of the quadratic assignment problem. The heuristic combines ideas from tabu search and genetic algorithms. ...
Run times are very short compared with other heuristic procedures. The heuristic performed very well on a set of test problems. ...
We search the solution space starting from the center solution, and each step we search solutions with increasing distance from the center solution, until we reach a prespecified search depth d ≤ n. ...
doi:10.1207/s15327612jamd0603_1
fatcat:fugkwtzrbzaihoxhra6cu7p7ku
Determining Solution Space Characteristics for Real-Time Strategy Games and Characterizing Winning Strategies
2011
International Journal of Computer Games Technology
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Their performance allows us to draw various conclusions about applying a competing agent in complex search landscapes associated with RTS games. ...
The underlying goal of a competing agent in a discrete real-time strategy (RTS) game is to defeat an adversary. ...
Acknowledgment This investigation is a research effort of the AFIT Center for Cyberspace Research (CCR), Director: Dr. Rick Raines. ...
doi:10.1155/2011/834026
fatcat:5aowrcrryjfdjikssxbvgnzj3q
Case-Based Subgoaling in Real-Time Heuristic Search for Video Game Pathfinding
2010
The Journal of Artificial Intelligence Research
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On the downside, real-time search algorithms employ learning methods that frequently lead to poor solution quality and cause the agent to appear irrational by re-visiting the same problem states repeatedly ...
Real-time heuristic search algorithms satisfy a constant bound on the amount of planning per action, independent of problem size. As a result, they scale up well as problems become larger. ...
The learning process has precluded real-time heuristic search agents from being widely deployed for pathfinding in video games. ...
doi:10.1613/jair.3076
fatcat:rlxnj7jolvgbxi3e435jwi6d54
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