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Comparing real-time and incremental heuristic search for real-time situated agents

Sven Koenig, Xiaoxun Sun
2008 Autonomous Agents and Multi-Agent Systems  
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  ...  developed, resulting in the first comparison of real-time and incremental heuristic search in the literature.  ...  Acknowledgments We thank Xiaoming Zheng for his suggestions regarding one of the proofs. We thank Vadim Bulitko for making a map from the real-time game Baldur's Gate II available to us.  ... 
doi:10.1007/s10458-008-9061-x fatcat:msfy72fqrfdgfphf6ybvanidym

A Survey and Classification of A* Based Best-First Heuristic Search Algorithms [chapter]

Luis Henrique Oliveira Rios, Luiz Chaimowicz
2010 Lecture Notes in Computer Science  
We organize them into five classes according to their objectives and characteristics: incremental, memoryconcerned, parallel, anytime, and real-time.  ...  For each class, we discuss its main characteristics and applications and present the most representative algorithms.  ...  The Real-Time Class The real-time class, also known as local search or agent-centered search [27] , groups the algorithms that can search in the presence of time constraints.  ... 
doi:10.1007/978-3-642-16138-4_26 fatcat:2p6emnp3jfdctensjfyk7vd5ma

Escaping Depressions in LRTS Based on Incremental Refinement of Encoded Quad-Trees

Yue Hu, Long Qin, Quanjun Yin, Lin Sun
2017 Mathematical Problems in Engineering  
Though it satisfies the requirement of quick response to users' commands and environmental changes, learning real-time search (LRTS) suffers from the heuristic depressions where agents behave irrationally  ...  In the context of robot navigation, game AI, and so on, real-time search is extensively used to undertake motion planning.  ...  The authors appreciate the fruitful discussion with the Sim812 Group: Yong Peng, Shiguang Yue, and Qi Zhang.  ... 
doi:10.1155/2017/1850678 fatcat:4ak5ztp6kjh5nkdfzdalputlbm

Are Many Reactive Agents Better Than a Few Deliberative Ones?

Kevin Knight
1993 International Joint Conference on Artificial Intelligence  
This is because solution quality improves rapidly as more reactive agents are added, but search time only increases linearly.  ...  One way to generate incrementally more efficient solutions is to be incrementally more deliberative, e.g., to increase the amount of mental search between actions.  ...  Thanks to Yolanda Gil, Milind Tambe, and Gary Knight for suggestions and assistance.  ... 
dblp:conf/ijcai/Knight93 fatcat:3s7y6m7dxvdkbiqoc7kcnmpsaa

Using a Goal-Agenda and Committed Actions in Real-Time Planning

Damien Pellier, Bruno Bouzy, Marc Metivier
2011 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence  
In the context of real-time planning, this paper investigates the contributions of two enhancements for selecting actions.  ...  Experimental results, performed on classical planning problems, show that agenda-planning and committed actions are clear advantages in the real-time context.  ...  We use the non-admissible heuristic function of FF [10] to drive the search. The use of non-admissible heuristics for real-time search is not new [15] .  ... 
doi:10.1109/ictai.2011.20 dblp:conf/ictai/PellierBM11 fatcat:6kksmp5v2fcyzamw5i3xxv24aa

Real-Time Edge Follow: A Real-Time Path Search Approach

Cagatay Undeger, Faruk Polat
2007 IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)  
Index Terms-Path planning, real-time heuristic search.  ...  In this paper, we address the problem of real-time path search for grid-type environments; we propose an effective heuristic method, namely a real-time edge follow alternative reduction method (RTEF-ARM  ...  LRTA* [14] introduced by Korf is one of the real-time heuristic search algorithms for fixed goals.  ... 
doi:10.1109/tsmcc.2007.900663 fatcat:kqvorg4mqzb55m3crcxfe4nsdm

Real-Time Heuristic Search with a Priority Queue

D. Chris Rayner, Katherine Davison, Vadim Bulitko, Kenneth Anderson, Jieshan Lu
2007 International Joint Conference on Artificial Intelligence  
Learning real-time search, which interleaves planning and acting, allows agents to learn from multiple trials and respond quickly.  ...  We introduce Prioritized-LRTA* (P-LRTA*), a learning real-time search algorithm based on Prioritized Sweeping.  ...  We would also like to thank Mitja Lustrek, David Thue, and several anonymous reviewers for their helpful comments. This work was supported by NSERC and iCORE.  ... 
dblp:conf/ijcai/RaynerDBAL07 fatcat:tyqzmtub25eipdw5zswajce42u

A general programming language for unified planning and control

Richard Levinson
1995 Artificial Intelligence  
In this paper, we consider how AI planning techniques can improve the ability of real-time control software to operate in unexpected situations. *  ...  This paper presents a method for embedding predictive search techniques within a generalpurpose programming language.  ...  Special thanks to Keith for listening to these ideas for a long time, and for providing essential advice about how to present it coherently.  ... 
doi:10.1016/0004-3702(94)00075-c fatcat:gv3wkvv2brftlhv4gvefiisbly

Making Good Decisions Quickly

Sven Koenig
2012 The IEEE intelligent informatics bulletin  
To illustrate this point, we give a broad overview of our ongoing research on search and planning (with a large number of students and colleagues, both at the University of Southern California and elsewhere  ...  For example, we describe how to combine ideas from artificial intelligence, operations research, and utility theory to create the foundations for building decision support systems that fit the risk preferences  ...  To make search fast, one can use heuristic search methods with limited lookahead (agent-centered search, such as real-time heuristic search [30] ) or heuristic search methods that reuse information from  ... 
dblp:journals/cib/Koenig12 fatcat:ua3jr52sxfhtviadlthpgim2ma

Adaptive A

Sven Koenig, Maxim Likhachev
2005 Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems - AAMAS '05  
We describe an incremental version of A*, called Adaptive A*, that solves series of similar search problems faster than running A* repeatedly from scratch because it updates its heuristics between search  ...  It is simpler than other incremental versions of A* and thus likely easier to extend and adapt to new applications.  ...  These real-time situated agents often do not know the terrain in advance but automatically observe it within a certain range around them and then remember it for future use.  ... 
doi:10.1145/1082473.1082748 dblp:conf/atal/KoenigL05 fatcat:zv67lepz25bivj6lxlfp2sneym

Incremental Refinement of Solutions for Dynamic Multi Objective Optimization Problems

Carlos E. Mariano-Romero, Eduardo F. Morales M.
2007 2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI)  
A finer granularity on the states creates more precise solutions but at the expense of a larger search space, and consequently the need for more computational resources.  ...  A group of agents explore this state space and are able to find Pareto sets applying a distributed reinforcement learning algorithm.  ...  This situation could be presented if: a) the heuristic has a reasonably recovery time for all type of landscape changes; b) The global maximum fitness can be assumed to be restricted to a relatively small  ... 
doi:10.1109/micai.2007.47 fatcat:3un5b56kifgedm3bdclhl4jzta

Real-time adaptive A*

Sven Koenig, Maxim Likhachev
2006 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems - AAMAS '06  
Characters in real-time computer games need to move smoothly and thus need to search in real time.  ...  trajectories of smaller cost for given time limits per search episode than a recently proposed real-time heuristic search method (Koenig 2004) that is more difficult to implement.  ...  The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations  ... 
doi:10.1145/1160633.1160682 dblp:conf/atal/KoenigL06 fatcat:yqj4yzjl6ne6pbq32au4r7exxi

Dynamic Control in Real-Time Heuristic Search

V. Bulitko, M. Lustrek, J. Schaeffer, Y. Bjornsson, S. Sigmundarson
2008 The Journal of Artificial Intelligence Research  
Real-time heuristic search is a challenging type of agent-centered search because the agent's planning time per action is bounded by a constant independent of problem size.  ...  In this paper, we extend classic and modern real-time search algorithms with an automated mechanism for dynamic depth and subgoal selection. The new algorithms remain real-time and complete.  ...  Introduction In this paper we study the problem of agent-centered real-time heuristic search (Koenig, 2001) .  ... 
doi:10.1613/jair.2497 fatcat:zkscsw2epzfr3ihzcjkw7ovrce

A Hybrid Path-Planning Strategy for Mobile Robots with Limited Sensor Capabilities

Guilherme de Oliveira, Kevin de Carvalho, Alexandre Brandão
2019 Sensors  
For the real time obstacle avoidance task, a simple and cost-efficient local planner is used, making the algorithm a hybrid global and local planning solution.  ...  This paper introduces a strategy for the path planning problem for platforms with limited sensor and processing capabilities.  ...  Acknowledgments: The authors thank Federal University of Viçosa and Department of Electrical Engineering and Department of Informatics for all the support and infrastructure provided for the execution  ... 
doi:10.3390/s19051049 fatcat:dlbxv666vrcufjpvd6v6bo4emu

A survey of dynamic scheduling in manufacturing systems

Djamila Ouelhadj, Sanja Petrovic
2008 Journal of Scheduling  
The problem of scheduling is concerned with searching for optimal (or near-optimal) schedules subject to a number of constraints.  ...  The principles of several dynamic scheduling techniques, namely, dispatching rules, heuristics, meta-heuristics, artificial intelligence techniques, and multi-agent systems are described in detail, followed  ...  Wu et al. (1991 Wu et al. ( , 1993 compared the performance of genetic algorithms and local search heuristics to generate robust schedules.  ... 
doi:10.1007/s10951-008-0090-8 fatcat:ey7we6uyq5azdi2lcruqlxiqje
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