Coordination of intelligent agents in real-time search

Date

2002-05-01

Author

Cakir, A
Polat, Faruk

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

137
views

0
downloads

Search is a fundamental problem-solving method in artificial intelligence. Traditional off-line search algorithms attempt to find an optimal solution whereas real-time search algorithms try to find a suboptimal solution more quickly than traditional algorithms to meet real-time constraints. In this work, a new multi-agent real-time search algorithm is developed and its effectiveness is illustrated on a sample domain, namely maze problems. Searching agents can see their environment with a specified visual depth and hence can partially observe their environment. An agent makes use of its partial observation to select a next move, instead Of using only one-move-ahead information. Furthermore agents cooperate through a marking mechanism to be able to search different parts of the search space. When an agent selects its next move, it marks its direction of move before executing the move. When another agent comes to this position, it sees this mark and, if possible, moves in a different direction than the previously selected direction. In this way, marking helps agents coordinate their moves with other agents. Although coordination brings an overhead, from experiments we observe that this mechanism is effective in both search time and solution length in maze problems.

Subject Keywords

Control and Systems Engineering, Theoretical Computer Science, Computational Theory and Mathematics, Artificial Intelligence

URI

https://hdl.handle.net/11511/49152

Journal

EXPERT SYSTEMS

DOI

https://doi.org/10.1111/1468-0394.00193

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

THE USE OF EXPERT SYSTEM BUILDING TOOLS IN PROCESS PLANNING ESKICIOGLU, H (Elsevier BV, 1992-01-01) The use of expert systems is a break-through in solving problems which require complex decision-making based on the past experience of an expert human being rather than relying on sequential algorithms which include simple combination of logical rules and calculations. Expert system building tools are programming systems developed to facilitate the construction of expert systems in a particular domain with less effort and in a relatively shorter time. Expert system building tools are now moving from Lisp ma...
Free gait generation with reinforcement learning for a six-legged robot Erden, Mustafa Suphi; Leblebicioğlu, Mehmet Kemal (Elsevier BV, 2008-03-31) In this paper the problem of free gait generation and adaptability with reinforcement learning are addressed for a six-legged robot. Using the developed free gait generation algorithm the robot maintains to generate stable gaits according to the commanded velocity. The reinforcement learning scheme incorporated into the free gait generation makes the robot choose more stable states and develop a continuous walking pattern with a larger average stability margin. While walking in normal conditions with no ext...
AN EFFICIENT DATABASE TRANSITIVE CLOSURE ALGORITHM Toroslu, İsmail Hakkı; HENSCHEN, L (Springer Science and Business Media LLC, 1994-05-01) The integration of logic rules and relational databases has recently emerged as an important technique for developing knowledge management systems. An important class of logic rules utilized by these systems is the so-called transitive closure rules, the processing of which requires the computation of the transitive closure of database relations referenced by these rules. This article presents a new algorithm suitable for computing the transitive closure of very large database relations. This algorithm proc...
Fusion of Image Segmentations under Markov Random Fields Karadag, Ozge Oztimur; Yarman Vural, Fatoş Tunay (2014-08-28) In this study, a fast and efficient consensus segmentation method is proposed which fuses a set of baseline segmentation maps under an unsupervised Markov Random Fields (MRF) framework. The degree of consensus among the segmentation maps are estimated as the relative frequency of co-occurrences among the adjacent segments. Then, these relative frequencies are used to construct the energy function of an unsupervised MRF model. It is well-known that MRF framework is commonly used for formulating the spatial r...
Introduction to the Special Issue on Fuzzy Analytics and Stochastic Methods in Neurosciences Kropat, E.; Turkay, M.; Weber, Gerhard Wilhelm (Institute of Electrical and Electronics Engineers (IEEE), 2020-01-01) The papers in this special section examine the use of fuzzy analytics and stochastic methods in the field of neuroscience. Recent theoretical and technological advancements provide new and deeper insights into the fundamental mechanisms of information processing in the neural system. This important process is accompanied by the tremendous rise of experimental data, which are waiting for further exploration. Modern methodologies and tools from neuroimaging, brain imaging, optogenetic devices, and in vitro an...

Citation Formats

A. Cakir and F. Polat, “Coordination of intelligent agents in real-time search,” EXPERT SYSTEMS, pp. 80–87, 2002, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/49152.