Multi-agent Physical A* with Large Pheromones

Physical A* (PHA*) and its multi-agent version MAPHA* are algorithms that find the shortest path between two points in an unknown real physical environment with one or many mobile agents [A. Felner et al. Journal of Artificial Intelligence Research, 21:631–679, 2004; A. Felner et al. Proceedings of the First International Joint Conference on Autonomous Agents and Multi-Agent Systems, Bologna, Italy, 2002:240–247]. Previous work assumed a complete sharing of knowledge between agents. Here we apply this algorithm to a more restricted model of communication which we call large pheromones, where agents communicate by writing and reading data at nodes of the graph that constitutes their environment. Previous works on pheromones usually assumed that only a limited amount of data can be written at each node. The large pheromones model assumes no limitation on the size of the pheromones and thus each agent can write its entire knowledge at a node. We show that with this model of communication the behavior of a multi-agent system is almost as good as with complete knowledge sharing. Under this model we also introduce a new type of agent, a communication agent, that is responsible for spreading the knowledge among other agents by moving around the graph and copying pheromones. Experimental results show that the contribution of communication agents is rather limited as data is already spread to other agents very well with large pheromones

[1]  F R Adler,et al.  Information Collection and Spread by Networks of Patrolling Ants , 1992, The American Naturalist.

[2]  Azriel Rosenfeld,et al.  Learning in Navigation Goal Finding in Graphs , 1996, Int. J. Pattern Recognit. Artif. Intell..

[3]  Marco Dorigo,et al.  AntNet: Distributed Stigmergetic Control for Communications Networks , 1998, J. Artif. Intell. Res..

[4]  Tucker R. Balch,et al.  AuRA: principles and practice in review , 1997, J. Exp. Theor. Artif. Intell..

[5]  Louis Hugues,et al.  Collective Grounded Representations for Robots , 2000, DARS.

[6]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[7]  Tucker R. Balch,et al.  Communication in reactive multiagent robotic systems , 1995, Auton. Robots.

[8]  Alexander Zelinsky,et al.  Grounded Symbolic Communication between Heterogeneous Cooperating Robots , 2000, Auton. Robots.

[9]  Atsuyuki Okabe,et al.  Spatial Tessellations: Concepts and Applications of Voronoi Diagrams , 1992, Wiley Series in Probability and Mathematical Statistics.

[10]  Enrico Pagello,et al.  Cooperative behaviors in multi-robot systems through implicit communication , 1999, Robotics Auton. Syst..

[11]  Paul Valckenaers,et al.  Holonic and Multi-Agent Systems for Manufacturing , 2003, Lecture Notes in Computer Science.

[12]  M. C. Sinclair,et al.  Ant colony optimisation for virtual-wavelength-path routing and wavelength allocation , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[13]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[14]  R. Matthews,et al.  Ants. , 1898, Science.

[15]  Israel A. Wagner,et al.  Large Pheromones: A Case Study with Multi-agent Physical A* , 2004, ANTS Workshop.

[16]  Rina Dechter,et al.  Generalized best-first search strategies and the optimality of A* , 1985, JACM.

[17]  Israel A. Wagner,et al.  Vertex-Ant-Walk – A robust method for efficient exploration of faulty graphs , 2004, Annals of Mathematics and Artificial Intelligence.

[18]  Michael A. Bender,et al.  The power of a pebble: exploring and mapping directed graphs , 1998, STOC '98.

[19]  J. Deneubourg,et al.  Self-organized shortcuts in the Argentine ant , 1989, Naturwissenschaften.

[21]  Nathan S. Netanyahu,et al.  PHA*: Finding the Shortest Path with A* in An Unknown Physical Environment , 2011, J. Artif. Intell. Res..

[22]  Deborah M. Gordon,et al.  The expandable network of ant exploration , 1995, Animal Behaviour.

[23]  Elon Rimon,et al.  A/sub /spl epsiv//*-DFS: an algorithm for minimizing search effort in sensor based mobile robot navigation , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[24]  Yakov Rekhter,et al.  A Border Gateway Protocol 4 (BGP-4) , 1994, RFC.

[25]  Richard E. Korf,et al.  Depth-First Iterative-Deepening: An Optimal Admissible Tree Search , 1985, Artif. Intell..

[26]  Steven R. Lindsay Adaptation and learning , 2000 .

[27]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[28]  Maja J. Matarić,et al.  Designing emergent behaviors: from local interactions to collective intelligence , 1993 .

[29]  M Dorigo,et al.  Ant colonies for the quadratic assignment problem , 1999, J. Oper. Res. Soc..

[30]  M. Benda,et al.  On Optimal Cooperation of Knowledge Sources , 1985 .

[31]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[32]  David P. Dobkin,et al.  The quickhull algorithm for convex hulls , 1996, TOMS.

[33]  Alain Hertz,et al.  Ants can colour graphs , 1997 .

[34]  Richard E. Korf,et al.  Linear-Space Best-First Search , 1993, Artif. Intell..

[35]  Jacques Ferber,et al.  From Tom Thumb to the Dockers: some experiments with foraging robots , 1993 .

[36]  Israel A. Wagner,et al.  A Distributed Ant Algorithm for\protect Efficiently Patrolling a Network , 2003, Algorithmica.

[37]  J. Deneubourg,et al.  Trails and U-turns in the Selection of a Path by the Ant Lasius niger , 1992 .

[38]  Sandip Sen,et al.  Adaption and Learning in Multi-Agent Systems , 1995, Lecture Notes in Computer Science.

[39]  S. Appleby,et al.  Mobile Software Agents for Control in Telecommunications Networks , 2000 .

[40]  Sarit Kraus,et al.  PHA*: performing A* in unknown physical environments , 2002, AAMAS '02.

[41]  Israel A. Wagner,et al.  ANTS: Agents on Networks, Trees, and Subgraphs , 2000, Future Gener. Comput. Syst..

[42]  Léon J. M. Rothkrantz,et al.  Ant-Based Load Balancing in Telecommunications Networks , 1996, Adapt. Behav..

[43]  Elon Rimon,et al.  Roadmap-A*: An algorithm for minimizing travel e ort in sensor based mobile robot navigation , 1998 .

[44]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[45]  Sandip Sen,et al.  Evolving Beharioral Strategies in Predators and Prey , 1995, Adaption and Learning in Multi-Agent Systems.

[46]  Anthony Stentz,et al.  Optimal and efficient path planning for partially-known environments , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[47]  Gary Scott Malkin RIPng Protocol Applicability Statement , 1997, RFC.

[48]  Luca Maria Gambardella,et al.  HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem , 1997 .

[49]  Charles D. Schaper,et al.  Communications, Computation, Control, and Signal Processing: A Tribute to Thomas Kailath , 1997 .

[50]  Marco Dorigo,et al.  Ant system for Job-shop Scheduling , 1994 .

[51]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[52]  Ronald C. Arkin,et al.  Integrating behavioral, perceptual, and world knowledge in reactive navigation , 1990, Robotics Auton. Syst..

[53]  Richard F. Hartl,et al.  An improved Ant System algorithm for theVehicle Routing Problem , 1999, Ann. Oper. Res..

[54]  Giulio Sandini,et al.  Self-organizing collection and transport of objects in unpredictable environments , 1990 .

[55]  Richard E. Korf,et al.  Real-Time Heuristic Search , 1990, Artif. Intell..