ANTS: Agents on Networks, Trees, and Subgraphs

Abstract Efficient exploration of large networks is a central issue in data mining and network maintenance applications. In most existing work there is a distinction between the active ‘searcher’ which both executes the algorithm and holds the memory and the passive ‘searched graph’ over which the searcher has no control at all. Large dynamic networks like the Internet, where the nodes are powerful computers and the links have narrow bandwidth and are heavily-loaded, call for a different paradigm, in which a noncentralized group of one or more lightweight autonomous agents traverse the network in a completely distributed and parallelizable way. Potential advantages of such a paradigm would be fault tolerance against network and agent failures, and reduced load on the busy nodes due to the small amount of memory and computing resources required by the agent in each node. Algorithms for network covering based on this paradigm could be used in today’s Internet as a support for data mining and network control algorithms. Recently, a vertex ant walk ( VAW ) method has been suggested [I.A. Wagner, M. Lindenbaum, A.M. Bruckstein, Ann. Math. Artificial Intelligence 24 (1998) 211–223] for searching an undirected, connected graph by an a(ge)nt that walks along the edges of the graph, occasionally leaving ‘pheromone’ traces at nodes, and using those traces to guide its exploration. It was shown there that the ant can cover a static graph within time nd , where n is the number of vertices and d the diameter of the graph. In this work we further investigate the performance of the VAW method on dynamic graphs, where edges may appear or disappear during the search process. In particular we prove that (a) if a certain spanning subgraph S is stable during the period of covering, then the VAW method is guaranteed to cover the graph within time nd s , where d s is the diameter of S , and (b) if a failure occurs on each edge with probability p , then the expected cover time is bounded from above by nd(( log Δ / log (1/p))+((1+p)/(1−p))) , where Δ is the maximum vertex degree in the graph. We also show that (c) if G is a static tree then it is covered within time 2 n .

[1]  J. Hopcroft,et al.  Algorithm 447: efficient algorithms for graph manipulation , 1973, CACM.

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

[3]  Uriel Feige,et al.  Short random walks on graphs , 1993, SIAM J. Discret. Math..

[4]  Barbara Webb,et al.  Swarm Intelligence: From Natural to Artificial Systems , 2002, Connect. Sci..

[5]  Shmuel Gal,et al.  Search in a Maze , 1990 .

[6]  Robert E. Tarjan,et al.  Depth-First Search and Linear Graph Algorithms , 1972, SIAM J. Comput..

[7]  Giles,et al.  Searching the world wide Web , 1998, Science.

[8]  Richard J. Lipton,et al.  Random walks, universal traversal sequences, and the complexity of maze problems , 1979, 20th Annual Symposium on Foundations of Computer Science (sfcs 1979).

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

[10]  Israel A. Wagner,et al.  Smell as a Computational Resource - A Lesson We Can Learn from the Ant , 1996, ISTCS.

[11]  J. Hopcroft,et al.  Efficient algorithms for graph manipulation , 1971 .

[12]  Eli Upfal,et al.  Trading Space for Time in Undirected s-t Connectivity , 1994, SIAM J. Comput..

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

[14]  Israel A. Wagner,et al.  Distributed covering by ant-robots using evaporating traces , 1999, IEEE Trans. Robotics Autom..

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

[16]  A. Fraenkel Economic Traversal of Labyrinths , 1970 .

[17]  G. Di Caro,et al.  Ant colony optimization: a new meta-heuristic , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

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

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

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

[21]  D. Sofge THE ROLE OF EXPLORATION IN LEARNING CONTROL , 1992 .

[22]  S. Thrun Eecient Exploration in Reinforcement Learning , 1992 .

[23]  Marco Dorigo,et al.  The ant colony optimization meta-heuristic , 1999 .

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

[25]  Eli Upfal,et al.  Trading space for time in undirected s-t connectivity , 1989, STOC '89.

[26]  Sebastian Thrun,et al.  The role of exploration in learning control , 1992 .

[27]  Thomas Stützle,et al.  ACO algorithms for the quadratic assignment problem , 1999 .

[28]  Sven Koenig,et al.  Graph learning with a nearest neighbor approach , 1996, COLT '96.