Generalized best-first search strategies and the optimality of A*
Journal of the ACM (JACM)
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Artificial Intelligence
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
Linear-space best-first search
Artificial Intelligence
Designing emergent behaviors: from local interactions to collective intelligence
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
From Tom Thumb to the Dockers: some experiments with foraging robots
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Communication in reactive multiagent robotic systems
Autonomous Robots
Ant-based load balancing in telecommunications networks
Adaptive Behavior
The power of a pebble: exploring and mapping directed graphs
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Ant algorithms for discrete optimization
Artificial Life
Ants: agents on networks, trees, and subgraphs
Future Generation Computer Systems
PHA*: performing A* in unknown physical environments
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Vertex-Ant-Walk – A robust method for efficient exploration of faulty graphs
Annals of Mathematics and Artificial Intelligence
Grounded Symbolic Communication between Heterogeneous Cooperating Robots
Autonomous Robots
Evolving Beharioral Strategies in Predators and Prey
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem
HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem
PHA*: finding the shortest path with A* in an unknown physical environment
Journal of Artificial Intelligence Research
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Multi-agent Cooperative Cleaning of Expanding Domains
International Journal of Robotics Research
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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