Designing Cooperation Strategy in a 3D Hunting Game Using Swarm Intelligence

Systems of distributed artificial intelligence can be powerful tools in a wide variety of practical applications. The predator-prey pursuit problem is used to confirm our hypothesis that complex surrounding behavior may emerge from simple, implicit defined interactions among the predator agents. The emergent behavior, is also the most answerable for the difficulty in projecting these systems. This work proposes a tool capable to generate individual strategies for the elements of a multi-agent system and thereof providing simple mechanism of implicit interaction and no explicit communications among the predator agents. A synthesis of system strategies was implemented of which internal mechanism involves the integration between simulators by particle swarm optimization algorithm (PSO), a swarm intelligence technique. The system had been tested in several simulation settings and it was capable to synthesize automatically successful hunting strategies, substantiating that the developed tool can provide, as long as it works with well- elaborated patterns, satisfactory solutions for problems of complex nature and of difficult resolution starting from analytical approaches.