Adaptive Behavior
Emergent cooperative goal-satisfaction in large-scale automated-agent systems
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
Methods for task allocation via agent coalition formation
Artificial Intelligence
Experience-based reinforcement learning to acquire effective behavior in a multi-agent domain
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Socially intelligent reasoning for autonomous agents
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
| Hi-index | 0.00 |
In non-communicative environment, it is important for agents to assess the situation prevailing in the system, especially to anticipate other agents' intentions. In this paper, we argue in favor of cooperation among agents and propose a new method to utilize potential field as a tool for estimation of the environment. In our method, potential of environment gives agents some criteria to assess environmental situations from their own perspective. The potential of each object represents its influence on the environment and the environmental potential, i.e.,summation of each object's potential, represents global situation of the environment. Agents' decision of their behavior will be done by refining the policy obtained from potential. We use a trash collecting problem as an example to show the effectiveness of our method by some sets of experiments of the trash collecting problem. We also discuss the applicability of our method to hybrid systems or environments where agent's range of vision are limited.