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Autonomous agents in a multi-agent system work with each other to achieve their goals. However, In a partially observable world, current multi-agent systems are often less effective in achieving their goals. This limitation is due to the agents’ lack of reasoning about other agents and their mental states. Another factor is the agents’ inability to share required knowledge with other agents. This paper addresses the limitations by presenting a general approach for autonomous agents to work together in a multi-agent system. In this approach, an agent applies two main concepts: goal reasoning- to determine what goals to pursue and share; Theory of mind-to select an agent(s) for sharing goals and knowledge. We evaluate the performance of our multi-agent system in a Marine Life Survey Domain and compare it to another multi-agent system that randomly selects agent(s) to delegates its goals.
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Autonomous goal selection operation for agent based architectures
An intelligent agent has many tasks and goals to achieve over specific time intervals. The goals may be assigned to it or the agent may generate its own goals. In either case, the number of goals at any given time may exceed its capacity to act upon concurrently. Therefore, an agent must prioritize the goals in chronological order as per their relative importance or significance. We show how an intelligent agent can estimate the trade-off between performance gains and resource costs to make smart choices concerning the goals it intends to achieve as opposed to selecting them in an arbitrary basis. We illustrate this method within the context of an intelligent cognitive architecture that supports various agent models.
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- Award ID(s):
- 1849131
- PAR ID:
- 10352582
- Date Published:
- Journal Name:
- Proceedings of the 2021 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'21)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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