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In multi-agent domains (MADs), an agent's action may not just change the world and the agent's knowledge and beliefs about the world, but also may change other agents' knowledge and beliefs about the world and their knowledge and beliefs about other agents' knowledge and beliefs about the world. The goals of an agent in a multi-agent world may involve manipulating the knowledge and beliefs of other agents' and again, not just their knowledge/belief about the world, but also their knowledge about other agents' knowledge about the world. Our goal is to present an action language (mA+) that has the necessary features to address the above aspects in representing and RAC in MADs. mA+ allows the representation of and reasoning about different types of actions that an agent can perform in a domain where many other agents might be present -- such as world-altering actions, sensing actions, and announcement/communication actions. It also allows the specification of agents' dynamic awareness of action occurrences which has future implications on what agents' know about the world and other agents' knowledge about the world. mA+ considers three different types of awareness: full-, partial- awareness, and complete oblivion of an action occurrence and its effects. This keeps the language simple, yet powerful enough to address a large variety of knowledge manipulation scenarios in MADs. The semantics of mA+ relies on the notion of state, which is described by a pointed Kripke model and is used to encode the agent's knowledge and the real state of the world. It is defined by a transition function that maps pairs of actions and states into sets of states. We illustrate properties of the action theories, including properties that guarantee finiteness of the set of initial states and their practical implementability. Finally, we relate mA+ to other related formalisms that contribute to RAC in MADs.
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Multi-agent goal delegation
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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- Award ID(s):
- 1849131
- PAR ID:
- 10352576
- Date Published:
- Journal Name:
- Proceedings of the 9th Goal Reasoning Workshop
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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