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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Rebel agents that adapt to goal expectation failures
Humans and autonomous agents often have differing knowledge about the world, the goals they pursue, and the actions they perform. Given these differences, an autonomous agent should be capable of rebelling against a goal when its completion would violate that agent’s preferences and motivations. Prior work on agent rebellion has examined agents that can reject actions leading to harmful consequences. Here we elaborate on a specific justification for rebellion in terms of violated goal expectations. Further, the need for rebellion is not always known in advance. So to rebel correctly and justifiably in response to unforeseen circumstances, an autonomous agent must be able to learn the reasons behind violations of its expectations. This paper provides a novel framework for rebellion within a metacognitive architecture using goal monitoring and model learning, and it includes experimental results showing the efficacy of such rebellion.
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- Award ID(s):
- 1849131
- NSF-PAR ID:
- 10349752
- Date Published:
- Journal Name:
- Proceedings of the Integrated Execution / Goal Reasoning Workshop (held at the Thirtieth International Conference on Automated Planning and Scheduling - ICAPS-20)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
