Accomplishable Tasks in Knowledge Representation
This addresses a limitation in traditional KR for AI agents by allowing more expressive task-based representations, though it appears incremental as it builds on existing logical frameworks.
The paper tackles the problem of representing both accomplished and accomplishable tasks in Knowledge Representation (KR) by proposing a new approach based on Computability Logic, which enables building sophisticated KRs for agents not supported by previous logical languages.
Knowledge Representation (KR) is traditionally based on the logic of facts, expressed in boolean logic. However, facts about an agent can also be seen as a set of accomplished tasks by the agent. This paper proposes a new approach to KR: the notion of task logical KR based on Computability Logic. This notion allows the user to represent both accomplished tasks and accomplishable tasks by the agent. This notion allows us to build sophisticated KRs about many interesting agents, which have not been supported by previous logical languages.