Accomplishable Tasks in Knowledge Representation

TL;DR

This paper introduces a novel knowledge representation approach based on Computability Logic, enabling the depiction of both accomplished and accomplishable tasks, thus enhancing the expressiveness of agent modeling.

Contribution

It proposes task logical KR using Computability Logic, allowing richer representations of agents' capabilities beyond traditional fact-based methods.

Findings

Enables representation of accomplished and accomplishable tasks

Supports sophisticated agent modeling

Extends logical languages for knowledge representation

Abstract

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.