The Rationale behind the Concept of Goal
This work addresses a foundational problem in AI and philosophy for researchers studying agent reasoning and decision-making, but it appears incremental as it builds on existing concepts of outcomes and logic.
The paper tackles the problem of understanding motivational attitudes like desire, goal, and intention by proposing that they are facets of acceptable outcomes, and it results in a formalization in a novel Modal Defeasible Logic with proven computational feasibility.
The paper proposes a fresh look at the concept of goal and advances that motivational attitudes like desire, goal and intention are just facets of the broader notion of (acceptable) outcome. We propose to encode the preferences of an agent as sequences of "alternative acceptable outcomes". We then study how the agent's beliefs and norms can be used to filter the mental attitudes out of the sequences of alternative acceptable outcomes. Finally, we formalise such intuitions in a novel Modal Defeasible Logic and we prove that the resulting formalisation is computationally feasible.