Knowledge-Based Programs as Plans: Succinctness and the Complexity of Plan Existence
This work addresses the problem of efficient plan representation for AI agents, but it is incremental as it builds on existing literature on planning under incomplete knowledge.
The paper tackles the problem of representing action policies in AI planning by formalizing the exponential succinctness gap between knowledge-based programs and standard plans, and it addresses the complexity of plan existence, revealing both known and new results.
Knowledge-based programs (KBPs) are high-level protocols describing the course of action an agent should perform as a function of its knowledge. The use of KBPs for expressing action policies in AI planning has been surprisingly overlooked. Given that to each KBP corresponds an equivalent plan and vice versa, KBPs are typically more succinct than standard plans, but imply more on-line computation time. Here we make this argument formal, and prove that there exists an exponential succinctness gap between knowledge-based programs and standard plans. Then we address the complexity of plan existence. Some results trivially follow from results already known from the literature on planning under incomplete knowledge, but many were unknown so far.