Planning and Synthesis Under Assumptions
This work addresses foundational issues in automated planning and synthesis for AI agents, but it appears incremental as it builds on existing frameworks for assumptions and goals.
The paper tackles the problem of synthesizing agent plans under environmental assumptions, characterizing the synthesis problem mathematically and algorithmically for goals ranging from reachability to temporal logic specifications.
In Reasoning about Action and Planning, one synthesizes the agent plan by taking advantage of the assumption on how the environment works (that is, one exploits the environment's effects, its fairness, its trajectory constraints). In this paper we study this form of synthesis in detail. We consider assumptions as constraints on the possible strategies that the environment can have in order to respond to the agent's actions. Such constraints may be given in the form of a planning domain (or action theory), as linear-time formulas over infinite or finite runs, or as a combination of the two. We argue though that not all assumption specifications are meaningful: they need to be consistent, which means that there must exist an environment strategy fulfilling the assumption in spite of the agent actions. For such assumptions, we study how to do synthesis/planning for agent goals, ranging from a classical reachability to goal on traces specified in \LTL and \LTLf/\LDLf, characterizing the problem both mathematically and algorithmically.