Automata-Theoretic Synthesis of Plans and Reactive Strategies

Sequential decision making is a central problem in Artificial Intelligence (AI) with applications ranging from the automated synthesis of plans for conversational agents to the synthesis of controllers for power plants and autonomous vehicles. We are concerned with synthesizing strategies for sequential decision making in discrete dynamical systems, directly from a logical specification. This problem has been investigated within the fields of automated planning and reactive synthesis, utilizing different formulations of the problem dynamics and objectives. By relating the two models, we exploit the theory and practice from one to make significant advances in the theory and practice of the other. We start by revealing a direct correspondence between reactive synthesis and automated planning with temporally extended goals in fully observable and non-deterministic (FOND) environments. Such a correspondence exists when goals and specifications are expressed in Linear Temporal Logic (LTL), and it extends beyond LTL to all the regular and omega-regular specification languages, that can be transformed into finite state automata. Finite state automata are a central theme in the dissertation, and we use them as the computational normal form to derive theoretical results and algorithms. Another central theme is the use of FOND planners as a tool. FOND planners have been highly optimized to exploit the problem structure, and also environment fairness assumptions. However, such tools can only handle final-state goals. We develop theory and algorithms for FOND planning with goals that are temporally extended, using several types of finite state automata to represent such goals. Our algorithms compile automata goals away, and make it possible to use existing FOND planners for the purpose of planning with temporally extended goals. In the second part of the thesis, we revisit specifications for reactive synthesis. We start by studying the crucial role of environment assumptions in realizability, and argue that they should be interpreted as properties of infinite-length traces, in general, even when programs terminate in finite time. We then recast specifications as automata games, and design principled methods to solve those games via compilation to FOND planning. Our methods can handle specifications for terminating and non-terminating programs, can incorporate environment assumptions, and can solve realizability and synthesis. Our empirical evaluation suggests that FOND planners can be an effective tool for sequential decision making, and that exploiting program termination and environment fairness can improve the scalability of algorithms. By studying planning and synthesis from an automata-theoretic perspective, our results can be extended to a variety of temporal logics and other regular and omega-regular specification languages that can be transformed into automata. The work presented in this dissertation establishes a new area of study for researchers in AI automated planning that holds the promise to be transformative with respect to the field of reactive synthesis and to program synthesis more generally.

Keywords: Automated Planning
Finite State Automata
Linear Temporal Logic
Reactive Synthesis
Temporally Extended Goals
Advisor: McIlraith, Sheila Ann