Planning as Theorem Proving with Heuristics

Planning as theorem proving in situation calculus was abandoned 50 years ago as an impossible project. But we have developed a Theorem Proving Lifted Heuristic (TPLH) planner that searches for a plan in a tree of situations using the A* search algorithm. It is controlled by a delete relaxation-based domain independent heuristic. We compare TPLH with Fast Downward (FD) and Best First Width Search (BFWS) planners over several standard benchmarks. Since our implementation of the heuristic function is not optimized, TPLH is slower than FD and BFWS. But it computes shorter plans, and it explores fewer states. We discuss previous research on planning within KR\&R and identify related directions. Thus, we show that deductive lifted heuristic planning in situation calculus is actually doable.Comment: Submitted for a review. Copyright (C) 2023 by Mikhail Soutchanski and Ryan Youn

Hybrid Temporal Situation Calculus

The ability to model continuous change in Reiter's temporal situation calculus action theories has attracted a lot of interest. In this paper, we propose a new development of his approach, which is directly inspired by hybrid systems in control theory. Specifically, while keeping the foundations of Reiter's axiomatization, we propose an elegant extension of his approach by adding a time argument to all fluents that represent continuous change. Thereby, we insure that change can happen not only because of actions, but also due to the passage of time. We present a systematic methodology to derive, from simple premises, a new group of axioms which specify how continuous fluents change over time within a situation. We study regression for our new temporal basic action theories and demonstrate what reasoning problems can be solved. Finally, we formally show that our temporal basic action theories indeed capture hybrid automata

Temporal Golog with Execution Monitoring

this paper is an adaptation of the framework developed in (De Giacomo, Reiter, & Soutchanski 1998) to the temporal domain. Our interpreter for sequential temporal Golog computes a schedule (which is a solution of temporal constraints) for a remaining part of a Golog program whenever it makes a single step of execution. In reality, some unexpected exogenous actions may delay execution of robot's actions. For this reason, before performing an action A that was scheduled for execution at time T 1 , robot senses its internal clock to determine the current time T 2 . If T 2 T 1 then robot waits until T 1 and then performs A. If T 1 ! T 2 , but there is an alternative solution of the system of temporal constraints such that a rescheduled remainder of a Golog program can be successfully completed, then the execution monitor reschedules the rest of the Golog program (in particular, it assigns new execution tim

High-Level Robot Programming in Dynamic and Incompletely Known Environments

This thesis advocates the usefulness and practicality of a logic-based approach to AI and in particular to high-level control of mobile robots. The contribution of the research work reported here is twofold: 1) the development of theoretical frameworks that account for uncertainty and unmodeled dynamics in an environment where an acting agent has to achieve certain goals and 2) the implementation of the developed ideas on a mobile robot. We have elaborated the approach to designing efficient and reliable controllers in Golog following two different perspectives on the environment where the control program is supposed to operate. According to one perspective, investigated in Chapter 4, the agent has a logical model of the world, but there is no probabilistic information about the environment where the agent is planning to act, and the agent is not capable or has no time for acquiring probabilities of different effects of its actions. In this case, the uncertainty and dynamics of the environment can be accounted only by observing the real outcomes of actions executed by the agent, by determining possible discrepancies between the observed outcomes and the effects expected according to the logical model of the world and then by recovering, if necessary, from th

Automata Simulation of N-Person Social Dilemma Games

Collective behavior of N players in a social dilemma game is simulated by automata exhibiting asymptotically cooperative behavior. In his automata models of simple biological systems M.Tsetlin assumed minimum of information available to the "players." Our automata were somewhat more sophisticated, using Markov strategies in their interactions. We investigated relationships between information received by the automata and the emergence of cooperation in a simulated evolution process. In some ways our approach is similar to that of R.Axelrod. It differs in that instead of determining the "most successful" strategy, we seek surviving strategies in a social dilemma environment. Previous results showed that cooperation could be established asymptotically under partially centralized control. In our model there is no such control. Our main result is that more sophisticated behavior of "self-seeking" automata compensates for the absence of such control. Moreover cooperation is established more..