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A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decision-theoretic planning, where many desirable behaviours are more naturally expressed as properties of execution sequences rather than as properties of states, NMRDPs form a more natural model than the commonly adopted fully Markovian decision process (MDP) model. While the more tractable solution methods developed for MDPs do not directly apply in the presence of non-Markovian rewards, a number of solution methods for NMRDPs have been proposed in the literature. These all exploit a compact specification of the non-Markovian reward function in temporal logic, to automatically translate the NMRDP into an equivalent MDP which is solved using efficient MDP solution methods. This paper presents NMRDPP (Non-Markovian Reward Decision Process Planner), a software platform for the development and experimentation of methods for decision-theoretic planning with non-Markovian rewards. The current version of NMRDPP implements, under a single interface, a family of methods based on existing as well as new approaches which we describe in detail. These include dynamic programming, heuristic search, and structured methods. Using NMRDPP, we compare the methods and identify certain problem features that affect their performance. NMRDPPs treatment of non-Markovian rewards is inspired by the treatment of domain-specific search control knowledge in the TLPlan planner, which it incorporates as a special case. In the First International Probabilistic Planning Competition, NMRDPP was able to compete and perform well in both the domain-independent and hand-coded tracks, using search control knowledge in the latter

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In this paper, we present a powerful framework for describing, storing, and reasoning about infinite temporal information. This framework is an extension of cla.ssical relational databases. It represents infinite temporal information by generalized tuples defined by linear repeating points and constraints on these points. We prove tl1a.t relations formed from generalized tuples are closed under the operations of relational algebra. A characterization of the expressiveness of generalized relations is given in terms of predicates definable in Presburger arithmetic. Finally, we provide some complexity results.

Abstract-This paper describes a synthesis method that automatically derives controllers for timed discrete-event systems with nonterminating behavior modeled by timed transition graphs and specifications of control requirements expressed by metric temporal logic (MTL) formulas. Synthesis is performed by using 1) a forward-chaining search that evaluates the satisfiability of MTL formulas over sequences of states generated by occurrences of actions and 2) a control-directed backtracking technique that takes into consideration the controllability of actions. This method has several interesting features. First, the issues of controllability, safety, liveness, and real time are integrated in a single framework. Second, the synthesis process does not require explicit storage of an entire transition structure over which formulas are checked and can be stopped at any moment, giving an approximate but useful result. Third, search and control mechanisms allow circumvention of the state explosion problem.

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