Decision-theoretic planning with non-Markovian rewards

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 a

On the Synthesis of Situation Control Rules under Exogenous Events

One approach for computing plans for reactive agents is is to check goal statements over state trajectories modeling predicted behaviors of an agent. This paper describes a powerful extension of this approach to handle time, safety, and liveness goals that are specified by Metric Temporal Logic formulas. Our planning method is based on an incremental planning algorithm that generates a reactive plan by computing a sequence of partially satisfactory reactive plans converging towards a completely satisfactory one. Partial satisfaction means that an agent controlled by the plan accomplishes its goal only for some environment events. Complete satisfaction means that the agent accomplishes its goal whatever the environment event occur during the execution of the plan. As such, our planner can be stopped at any time to yield a useful plan. Keywords: Planning, control, reactive agents, temporal goals. Introduction Reactive agents play an increasingly important role in many computer applicat..

Planning for Temporally Extended Goals

this paper appears in Proceedings of AAAI '96, pp. 1215-1222. F. Bacchus and F. Kabanza / Temporally Extended Goals 2 Yet this flexibility also poses a problem: how do we communicate to such an agent the task we want accomplished in a sufficiently precise manner so that it does what we reall

Search control in planning for temporally extended goals

Current techniques for reasoning about search control knowledge in AI planning, such as those used in TLPlan, TALPlanner, or SHOP2, assume that search control knowledge is conditioned upon and interpreted with respect to a fixed set of goal states. Therefore, these techniques can deal with reachability goals but do not apply to temporally extended goals, such as goals of achieving a condition whenever a certain fact becomes true. Temporally extended goals convey several intermediate reachability goals to be achieved at different point of execution, sometimes with cyclic executions; that is, the notion of goal state becomes dynamic. In this paper, we describe a method for reasoning about search control knowledge in the presence of temporally extended goals. Given such a goal, we generate an equivalent Büchi automaton— an automaton recognising the language of the executions satisfying the goal—and interpret control knowledge over this automaton and the world state trajectories generated by a forward search planner. This method is implemented and experimented with as an extension of the TLPlan planner, which incidentally becomes capable of handling cyclic goals

Planning Agents in a Multi-agents Intelligent Tutoring System

Abstract. Most recent architectures of intelligent tutoring system (ITS) focussed on the tutor or the curriculum components, but with little attention being paid to planning. Therefore, some works identified two main planning process in ITS: content planning and delivery planning. In this paper, we propose a new ITS architecture that involves sophisticated planning agent at four different levels of the ITS processing. The first planning level (course planning) and the second one (Lesson planning) are derived from the traditional content planning. The third and fourth planning levels come from a decomposition of the tutor module into two different components, one specialized in the tutorial actions planning and the other tailored for the generation of multimedia presentations. After a brief presentation of the new architecture, the paper mainly focussed on the first level planning using SIMPLAN. This planning oriented achitecture takes advantages of previous ITS architecture and provides a uniform view of ITS components that can facilitate collaboration between them

Clinical Reasoning Automata for Simulated Patients

Abstract. In this paper we introduce clinical reasoning automata to model states and transitions about different cognitive processes that occur during a clinical reasoning activity. A state of the automaton represents a particular process in a complex patient diagnosis using influence diagrams encoding clinical knowledge about the case. Transitions model switch between diagnosis cognitive processes, such as collecting evidences, formulating hypothesis or explicitly asking for assistance at a given point during the reasoning process. That way, we can efficiently model tutoring feedback hints for clinical reasoning learning that are based not only on the clinical knowledge, but also on the sequencing of the tutoring processes.