This extended abstract from the area of automated planning discusses work on Attributed Transition-Based Domain Control Knowledge (ATB-DCK). ATB-DCK, roughly speaking, represents the "grammar" of solution plans that guides the search. ATB-DCK is expressed by a finite state automaton with attributed states, referring to specific states of objects, connected by transitions imposing constraints on action applicability. This representation stays on side of the planning domain model, but it can be compiled into a classical planning task and thus it complements domain-independent planning techniques. Results on several benchmark domains from the International Planning Competitions show that the use of ATB-DCK often considerably improves efficiency of existing state-of-the-art planning engines.

Domain-independent planning decouples a planning task specification from planning engines. As the specification is usually describing only the physics of the environment, actions and a goal, the planning engines being generic solvers designed to solve any planning task tend to struggle with tasks that can be easily solved by domain-specific algorithms. Additional control knowledge can, to large extent, bridge such a performance gap. Instead of providing a specific planner supporting a given form of control knowledge, control knowledge can be directly encoded within the planning task specification and thus can be exploited by generic planners. In this paper, we proposeAttributed Transition-Based Domain Control Knowledge (ATB-DCK)that is represented by a finite state automaton with attributed states, referring to specific states of objects, connected by transitions imposing constraints on action applicability. ATB-DCK, roughly speaking, represents the “grammar” of solution plans that guides the search. We show that ATB-DCK can be compiled into a classical planning task and thus it complements domain-independent planning techniques. Using several domains from the International Planning Competitions as benchmarks, we demonstrate that this approach often considerably improves efficiency of existing state-of-the-art planning engines.

Standard methods for solving influence diagrams consist in stepwise elimination of variables, and along with elimination of a variable a set of new potentials over new domains is calculated. It is well known that these methods tend to produce unnecessarily large domains resulting in excessive consumption of time and memory. The lazy evaluation method represents only a partial solution to the problem. In this paper we extend any potential with two graphs over its domain representing the dependencies of variables. When a node A is eliminated, all necessary structural information for establishing the minimal sets of domains for potentials is contained in these graphs. We push lazy evaluation a step further to avoid performing unnecessary multiplications and subsequent division with equivalent potentials.

The goal of troubleshooting is to find an optimal solution strategy consisting of actions and observations for repairing a device. We assume a probabilistic model of dependence between possible faults, actions, and observations; the goal is to minimize the expected cost of repair (ECR). We show that the task of finding an optimal solution strategy is NP hard for various troubleshooting models; therefore, approximate algorithms are necessary. © 2003 Wiley Periodicals, Inc.