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.

Multi-Agent Path Finding (MAPF) deals with finding collision-free paths for a set of agents. It is a special case of a planning problem with only two actions - move and wait - and with one major constraint of no collision between the agents. The paper addresses the question of how to model MAPF as a classical sequential planning problem. Several models in the PDDL language are proposed and empirically compared.

The overall performance of classical planner depends heavily on the domain model which can be enhanced by adding control knowledge and heuristics. Both of them are known techniques which can boost the search process in exchange for some computational overhead needed for their repeated evaluation. Our experiments show that the gain from usage of heuristics and control knowledge is evolving throughout the search process and also depends on the type of search algorithm. We demonstrate the idea using the branch-and-bound and iterative deepening search techniques, both implemented in the Picat planning module.

Logic programming provides a declarative framework for modeling and solving many combinatorial problems. Until recently, it was not competitive with state-of-the-art planning techniques partly due to search capabilities limited to backtracking. Recent development brought more advanced search techniques to logic programming such as tabling that simplifies implementation and exploitation of more sophisticated search algorithms. Together with rich modeling capabilities this progress brings tabled logic programing on a par with current best planners. This paper describes the planner module of the tabled logic programming language Picat, its modeling capabilities, and core search procedures behind the planner. The major contribution is an experimental comparison of the influence of various modeling techniques, namely factored vs. structured representations of states, control knowledge, and heuristics on the performance of two search procedures – iterative deepening and branch and bound – behind the planner. The paper also compares the Picat planner with winning automated planners both domain dependent and domain independent to demonstrate that the presented techniques are competitive with state-ofthe- art.