56 research outputs found
Planning as Tabled Logic Programming
This paper describes Picat's planner, its implementation, and planning models
for several domains used in International Planning Competition (IPC) 2014.
Picat's planner is implemented by use of tabling. During search, every state
encountered is tabled, and tabled states are used to effectively perform
resource-bounded search. In Picat, structured data can be used to avoid
enumerating all possible permutations of objects, and term sharing is used to
avoid duplication of common state data. This paper presents several modeling
techniques through the example models, ranging from designing state
representations to facilitate data sharing and symmetry breaking, encoding
actions with operations for efficient precondition checking and state updating,
to incorporating domain knowledge and heuristics. Broadly, this paper
demonstrates the effectiveness of tabled logic programming for planning, and
argues the importance of modeling despite recent significant progress in
domain-independent PDDL planners.Comment: 27 pages in TPLP 201
Efficient Declarative Solutions in Picat for Optimal Multi-Agent Pathfinding
The multi-agent pathfinding (MAPF) problem has attracted considerable attention because of its relation to practical applications. The majority of solutions for MAPF are algorithmic. Recently, declarative solutions that reduce MAPF to encodings for off-the-shelf solvers have achieved remarkable success. We present a constraint-based declarative model for MAPF, together with its implementation in Picat, which uses SAT and MIP. We consider both the makespan and the sum-of-costs objectives, and propose a preprocessing technique for improving the performance of the model. Experimental results show that the implementation using SAT is highly competitive. We also analyze the high performance of the SAT solution by relating it to the SAT encoding algorithms that are used in the Picat compiler
The Third Competition on Knowledge Engineering for Planning and Scheduling
We report on the staging of the third competition on knowledge engineering for AI planning and scheduling systems, held during ICAPS-09 at Thessaloniki, Greece in September 2009.. We give an overview of how the competition has developed since its first run in 2005, and its relationship with the AI planning field. This run of the competition focused on translators that when input with some formal description in an application-area-specific language, output solver-ready domain models. Despite a fairly narrow focus within knowledge engineering, seven teams took part in what turned out to be a very interesting and successful competition
Preface to special issue on planning and scheduling
Planning, scheduling and constraint satisfaction are important areas in artificial intelligence (AI)
with broad practical applicability. Many real-world problems can be formulated as AI planning
and scheduling (P&S) problems, where resources must be allocated to optimize overall performance
objectives. Frequently, solving these problems requires an adequate mixture of planning,
scheduling and resource allocation to competing goal activities over time in the presence of
complex state-dependent constraints. Constraint satisfaction plays an important role in solving
such real-life problems, and integrated techniques that manage P&S with constraint satisfaction
are particularly useful. Knowledge engineering supports the solution of such problems by providing
adequate modelling techniques and knowledge extraction techniques for improving the
performance of planners and schedulers. Briefly speaking, knowledge engineering tools serve as a
bridge between the real world and P&S systems
Expertni systemy zalozene na omezujicich podminkach
Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi
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