On Reformulating Planning as Dynamic Constraint Satisfaction (extended Abstract)

On Reformulating Planning as Dynamic Constraint Satisfaction

In recentyears,researchers have reformulatedSTRIPS planning problems as SAT problems or CSPs. In thispaper,we discussthe Constraint-BasedIntervalPlanning(CBIP) paradigm,which can represent planning problems incorporatingintervaltime and resottrces. We describehow to reformulatemutual exclusionconstraintsfora CBIPbased system,the ExtendibleUniform Remote OperationsPlannerArchitecture(EUROPA). We...

Reformulating Constraint Models for Classical Planning

Constraint satisfaction techniques are commonly used for solving scheduling problems, still they are rare in AI planning. Although there are several attempts to apply constraint satisfaction for solving AI planning problems, these techniques never became predominant in planning; and they never reached the success of, for example, SATbased planners. In this paper we argue that existing constrain...

On the Complexity of Dynamic Constraint Satisfaction (Extended Abstract)

Fuzzy rrDFCSP and planning

Constraint satisfaction is a fundamental Artificial Intelligence technique for knowledge representation and inference. However, the formulation of a static constraint satisfaction problem (CSP) with hard, imperative constraints is insufficient to model many real problems. Fuzzy constraint satisfaction provides a more graded viewpoint. Priorities and preferences are placed on individual constrai...

Revisiting dynamic constraint satisfaction for model-based planning

As planning problems become more complex, it is increasingly useful to integrate complex constraints on time and resources into planning models, and use constraint reasoning approaches to help solve the resulting problems. Dynamic constraint satisfaction is a key enabler of automated planning in the presence of such constraints. In this paper, we identify some limitations with the previously de...