Collaborating Authors

On the Compilation of Plan Constraints and Preferences

AAAI Conferences

In this work we make existing solver technology applicable to planning with PDDL3 domains by compiling the new constructs back to PDDL2 (Fox & Long 2003). State trajectory constraints are translated into LTL formulae and further to Büchi automata, one for each constraint. These automata are compiled to grounded PDDL and the results are merged with the grounded representation of the original problem. Preference constraints are compiled away using numerical state variables. We provide encouraging experimental results in heuristic search planning.

Planning as Model Checking in Hybrid Domains

AAAI Conferences

Planning in hybrid domains is an important and challenging task, and various planning algorithms have been proposed in the last years. From an abstract point of view, hybrid planning domains are based on hybrid automata, which have been studied intensively in the model checking community. In particular, powerful model checking algorithms and tools have emerged for this formalism. However, despite the quest for more scalable planning approaches, model checking algorithms have not been applied to planning in hybrid domains so far. In this paper, we make a first step in bridging the gap between these two worlds. We provide a formal translation scheme from PDDL+ to the standard formalism of hybrid automata, as a solid basis for using hybrid system model-checking tools for dealing with hybrid planning domains. As a case study, we use the SpaceEx model checker, showing how we can address PDDL+ domains that are out of the scope of state-of-the-art planners.

Reduction of fuzzy automata by means of fuzzy quasi-orders Artificial Intelligence

In our recent paper we have established close relationships between state reduction of a fuzzy recognizer and resolution of a particular system of fuzzy relation equations. In that paper we have also studied reductions by means of those solutions which are fuzzy equivalences. In this paper we will see that in some cases better reductions can be obtained using the solutions of this system that are fuzzy quasi-orders. Generally, fuzzy quasi-orders and fuzzy equivalences are equally good in the state reduction, but we show that right and left invariant fuzzy quasi-orders give better reductions than right and left invariant fuzzy equivalences. We also show that alternate reductions by means of fuzzy quasi-orders give better results than alternate reductions by means of fuzzy equivalences. Furthermore we study a more general type of fuzzy quasi-orders, weakly right and left invariant ones, and we show that they are closely related to determinization of fuzzy recognizers. We also demonstrate some applications of weakly left invariant fuzzy quasi-orders in conflict analysis of fuzzy discrete event systems.

The RegularGcc Matrix Constraint Artificial Intelligence

We study propagation of the RegularGcc global constraint. This ensures that each row of a matrix of decision variables satisfies a Regular constraint, and each column satisfies a Gcc constraint. On the negative side, we prove that propagation is NP-hard even under some strong restrictions (e.g. just 3 values, just 4 states in the automaton, or just 5 columns to the matrix). On the positive side, we identify two cases where propagation is fixed parameter tractable. In addition, we show how to improve propagation over a simple decomposition into separate Regular and Gcc constraints by identifying some necessary but insufficient conditions for a solution. We enforce these conditions with some additional weighted row automata. Experimental results demonstrate the potential of these methods on some standard benchmark problems.