Automatic Generation of Test Oracles—From Pilot Studies to Application
Automated Software Engineering
Planning as Satisfiability in Nondeterministic Domains
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Planning via Model Checking: A Decision Procedure for AR
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Actions and Events in Interval Temporal Logic
Actions and Events in Interval Temporal Logic
Constraint-Based Attribute and Interval Planning
Constraints
Bridging the gap between planning and scheduling
The Knowledge Engineering Review
Symbolic Model Checking of Logics with Actions
Model Checking and Artificial Intelligence
Automatic verification of knowledge and time with NuSMV
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
SPUDD: stochastic planning using decision diagrams
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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The marriage of model checking and planning faces two seemingly diverging alternatives: the need for a planning language expressive enough to capture the complexity of real-life applications, as opposed to a language simple, yet robust enough to be amenable to exhaustive verification and validation techniques. In an attempt to reconcile these differences, we have designed an abstract plan description language, ANMLite, inspired from the Action Notation Modeling Language (ANML). We present the basic concepts of the ANMLite language as well as an automatic translator from ANMLite to the model checker SAL (Symbolic Analysis Laboratory). We discuss various aspects of specifying a plan in terms of constraints and explore the implications of choosing a robust logic behind the specification of constraints, rather than simply propose a new planning language. Additionally, we provide an initial assessment of the efficiency of model checking to search for solutions of planning problems. To this end, we design a basic test benchmark and study the scalability of the generated SAL models in terms of plan complexity.