Generating feasible schedules under complex metric constraints
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Model checking
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Knowledge Acquisition for the Onboard Planner of an Autonomous Spacecraft
EKAW '97 Proceedings of the 10th European Workshop on Knowledge Acquisition, Modeling and Management
FAABS '00 Proceedings of the First International Workshop on Formal Approaches to Agent-Based Systems-Revised Papers
Exploiting Implicit Representations in Timed Automaton Verification for Controller Synthesis
HSCC '02 Proceedings of the 5th International Workshop on Hybrid Systems: Computation and Control
NuSMV 2: An OpenSource Tool for Symbolic Model Checking
CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
Mapping Temporal Planning Constraints into Timed Automata
TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
Constraint-Based Attribute and Interval Planning
Constraints
Bridging the gap between planning and scheduling
The Knowledge Engineering Review
Planning domain definition using GIPO
The Knowledge Engineering Review
Spin model checker, the: primer and reference manual
Spin model checker, the: primer and reference manual
Flexible Plan Verification: Feasibility Results
Fundamenta Informaticae - RCRA 2009 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
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To foster effective use of artificial intelligence planning and scheduling (P&S) systems in the real world, it is of great importance to both (a) broaden direct access to the technology for the end users and (b) significantly increase their trust in such technology. Automated P&S systems often bring solutions to the users that are neither ‘obvious’ nor immediately acceptable to them. This is because these tools directly reason on causal, temporal, and resource constraints; moreover, they employ resolution processes designed to optimize the solution with respect to non-trivial evaluation functions. Knowledge engineering environments aim at simplifying direct access to the technology for people other than the original system designers, while the integration of validation and verification (V&V) capabilities in such environments may potentially enhance the users’ trust in the technology. Somehow, V&V techniques may represent a complementary technology, with respect to P&S, that contributes to developing richer software environments to synthesize a new generation of robust problem-solving applications. The integration of V&V and P&S techniques in a knowledge engineering environment is the topic of this paper. In particular, it analyzes the use of state-of-the-art V&V technology to support knowledge engineering for a timeline-based planning system called MrSPOCK. The paper presents the application domain for which the automated solver has been developed, introduces the timeline-based planning ideas, and then describes the different possibilities to apply V&V to planning. Hence, it continues by describing the step of adding V&V functionalities around the specialized planner, MrSPOCK. New functionalities have been added to perform both model validation and plan verification. Lastly, a specific section describes the benefits as well as the performance of such functionalities.