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Found 7 papers, 0 papers with code
Playing Angry Birds with a Domain-Independent PDDL+ Planner
This demo paper presents the first system for playing the popular Angry Birds game using a domain-independent planner.
System Resilience through Health Monitoring and Reconfiguration
The framework is based on a physics-based digital twin model and three modules tasked with real-time fault diagnosis, prognostics and reconfiguration.
Learning to Operate in Open Worlds by Adapting Planning Models
Planning agents are ill-equipped to act in novel situations in which their domain model no longer accurately represents the world.
Heuristic Search For Physics-Based Problems: Angry Birds in PDDL+
This paper studies how a domain-independent planner and combinatorial search can be employed to play Angry Birds, a well established AI challenge problem.
A Domain-Independent Agent Architecture for Adaptive Operation in Evolving Open Worlds
Model-based reasoning agents are ill-equipped to act in novel situations in which their model of the environment no longer sufficiently represents the world.
Novelty Accommodating Multi-Agent Planning in High Fidelity Simulated Open World
Intelligent agents reason with an internal model of the world to understand the intricacies of their environment and to successfully execute their plans.
Real-World Planning with PDDL+ and Beyond
Hybrid systems are ubiquitous in the real-world, and PDDL+ is the standardized modeling language for capturing such systems as planning domains.
