DPlan

DPlan is a planning framework in artificial intelligence designed to generate executable action sequences that achieve specified goals within dynamic environments. It emphasizes dynamic replanning and close integration between planning and execution.

An architecture for DPlan typically comprises a planner component and an execution monitor. The planner constructs plans from a domain model using representations such as hierarchical task networks or operators. The execution monitor observes action outcomes, tracks progress toward goals, and initiates replanning or plan repair when deviations render the current plan infeasible or suboptimal.

DPlan systems commonly support online planning, conditional and contingent plans, partial observability, and the handling of uncertainty. They can incorporate cost or preference metrics to guide plan selection and often rely on heuristics to prune search space. Many implementations also include mechanisms for learning from past executions to improve domain models or action estimates.

Variants and related work describe different emphases, such as safety-focused planning, probabilistic reasoning under uncertainty, or integration with learning components. DPlan is frequently discussed in the contexts of robotics, autonomous agents, workflow automation, and real-time decision making.

Applications include autonomous robots performing tasks in changing environments, unmanned vehicles, industrial process automation, logistics optimization, and game AI where plans must adapt to dynamic events.

Limitations include computational complexity in large or highly dynamic domains, sensitivity to the accuracy of action models, and challenges ensuring robust real-time performance. Evaluation typically uses benchmark scenarios with disruptions, delays, or partial observability to compare replanning efficacy and plan quality.

See also: planning, dynamic planning, plan repair, hierarchical task networks.