AI Planning Task

An AI Planning Task is a problem solving task that requires an AI planning system to generate action sequences for achieving goal states.

• AKA: Automated Planning Task, Artificial Intelligence Planning Task, Machine Planning Task.
• Context:
  • Task Input: Initial State, Goal State, Domain Model
    • Optional Input: Resource Constraints, Time Constraints, Quality Metrics
  • Task Output: Action Plan, Execution Sequence
  • Task Performance Measure: plan quality, planning time, solution optimality
  • ...
  • It can typically process Planning Domain Description through planning language parsers.
  • It can typically search State Space via planning search algorithms.
  • It can typically evaluate Plan Validity using planning constraint checkers.
  • It can typically optimize Plan Quality through planning optimization methods.
  • It can typically handle Planning Uncertainty with planning robustness techniques.
  • ...
  • It can often employ Heuristic Search for planning efficiency improvement.
  • It can often utilize Domain Knowledge for planning guidance.
  • It can often support Replanning through planning adaptation mechanisms.
  • It can often enable Hierarchical Planning via planning abstraction levels.
  • It can often incorporate Temporal Reasoning for planning time management.
  • ...
  • It can range from being a Classical AI Planning Task to being a Non-Classical AI Planning Task, depending on its planning assumption.
  • It can range from being a Deterministic AI Planning Task to being a Probabilistic AI Planning Task, depending on its planning uncertainty model.
  • It can range from being an Offline AI Planning Task to being an Online AI Planning Task, depending on its planning execution mode.
  • It can range from being a Single-Agent AI Planning Task to being a Multi-Agent AI Planning Task, depending on its planning coordination requirement.
  • It can range from being a Domain-Independent AI Planning Task to being a Domain-Specific AI Planning Task, depending on its planning knowledge requirement.
  • ...
  • It can be solved by AI Planning System through planning algorithm implementation.
  • It can be specified in Planning Domain Definition Language for planning problem representation.
  • It can be evaluated by Planning Benchmark for planning performance assessment.
  • It can be decomposed into Planning Subtask for planning complexity management.
  • It can be integrated with Execution Monitoring for planning robustness.
  • ...
• Example(s):
  • Classical Planning Tasks, such as:
    • STRIPS Planning Task for symbolic action planning.
    • PDDL Planning Task using planning domain definition language.
    • HTN Planning Task with hierarchical task networks.
  • Temporal Planning Tasks, such as:
    • Timeline Planning Task for temporal constraint satisfaction.
    • Scheduling Planning Task for resource-time optimization.
  • Probabilistic Planning Tasks, such as:
    • MDP Planning Task for Markov decision processes.
    • POMDP Planning Task with partial observability.
  • Multi-Agent Planning Tasks, such as:
    • Cooperative Planning Task for agent coordination.
    • Adversarial Planning Task for competitive scenarios.
  • ...
• Counter-Example(s):
  • Reactive Control Tasks, which respond without planning lookahead.
  • Classification Tasks, which categorize without action sequence generation.
  • Optimization Tasks, which find optimal values without plan construction.
• See: Problem Solving Task, Constraint Satisfaction Task, Search Task, Optimization Task, Decision Making Task, Scheduling Task, AI Planning System, Planning Domain Definition Language, Automated Planning, Cognitive Task.

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