COLIN: Planning with Continuous Linear Numeric Change
Not on the map. Why?
Abstract (arXiv)
In this paper we describe COLIN, a forward-chaining heuristic search planner, capable of reasoning with COntinuous LINear numeric change, in addition to the full temporal semantics of PDDL. Through this work we make two advances to the state-of-the-art in terms of expressive reasoning capabilities of planners: the handling of continuous linear change, and the handling of duration-dependent effects in combination with duration inequalities, both of which require tightly coupled temporal and numeric reasoning during planning. COLIN combines FF-style forward chaining search, with the use of a Linear Program (LP) to check the consistency of the interacting temporal and numeric constraints at each state. The LP is used to compute bounds on the values of variables in each state, reducing the range of actions that need to be considered for application. In addition, we develop an extension of the Temporal Relaxed Planning Graph heuristic of CRIKEY3, to support reasoning directly with continuous change. We extend the range of task variables considered to be suitable candidates for specifying the gradient of the continuous numeric change effected by an action. Finally, we explore the potential for employing mixed integer programming as a tool for optimising the timestamps of the actions in the plan, once a solution has been found. To support this, we further contribute a selection of extended benchmark domains that include continuous numeric effects. We present results for COLIN that demonstrate its scalability on a range of benchmarks, and compare to existing state-of-the-art planners.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2014.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 1; 2023: 2; 2024: 2; 2025: 1; 2026: 1.
10 papers.
- 2025ExoPredicator: Learning Abstract Models of Dynamic Worlds for Robot Planningcited by 8
- 2021Gradient-Based Mixed Planning with Symbolic and Numeric Action Parameterscited by 6
- 2023A Domain-Independent Agent Architecture for Adaptive Operation in Evolving Open Worldscited by 3
- 2023When Prolog meets generative models: a new approach for managing knowledge and planning in robotic applicationscited by 2
- 2021Efficient Temporal Piecewise-Linear Numeric Planning with Lazy Consistency Checkingcited by 0
- 2021Improving Search by Utilizing State Information in OPTIC Planners Compilation to LPcited by 0
- 2022System Resilience through Health Monitoring and Reconfigurationcited by 0
- 2024A Metric Hybrid Planning Approach to Solving Pandemic Planning Problems with Simple SIR Modelscited by 0
- 2024A User Study on Contrastive Explanations for Multi-Effector Temporal Planning with Non-Stationary Costscited by 0
- 2026From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Executioncited by 0
- 2026From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution14 Apr 2026
- 2025ExoPredicator: Learning Abstract Models of Dynamic Worlds for Robot Planning30 Sep 2025
- 2024A User Study on Contrastive Explanations for Multi-Effector Temporal Planning with Non-Stationary Costs20 Sep 2024
- 2024A Metric Hybrid Planning Approach to Solving Pandemic Planning Problems with Simple SIR Models18 Sep 2024
- 2023When Prolog meets generative models: a new approach for managing knowledge and planning in robotic applications26 Sep 2023
- 2023A Domain-Independent Agent Architecture for Adaptive Operation in Evolving Open Worlds9 Jun 2023
- 2022System Resilience through Health Monitoring and Reconfiguration30 Aug 2022
- 2021Gradient-Based Mixed Planning with Symbolic and Numeric Action Parameters19 Oct 2021
- 2021Improving Search by Utilizing State Information in OPTIC Planners Compilation to LP15 Jun 2021
- 2021Efficient Temporal Piecewise-Linear Numeric Planning with Lazy Consistency Checking21 May 2021