COLIN: Planning with Continuous Linear Numeric Change (original) (raw)

Planning with Problems Requiring Temporal Coordination

2008

We present the first planner capable of reasoning with both the full semantics of PDDL2.1 (level 3) temporal planning and with numeric resources. Our planner, CRIKEY3, employs heuristic forward search, using the start-and-end semantics of PDDL2.1 to manage temporal actions. The planning phase is interleaved with a scheduling phase, using a Simple Temporal Network, in order to ensure that temporal constraints are met. To guide search, we introduce a new temporal variant of the Relaxed Planning Graph heuristic that is capable of reasoning with the features of this class of domains, along with the Timed Initial Literals of PDDL2.2. CRIKEY3 extends the state-of-the-art in handling the full temporal expressive power of PDDL2.1, including numeric temporal domains.

Temporal Planning in Domains with Linear Processes

We consider the problem of planning in domains with continuous linear numeric change. Such change cannot always be adequately modelled by discretisation and is a key facet of many interesting problems. We show how a forward-chaining temporal planner can be extended to reason with actions with continuous linear effects. We extend a temporal planner to handle numeric values using linear programming. We show how linear continuous change can be integrated into the same linear program and we discuss how a temporal-numeric heuristic can be used to provide the search guidance necessary to underpin continuous planning. We present results to show that the approach can effectively handle duration-dependent change and numeric variables subject to continuous linear change.

Planning with Linear Continuous Numeric Change

cis.strath.ac.uk

Reasoning with domains containing continuous numeric change remains an open challenge in planning. In this paper, we discuss how an established temporal planner, CRIKEY3, can be adapted to plan in domains containing linear continuous numeric change. In particular, we discuss how an LP can be used to scheudule action choices, and how the LPRPG heuristic (hitherto for non-temporal planning) can be adapted for use in a temporal and linear-continuous setting.

Efficient Temporal Piecewise-Linear Numeric Planning With Lazy Consistency Checking

IEEE transactions on artificial intelligence, 2022

Temporal planning often involves numeric effects that are directly proportional to their action's duration. These include continuous effects, where a numeric variable is subjected to a rate of change while the action is being executed, and discrete duration-dependent effects, where the variable is updated instantaneously but the magnitude of such change is computed from the action's duration. When these effects are linear, state-of-theart temporal planners often make use of Linear Programming to ensure that these numeric updates are consistent with the chosen start times and durations of the plan's actions. This is typically done for each evaluated state as part of the search process. This exhaustive approach is not scalable to solve realworld problems that require long plans, because the linear program's size becomes larger and slower to solve. In this work we propose techniques that minimise this overhead by computing these checks more selectively and formulating linear programs that have a smaller footprint. The effectiveness of these techniques is demonstrated on domains that use a mix of discrete and continuous effects, which is typical of real-world planning problems. The resultant planner also outperforms most state-ofthe-art temporal-numeric and hybrid planners, in terms of both coverage and scalability.

Temporal planning for rich numeric contexts

2016

Real-world planning problems often feature complex temporal and numeric characteristics. These include concurrent activities and also effects that involve continuous change. This work presents the formalism behind reasoning with required concurrency that involves continuous change in temporal planning problems, together with a set of techniques to solve a class of tasks that to date are hard to solve with current state-of-the-art temporal planners. The motivation for this work is scenarios where planning actions have rich numeric effects on some physical system. One such example is automated demand dispatch for electricity provision, where actions that fulfil customer requirements have an effect on various metrics, such as wattage or costs, which could be subject to operational or commercial constraints. An algorithm that handles discrete interference of linear continuous effects, referred to as constants in context, is presented. This technique allows discrete actions to update the rate of change of a continuous effect taking place concurrently. This work builds on techniques used in current temporal planners that make use of linear programming, and also extends the heuristic to guide the search to a solution. This algorithm was implemented in a new temporal and numeric planner called DICE and evaluated with some benchmark domains. PDDL, the current de facto standard language for planning domains and corresponding planning tasks, was extended to support interactions with external class modules. The proposed extension, PDDLx, defines a generic planner-solver interface for both discrete and continuous effects. This enables planners that implement this interface to interact with external solvers and incorporate context-specific effects in a black-box fashion, enabling complex numeric behaviour to be encapsulated within such modules. Non-linear monotonic continuous effects, defined in the proposed PDDLx extension, are integrated within the planner using a non-linear iterative convergence algorithm. It searches for a linear approximation within an acceptable configurable error margin, which is then used within the linear program computed for each temporal state. This algorithm proves to be effective in various domains where non-linear continuous behaviour is prevalent. This technique was implemented as an extension to DICE, called uNICOrn, which performs non-linear iterative convergence for continuous effects whose duration needs to be determined by the planner. uNICOrn was also evaluated with some benchmark non-linear domains. A case study on the automated demand dispatch domain is presented to demonstrate the use of the planning algorithms proposed in this thesis. Linear and non-linear planning problems are evaluated and the performance of uNICOrn on these problem instances was analysed. This work builds on current techniques used for temporal planning with continuous numeric behaviour using linear programming, and enhances them to remove some of their intrinsic limitations. The result is a set of algorithms that are more effective in solving real-world applications that involve continuous change and rich numeric behaviour. This work would not have been possible without the academic, moral and financial support of various people and organisations. I am deeply grateful to my supervisors, Professor Maria Fox and Professor Derek Long, for their guidance, inspiration and continuous encouragement. Apart from their in-depth knowledge, they provided me with unique opportunities to get international exposure within the A.I. community, which has enriched my experience even further. This research was funded and supported by the UK Engineering and Physical Sciences Research Council (EPSRC) as part of the project entitled The Autonomic Power System (Grant Ref: EP/I031650/1). I would like to thank all the researchers participating in the project for their insight and interesting discussions during the various meetings and events. I would also like to thank my colleagues and friends at King's College London, whose company and friendship made the whole experience even more pleasant and enjoyable. The regular meetings of the King's A.I. Planning Research Group were also instrumental to generate ideas and gather invaluable feedback throughout my research. Last and foremost, I would like to show my utmost gratitude to my wife Thérèse, my parents, and the rest of our respective families for their immeasurable patience and support throughout my academic and professional endeavours.

Efficiently Reasoning with Interval Constraints in Forward Search Planning

Proceedings of the AAAI Conference on Artificial Intelligence, 2019

In this paper we present techniques for reasoning natively with quantitative/qualitative interval constraints in statebased PDDL planners. While these are considered important in modeling and solving problems in timeline based planners; reasoning with these in PDDL planners has seen relatively little attention, yet is a crucial step towards making PDDL planners applicable in real-world scenarios, such as space missions. Our main contribution is to extend the planner OPTIC to reason natively with Allen interval constraints. We show that our approach outperforms both MTP, the only PDDL planner capable of handling similar constraints and a compilation to PDDL 2.1, by an order of magnitude. We go on to present initial results indicating that our approach is competitive with a timeline based planner on a Mars rover domain, showing the potential of PDDL planners in this setting.

Prottle: A probabilistic temporal planner

2005

Planning with concurrent durative actions and probabilistic effects, or probabilistic temporal planning, is a relatively new area of research. The challenge is to replicate the success of modern temporal and probabilistic planners with domains that exhibit an interaction between time and uncertainty. We present a general framework for probabilistic temporal planning in which effects, the time at which they occur, and action durations are all probabilistic. This framework includes a search space that is designed for solving probabilistic temporal planning problems via heuristic search, an algorithm that has been tailored to work with it, and an effective heuristic based on an extension of the planning graph data structure.

Using Temporal Knowledge in a Constraint-Based Planner

Incorporating domain-specic temporal knowledge into a constraint-based planner can signican tly decrease the computation time. This paper describes a method for compiling and embedding user- specied temporal information into the constraint problem translation of a planning problem. We provide empirical results for our framework implemented within the Blackbox (9) planning system; the data indicate that using temporal domain information accelerates planning. A classical planning problem is usually specied as sets of objects, actions, propositional pred- icates to describe the relationships of the objects in the world, a description of the initial state of the world, and a description of the desired nal state. The actions and predicates make up the planning domain, while a specic set of objects, an initial state, and a nal state constitute a particular planning problem. Actions act on objects; when taken, an action may change what is true about the world through its ee cts. Further, a...

Temporal Planning while the Clock Ticks

2018

One of the original motivations for domain-independent planning was to generate plans that would then be executed in the environment. However, most existing planners ignore the passage of time during planning. While this can work well when absolute time does not play a role, this approach can lead to plans failing when there are external timing constraints, such as deadlines. In this paper, we describe a new approach for time-sensitive temporal planning. Our planner is aware of the fact that plan execution will start only once planning finishes, and incorporates this information into its decision making, in order to focus the search on branches that are more likely to lead to plans that will be feasible when the planner finishes.

Extending the Use of Inference in Temporal Planning as Forwards Search

2009

PDDL2.1 supports modelling of complex temporal planning domains in which solutions must exploit concurrency. Few existing temporal planners can solve problems that require concurrency and those that do typically pay a performance price to deploy reasoning machinery that is not always required. In this paper we show how to improve the performance of forward-search planners that attempt to solve the full temporal planning problem, both by narrowing the use of the concurrency machinery to situations that demand it and also by improving the power of inference to prune redundant branches of the search space for common patterns of interaction in temporal domains that do require concurrency. Results illustrate the effectiveness of our ideas in improving the efficiency of a temporal planner that can solve problems with required concurrency, both in domains that exploit this ability and in those that do not.