Timelines Are Expressive Enough to Capture Action-Based Temporal Planning

Planning problems are usually expressed by specifying which actions can be performed to obtain a given goal. In temporal planning problems, actions come with a time duration and can overlap in time, which noticeably increase the complexity of the reasoning process. Action-based temporal planning has been thoroughly studied from the complexity-theoretic point of view, and has been proved to be EXPSPACE-complete in its general formulation. Conversely, timeline-based planning problems are represented as a collection of variables whose time-varying behavior is governed by a set of temporal constraints, called synchronization rules. Timelines provide a unified framework to reason about planning and execution under uncertainty. Timeline-based systems are being successfully employed in real-world complex tasks, but, in contrast to action-based planning, little is known on their computational complexity and expressiveness. In particular, a comparison of the expressiveness of the action- and timeline-based formalisms is still missing. This paper contributes a first step in this direction by proving the EXPSPACE-completeness of timeline-based planning with no temporal horizon and bounded temporal relations only. The result is shown via a reduction from action-based temporal planning, thus proving that timelines are expressive enough to capture it

Searching for Cost-Optimized Strategies: An Agricultural Application

Best Paper AwardInternational audienceWe consider a system modeled as a set of interacting components evolving along time according to explicit timing constraints. The decision making problem consists in selecting and organizing actions in order to reach a goal state in a limited time and in an optimal manner, assuming actions have a cost. We propose to reformulate the planning problem in terms of model-checking and controller synthesis such that the state to reach is expressed using a temporal logic. We have chosen to represent each agent using the formalism of Priced Timed Game Au-tomata (PTGA) and a set of knowledge. PTGA is an extension of Timed Automata that allows the representation of cost on actions and the definition of a goal (to reach or to avoid). This paper describes two algorithms designed to answer the planning problem on a network of agents and proposes practical implementation using model-checking tools that shows promising results on an agricultural application: a grassland based dairy production system

Temporal Planning with extended Timed Automata

International audienceWe consider a system modeled as a set of interacting agents evolving along time according to explicit timing constraints. In this kind of system, the planning task consists in selecting and organizing actions in order to reach a goal state in a limited time and in an optimal manner, assuming actions have a cost. We propose to reformulate the planning problem in terms of model-checking and controller synthesis on interacting agents such that the state to reach is expressed using temporal logic. We have chosen to represent each agent using the formalism of Priced Timed Game Automata (PTGA). PTGA is an extension of Timed Automata that allows the representation of cost on actions and uncontrollable actions. Relying on this domain description, we define a planning algorithm that computes the best strategy to achieve the goal. This algorithm is based on recognized model-checking and synthesis tools from the UPPAAL suite. The expressivity of this approach is evaluated on the classical Transport Domain which is extended in order to include timing constraints, cost values and uncontrollable actions. This work has been implemented and performances evaluated on benchmarks

Factories of the Future

Engineering; Industrial engineering; Production engineerin

Analyzing Flexible Timeline-based Plans

Timeline-based planners have been shown quite successful in addressing real world problems. Nevertheless they are considered as a niche technology in AI P&S research as an application synthesis with such techniques is still considered a sort of “black art”. Authors are currently developing a knowledge engineering tool around a timeline-based problem solving environment; in this framework we aim at integrating verification and validation methods. This work presents a verification process suitable for a timeline-based planner. It shows how a problem of flexible temporal plan verification can be cast as model-checking on timed game automata. Additionally it provides formal properties and checks the effectiveness of the proposed approach with a detailed experimental analysi

Analyzing Flexible Timeline-based Plans

Timeline-based planners have been shown quite successful in addressing real world problems. Nevertheless they are considered as a niche technology in AI P&S research as an application synthesis with such techniques is still considered a sort of "black art". Authors are currently developing a knowledge engineering tool around a timeline-based problem solving environment; in this framework we aim at integrating verification and validation methods. This work presents a verification process suitable for a timeline-based planner. It shows how a problem of flexible temporal plan verification can be cast as model-checking on timed game automata. Additionally it provides formal properties and checks the effectiveness of the proposed approach with a detailed experimental analysis