Temporal Planning for Compilation of Quantum Approximate Optimization Algorithm Circuits

We investigate the application of temporal planners to the problem of compiling quantum circuits to newly emerging quantum hardware. While our approach is general, we focus our initial experiments on Quantum Approximate Optimization Algorithm (QAOA) circuits that have few ordering constraints and allow highly parallel plans. We report on experiments using several temporal planners to compile circuits of various sizes to a realistic hardware. This early empirical evaluation suggests that temporal planning is a viable approach to quantum circuit compilation

Exploring Knowledge Engineering Strategies in Designing and Modelling a Road Traffic Accident Management Domain

Formulating knowledge for use in AI Planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers and the tools they use may significantly influence the quality of the resulting planning application. There is little in the way of guidelines or standard procedures, however, for knowledge engineers to use when formulating knowledge into planning domain languages such as PDDL. This paper seeks to investigate this process using as a case study a road traffic accident management domain. Managing road accidents requires systematic, sound planning and coordination of resources to improve outcomes for accident victims. We have derived a set of requirements in consultation with stakeholders for the resource coordination part of managing accidents. We evaluate two separate knowledge engineering strategies for encoding the resulting planning domain from the set of requirements: (a) the traditional method of PDDL experts and text editor, and (b) a leading planning GUI with built in UML modelling tools. These strategies are evaluated using process and product metrics, where the domain model (the product) was tested extensively with a range of planning engines. The results give insights into the strengths and weaknesses of the approaches, highlight lessons learned regarding knowledge encoding, and point to important lines of research for knowledge engineering for planning

An Automatic Algorithm Selection Approach for Planning

Despite the advances made in the last decade in automated planning, no planner outperforms all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners' performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains, the performance of a planner can be improved by exploiting additional knowledge, extracted in the form of macro-operators or entanglements. In this paper we propose ASAP, an automatic Algorithm Selection Approach for Planning that: (i) for a given domain initially learns additional knowledge, in the form of macro-operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings--planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans

Planification Evolutionnaire par Décomposition

Ce rapport présente l'approche Divide-and-Evolve pour la résolution générique des problèmes de planification temporelle par décomposition. L'idée principale de l'approche est la recherche des solutions dans l'espace des décompositions en états intermédiaires à l'aide d'un algorithme évolutionnaire: les solutions candidates sont des séquences d'états intermédiaires qui définissent successivement les plans partiels du problème initial. Nous nous sommes intéressés à la résolution des problèmes de type "simple temporal planning problems". La résolution des séquences d'états intermédiaires et la détermination d'une solution globale se font à l'aide du planificateur CPT. Ce rapport formalise l'approche, définit l'algorithme Divide-and-Evolve et compare les résultats obtenus à ceux trouvés par les meilleurs planificateurs existants à notre connaissance

Heuristic Search for Planning with Different Forced Goal-Ordering Constraints

Planning with forced goal-ordering (FGO) constraints has been proposed many times over the years, but there are still major difficulties in realizing these FGOs in plan generation. In certain planning domains, all the FGOs exist in the initial state. No matter which approach is adopted to achieve a subgoal, all the subgoals should be achieved in a given sequence from the initial state. Otherwise, the planning may arrive at a deadlock. For some other planning domains, there is no FGO in the initial state. However, FGO may occur during the planning process if certain subgoal is achieved by an inappropriate approach. This paper contributes to illustrate that it is the excludable constraints among the goal achievement operations (GAO) of different subgoals that introduce the FGOs into the planning problem, and planning with FGO is still a challenge for the heuristic search based planners. Then, a novel multistep forward search algorithm is proposed which can solve the planning problem with different FGOs efficiently

A SAT-based planning framework for optimizing resource production

Domain-independent automated planning is concerned with computing a sequence of actions that can transform an initial state into a desired goal state. Resource production domains form an interesting class of such problems, in that they typically require reasoning about concurrent durative-actions with continuous effects while minimizing some cost function. Although formulating planning problems as instances of SAT has proven to be very successful within the realm of STRIPS planning problems, where states and time are discrete and actions are instantaneous, it is unclear whether the same success can be transferred to resource production. Some of the major drawbacks to these systems are that they do not support reasoning about metric quantities, continuous time, and cost functions. TM-LPSAT was one of the first successful systems to reason about both metric quantities and continuous time within a SAT framework. However, TM-LPSAT does not provide a way to reason about cost functions. In this thesis, we extend the framework in a way that allows it to be capable of minimizing the costs, in our case makespans, of the plans that it finds

On-line planning and scheduling: an application to controlling modular printers

We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust continual operation. To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning. Our system handles execution failures and multi-objective preferences. At its heart is an on-line algorithm that combines techniques from state-space planning and partial-order scheduling. We suggest that this general architecture may prove useful in other applications as more intelligent systems operate in continual, on-line settings. Our system has been used to drive several commercial prototypes and has enabled a new product architecture for our industrial partner. When compared with state-of-the-art off-line planners, our system is hundreds of times faster and often finds better plans. Our experience demonstrates that domain-independent AI planning based on heuristic search can flexibly handle time, resources, replanning, and multiple objectives in a high-speed practical application without requiring hand-coded control knowledge