Filtering, Decomposition and Search Space Reduction for Optimal Sequential Planning

International audienceWe present in this paper a hybrid planning system which combines constraint satisfaction techniques and planning heuris-tics to produce optimal sequential plans. It integrates its own consistency rules and filtering and decomposition mechanisms suitable for planning. Given a fixed bound on the plan length, our planner works directly on a structure related to Graphplan's planning graph. This structure is incrementally built: Each time it is extended, a sequential plan is searched. Different search strategies may be employed. Currently, it is a forward chaining search based on problem decomposition with action sets partitioning. Various techniques are used to reduce the search space, such as memorizing nogood states or estimating goals reachability. In addition, the planner implements two different techniques to avoid enumerating some equivalent action sequences. Empirical evaluation shows that our system is very competitive on many problems, especially compared to other optimal sequential planners

VAL : automatic plan validation, continuous effects and mixed initiative planning using PDDL

This paper describes aspects of our plan validation tool, VAL. The tool was initially developed to support the 3rd International Planning Competition, but has subsequently been extended in order to exploit its capabilities in plan validation and development. In particular, the tool has been extended to include advanced features of PDDL2.1 which have proved important in mixed-initiative planning in a space operations project. Amongst these features, treatment of continuous effects is the most significant, with important effects on the semantic interpretation of plans. The tool has also been extended to keep abreast of developments in PDDL, providing critical support to participants and organisers of the 4th IPC

Formal Modelling of Goals in Organizations

Each organization exists or is created for the achievement of one or more goals. To ensure continued success, the organization should monitor its performance with respect to the formulated goals. In practice the performance of an organization is often evaluated by estimating its performance indicators. In most existing approaches on organization modelling the relation between performance indicators and goals remains implicit. This paper proposes a formal framework for modelling goals based on performance indicators and defines mechanisms for establishing goal satisfaction, which enable evaluation of organizational performance. Methodological and analysis issues related to goals are discussed in the paper. The described framework is a part of a general framework for organization modelling and analysis

Learning in planning with temporally extended goals and uncontrollable events

Recent contributions to advancing planning from the classical model to more realistic problems include using temporal logic such as LTL to express desired properties of a solution plan. This paper introduces a planning model that combines temporally extended goals and uncontrollable events. The planning task is to reach a state such that all event sequences generated from that state satisfy the problem's temporally extended goal. A real-life application that motivates this work is to use planning to configure a system in such a way that its subsequent, non-deterministic internal evolution (nominal behavior) is guaranteed to satisfy a condition expressed in temporal logic. A solving architecture is presented that combines planning, model checking and learning. An online learning process incrementally discovers information about the problem instance at hand. The learned information is useful both to guide the search in planning and to safely avoid unnecessary calls to the model checking module. A detailed experimental analysis of the approach presented in this paper is included. The new method for online learning is shown to greatly improve the system performance.NICTA is funded by the Australian Government’s Department of Communications, Information Technology, and the Arts and the Australian Research Council through Backing Australia’s Ability and the ICT Research Centre of Excellence program

Preparing Youth for College and Career: A Process Evaluation of Urban Alliance

Urban Alliance, headquartered in Washington, DC, serves at-risk youth through its high school internship program, which provides training, mentoring, and work experience to high school seniors from distressed communities in Washington, DC; Baltimore; Northern Virginia; and Chicago. The program serves youth before they become disconnected, helping them successfully transition to higher education or employment after graduation. Urban Alliance has commissioned the Urban Institute to conduct a six-year, randomized controlled trial impact and process evaluation of its high school internship program. This report provides a process analysis of the program; the analysis is informed by extensive evaluator observation and interviews with staff, stakeholders, and youth. It also presents baseline information about Urban Alliance and the youth participating in its high school internship program in Washington, DC, and Baltimore in the 2011–12 and 2012–13 program years. Subsequent reports as part of the impact study will describe the early-adulthood impacts of the Urban Alliance internship program on the youth it serves. Below is a summary of the findings in this first of three reports

An AI planning-based tool for scheduling satellite nominal operations

Satellite domains are becoming a fashionable area of research within the AI community due to the complexity of the problems that satellite domains need to solve. With the current U.S. and European focus on launching satellites for communication, broadcasting, or localization tasks, among others, the automatic control of these machines becomes an important problem. Many new techniques in both the planning and scheduling fields have been applied successfully, but still much work is left to be done for reliable autonomous architectures. The purpose of this article is to present CONSAT, a real application that plans and schedules the performance of nominal operations in four satellites during the course of a year for a commercial Spanish satellite company, HISPASAT. For this task, we have used an AI domain-independent planner that solves the planning and scheduling problems in the HISPASAT domain thanks to its capability of representing and handling continuous variables, coding functions to obtain the operators' variable values, and the use of control rules to prune the search. We also abstract the approach in order to generalize it to other domains that need an integrated approach to planning and scheduling.Publicad