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An Interactive Tool for Plan Generation, Inspection, and Visualization

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Knowledge Engineering Tools and Techniques for AI Planning

Abstract

In mixed-initiative planning systems, humans and AI planners work together for generating satisfactory solution plans or making easier solving hard planning problems, which otherwise would require much greater human planning efforts or much more computational resources. In this approach to plan generation, it is important to have effective plan visualization capabilities, as well to support the user with some interactive capabilities for the human intervention in the planning process. This paper presents an implemented interactive tool for the visualization, generation, and revision of plans. The tool provides an environment through which the user can interact with a state-of-the-art domain-independent planner, and obtain an effective visualization of a rich variety of information during planning, including the reasons why an action is being planned or why its execution in the current plan is expected to fail, the trend of the resource consumption in the plan, and the temporal scheduling of the planned actions. Moreover, the proposed tool supports some ways of human intervention during the planning process to guide the planner towards a solution plan, or to modify the plan under construction and the problem goals.

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Notes

  1. 1.

    The word satisficing was coined by Simon to mean “rational enough” [36], and subsequently it was adopted by the optimization community to mean “good enough.” This term has also been adopted by the planning community to indicate planners aimed at computing plans of good quality, but with no guarantee of their optimality w.r.t. a specified plan metric [23].

  2. 2.

    In PDDL, numerical fluents are functions over real values.

  3. 3.

    The quality of the plan is automatically measured according to the metric expression specified in the problem formulation. In this example, the quality is expressed by the duration of the plan.

  4. 4.

    When a search step reaches an LA-graph with no flaw, the planner has found a valid plan. However, this plan is given in output only if its quality improves the quality of the previous output plan. In the example of Fig. 7.9, some valid plans are computed, but only the one found at about the 350th step is given as the third output plan.

  5. 5.

    Action (load ?p ?t ?a ?l) represents the movement of package ?p from location ?l onto area ?a in truck ?t, while action (unload ?p ?t ?a ?l) represents the opposite movement.

  6. 6.

    In LPG, the evaluation of the successor LA-graph obtained by removing an action a supporting a precondition g is the estimated number of search steps required to support g by planning actions different from a.

  7. 7.

    LPG is written in C and is available from http://lpg.ing.unibs.it, while the user interface is written in Java and will soon be made publicly available.

  8. 8.

    In this experiment, the similarity threshold is set to 1, i.e., the memorized human decisions are reused only if the current neighborhood is the same as the neighborhood previously evaluated by the user.

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Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Brescia, Brescia, Italy

    Alfonso E. Gerevini & Alessandro Saetti

Authors
  1. Alfonso E. Gerevini
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  2. Alessandro Saetti
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Correspondence to Alessandro Saetti .

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Editors and Affiliations

  1. Department of Computer Science, University of Huddersfield, Huddersfield, UK

    Mauro Vallati

  2. Department of Computer Science, University of Huddersfield, Huddersfield, UK

    Diane Kitchin

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Gerevini, A.E., Saetti, A. (2020). An Interactive Tool for Plan Generation, Inspection, and Visualization. In: Vallati, M., Kitchin, D. (eds) Knowledge Engineering Tools and Techniques for AI Planning. Springer, Cham. https://doi.org/10.1007/978-3-030-38561-3_7

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  • DOI: https://doi.org/10.1007/978-3-030-38561-3_7

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