Advertisement

An Interactive Tool for Plan Generation, Inspection, and Visualization

  • Alfonso E. Gerevini
  • Alessandro SaettiEmail author
Chapter
  • 24 Downloads

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.

Keywords

Interactive planning Mixed-initiative planning Plan visualization and inspection Graphical user interfaces for planning 

References

  1. 1.
    J. Allen and G. Ferguson, Human-machine collaborative planning, in Proc. of the 3rd Int. NASA Workshop on Planning and Scheduling for Space (2002).Google Scholar
  2. 2.
    A. Blum and M. Furst, Fast planning through planning graph analysis, in Artificial Intelligence. 90(1997) 281–300.Google Scholar
  3. 3.
    J. Bresina, A. Jonsson, P. Morris, and R. K. Activity planning for the Mars Exploration Rovers. in Proc. of the 15th Int. Conf. on Automated Planning and Scheduling, (Monterey, California, USA, 2005), pp. 40–49.Google Scholar
  4. 4.
    M. Cox and C. Zhang, Planning as mixed-initiative goal manipulation, in Proc. of the 15th Int. Conf. on Automated Planning and Scheduling, (Monterey, California, USA, 2005), pp. 282–291.Google Scholar
  5. 5.
    M. T. Cox and M. Veloso, Supporting Combined Human and Machine Planning, in Proc. of the 2nd Int. Conf. on Case-Based Reasoning, (Providence, Rhode Island, USA), pp. 531–540.Google Scholar
  6. 6.
    M. T. Cox and M. Veloso, Controlling for unexpected goals when planning in a mixed-initiative setting. in Proc. of the 8th Portuguese Conf. on Artificial Intelligence, (Coimbra, Portugal, 1997), pp. 309–318.Google Scholar
  7. 7.
    K. Currie and A. Tate, O-plan: the open planning architecture, in Artificial Intelligence. 52(1991):49–86.Google Scholar
  8. 8.
    Y. Dimopoulos, A. Gerevini, P. Haslum, and A. Saetti, The benchmark domains of the deterministic part of IPC-5, in Abstract Booklet of the competing planners of ICAPS-06, (Cumbria, UK, 2006), pp. 14–19.Google Scholar
  9. 9.
    P. Eyerich, R. Mattmüller, and G. Röger, Using Context-Enhanced Additive Heuristics for Temporal Numerical Planning, in Proc. of the 19th Int. Conf. on Automated Planning and Scheduling, (Thessaloniki, Greece, 2009), pp. 130–137.Google Scholar
  10. 10.
    G. Ferguson, J. Allen, and B. Miller, TRAINS-95: Towards a mixed-initiative planning assistant, in Proc. of the 3rd Conf. on Artificial Intelligence Planning Systems, (Edinburgh, UK, 1996) pp. 70–77.Google Scholar
  11. 11.
    G. Ferguson and J. F. Allen, Arguing about plans: Plan representation and reasoning for mixed-initiative planning, in Proc. of the 2nd Int. Conf. on AI Planning Systems, (Chicago, Illinois, 1994), pp. 43–48.Google Scholar
  12. 12.
    M. Fox and D. Long, PDDL2.1: An extension to PDDL for expressing temporal planning domains, in Journal of Artificial Intelligence Research. 20(2003):61–124.Google Scholar
  13. 13.
    A. Gerevini, P. Haslum, D. Long, A. Saetti and Y. Dimopoulos, Deterministic Planning in the Fifth International Planning Competition: PDDL3 and Experimental Evaluation of the Planners, in Artificial Intelligence. 173(2009):619–668.Google Scholar
  14. 14.
    A. Gerevini, A. Saetti, and I. Serina, Planning through stochastic local search and temporal action graphs, in Journal of Artificial Intelligence Research. 20(2003):239–290.Google Scholar
  15. 15.
    A. Gerevini, A. Saetti, and I. Serina, An empirical analysis of some heuristic features for local search in LPG, in Proc. of the 14th Int. Conf. on Automated Planning and Scheduling, (Whistler, Canada, 2004), pp. 171–180.Google Scholar
  16. 16.
    A. Gerevini, A. Saetti, and I. Serina, An approach to temporal planning and scheduling in domains with predictable exogenous events, in Journal of Artificial Intelligence Research. 25(2006):187–231.Google Scholar
  17. 17.
    A. Gerevini, A. Saetti, and I. Serina, An Approach to Efficient Planning with Numerical Fluents and Multi-Criteria Plan Quality, in Artificial Intelligence. 172(2009):899–944.Google Scholar
  18. 18.
    A. Gerevini and I. Serina, Fast plan adaptation through planning graphs: Local and systematic search techniques, in Proc. of the 5th Int. Conf. on Artificial Intelligence Planning and Scheduling, (Breckenridge, Colorado, USA, 2000), pp. 112–121.Google Scholar
  19. 19.
    A. Gerevini and I. Serina, Efficient Plan Adaptation through Replanning Windows and Heuristic Goals, in Journal of Algorithms in Cognition, Informatics and Logic. 102(2010):287–323.Google Scholar
  20. 20.
    M. Ghallab, A. Howe, C. Knoblock, D. McDermott, A. Ram, M. Veloso, D. Weld, D. Wilkins, PDDL – The Planning Domain Definition Language, CVC TR98-003/DCS TR-1165 (1998), Yale Center for Computational Vision and Control, available at http://cs-www.cs.yale.edu/homes/dvm/,
  21. 21.
    M. Helmert, The Fast Downward Planning System, in Journal of Artificial Intelligence Research. 26(2006) 191–246.Google Scholar
  22. 22.
    J. Hoffmann and B. Nebel, The FF Planning System: Fast Plan Generation Through Heuristic Search, in Journal of Artificial Intelligence Research. 14(2001):253–302.Google Scholar
  23. 23.
    J. Hoffmann and S. Edelkamp, The deterministic part of IPC-4: An overview, in Journal of Artificial Intelligence Research. 24(2005):519–579.Google Scholar
  24. 24.
    J. Hoffmann, S. Edelkamp, S. Thiebaux, R. Englert, F. Liporace and S. Trueg, Engineering Benchmarks for Planning: the Domains Used in the Deterministic Part of IPC-4, in Journal of Artificial Intelligence Research. 26(2006):453–541.Google Scholar
  25. 25.
    N. Lino and A. Tate, A visualisation approach for collaborative planning systems based on ontologies, in Proc. of the 8th Int. Conference on Information Visualisation, (London, England, UK, 2004), pp. 807–811.Google Scholar
  26. 26.
    N. Lino, A. Tate, and Y.-H. Chen-Burger. Semantic support for visualisation in collaborative AI planning. In Proc. of the Workshop on The Role of Ontologies in Planning and Scheduling (2005).Google Scholar
  27. 27.
    N. Lipovetzky and H. Geffner, Best-First Width Search: Exploration and Exploitation in Classical Planning, in Proc. of the 31st AAAI Conference on Artificial Intelligence, (San Francisco, USA, 2017), pp. 3590–3596.Google Scholar
  28. 28.
    D. Long and M. Fox, The 3rd international planning competition: Results and analysis, in Journal of Artificial Intelligence Research. 20(2003):1–59.Google Scholar
  29. 29.
    D. McAllester and D. Rosenblitt, Systematic nonlinear planning, in Proc. of the 9th National Conf. on Artificial Intelligence, (Anaheim, California, USA, 1991), pp. 634–639.Google Scholar
  30. 30.
    N. Muscettola, HSTS: Integrating Planning and Scheduling, in Intelligent Scheduling, eds. M. Zweben and M.S. Fox (Morgan Kauffmann, San Francisco, USA, 1994), pp. 169–212.Google Scholar
  31. 31.
    K. L. Myers, P. A. Jarvis, W. M. Tyson, and M. J. Wolverton, A mixed-initiative framework for robust plan sketching, in Proc. of the 13th Int. Conf. on Automated Planning and Scheduling, (Trento, Italy, 2003), pp. 256–265.Google Scholar
  32. 32.
    X. Nguyen and S. Kambhampati, Reviving partial order planning, in Proc. of the 17th Int. Joint Conf. on Artificial Intelligence, (Seattle, Washington, USA, 2001), pp. 459–464.Google Scholar
  33. 33.
    J. Penberthy and D. Weld, UCPOP: A sound, complete, partial order planner for ADL, in Proc. of the 3rd Int. Conf. on Principles of Knowledge Representation and Reasoning (Cambridge, Massachusetts, USA, 1992), pp. 103–114.Google Scholar
  34. 34.
    F. Pommerening, A. Torralba, T. Balyo, The ninth international planning competition (2018), https://ipc2018.bitbucket.io
  35. 35.
    S. Richter, M. Westphal, The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks, in Journal of Artificial Intelligence Research, 29(2010):127–177.Google Scholar
  36. 36.
    H. A. Simon, Models of Man, (John Wiley & Sons Inc., New York, USA, 1957).Google Scholar
  37. 37.
    A. Tate, In Advanced Planning Technology: Technological Achievements of the ARPA/Rome Laboratory Planning Initiative, (AAAI Press, Menlo Park, California, USA, 1996).Google Scholar
  38. 38.
    G. Tecuci, Proc. of the IJCAI Workshop on Mixed-Initiative Intelligent Systems, (AAAI Press, Menlo Park, California, USA, 2003).Google Scholar
  39. 39.
    M. Veloso, M. Mulvehill, A., and T. Cox, M, Rationale-supported mixed-initiative case-based planning, in Proc. of the 9th Conf. on Innovative Applications of Artificial Intelligence, (Providence, Rhode Island, USA, 1997), pp. 1072–1077.Google Scholar
  40. 40.
    C. Zhang, Cognitive models for mixed-initiative planning, (PhD thesis, Wright State University, Computer Science and Engineering Department, Dayton, Ohio, USA, 2002).Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversità degli Studi di BresciaBresciaItaly

Personalised recommendations