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In Defence of Design Patterns for AI Planning Knowledge Models

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AIxIA 2020 – Advances in Artificial Intelligence (AIxIA 2020)

Abstract

Design patterns are widely used in various areas of computer science, the most notable example being software engineering. They have been introduced also for supporting the encoding of automated planning knowledge models, but up till now, with little success.

In this paper, we investigate the merits of design patterns, as an example of the broader class of reusable abstractions, in the automated planning context; particularly, we aim at drawing attention to their potential usefulness for the explainability of domain-independent planning systems. Further, we argue that to foster the use of design patterns, there is a need for a centralised repository, and we describe the functionalities that such repository should provide to support knowledge engineers.

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Notes

  1. 1.

    For an overview of the available KEPS tools, the interested reader is referred to [27].

  2. 2.

    Low-level in the sense that the design of the language is much influenced by its use in plan generation engines, much as machine code is designed to align with the execution architecture of the machine that runs it.

  3. 3.

    https://www.icaps-conference.org/competitions/.

  4. 4.

    http://editor.planning.domains/.

  5. 5.

    https://sourcemaking.com/.

  6. 6.

    https://conceptnet.io/.

  7. 7.

    https://www.icaps-conference.org/competitions/.

References

  1. Alexander, C.: A Pattern Language: Towns, Buildings, Construction. Oxford University Press (1977)

    Google Scholar 

  2. Bernardi, G., Cesta, A., Orlandini, A., Umbrico, A., Mayer, M.C.: A language for timeline-based planning. In: Proceedings of the 2nd Workshop on Artificial Intelligence and Formal Verification, Logic, Automata, and Synthesis, pp. 53–58 (2020)

    Google Scholar 

  3. Caminada, M.W., Kutlak, R., Oren, N., Vasconcelos, W.W.: Scrutable plan enactment via argumentation and natural language generation. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, pp. 1625–1626 (2014)

    Google Scholar 

  4. Chakraborti, T., Kulkarni, A., Sreedharan, S., Smith, D.E., Kambhampati, S.: Explicability? Legibility? Predictability? Transparency? Privacy? Security? The emerging landscape of interpretable agent behavior. In: Proceedings of the Twenty-Ninth International Conference on Automated Planning and Scheduling, ICAPS, pp. 86–96 (2019)

    Google Scholar 

  5. Chrpa, L., McCluskey, T.L., Vallati, M., Vaquero, T.S.: The fifth international competition on knowledge engineering for planning and scheduling: summary and trends. AI Mag. 38(1), 104–106 (2017)

    Google Scholar 

  6. Clark, A., Fox, C., Lappin, S.: The Handbook of Computational Linguistics and Natural Language Processing. Wiley, Hoboken (2013)

    Google Scholar 

  7. Durán, O.: Computer-aided maintenance management systems selection based on a fuzzy AHP approach. Adv. Eng. Softw. 42(10), 821–829 (2011)

    Article  Google Scholar 

  8. Eifler, R., Cashmore, M., Hoffmann, J., Magazzeni, D., Steinmetz, M.: A new approach to plan-space explanation: analyzing plan-property dependencies in oversubscription planning. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI, pp. 9818–9826 (2020)

    Google Scholar 

  9. Fox, M., Long, D., Magazzeni, D.: Explainable planning. CoRR abs/1709.10256 (2017)

    Google Scholar 

  10. Fox, M., Long, D., Tamboise, G., Isangulov, R.: Creating and executing a well construction/operation plan (2018). uS Patent App. 15/541,381

    Google Scholar 

  11. Gamma, E., Helm, R., Vlissides, J., Johnson, R.: Design Patterns Elements of Reusable Object-Oriented Software. Addison-Wesley (1997)

    Google Scholar 

  12. Garrido, A., Morales, L., Serina, I.: Using AI planning to enhance e-learning processes. In: Proceedings of the Twenty-Second International Conference on Automated Planning and Scheduling, ICAPS. AAAI (2012)

    Google Scholar 

  13. Ghallab, M., Nau, D., Traverso, P.: Automated Planning: Theory and Practice. Elsevier (2004)

    Google Scholar 

  14. Lindsay, A.: Towards exploiting generic problem structures in explanations for automated planning. In: Proceedings of the 10th International Conference on Knowledge Capture, K-CAP, pp. 235–238 (2019)

    Google Scholar 

  15. Lindsay, A.: Using generic subproblems for understanding and answering queries in XAIP. In: Proceedings of KEPS (2020)

    Google Scholar 

  16. Lipovetzky, N., Burt, C.N., Pearce, A.R., Stuckey, P.J.: Planning for mining operations with time and resource constraints. In: Proceedings of the International Conference on Automated Planning and Scheduling (2014)

    Google Scholar 

  17. Liu, D., McCluskey, T.: The OCL language manual. Technical report, Version 1.2. Technical report, Department of Computing and Mathematical (2000)

    Google Scholar 

  18. McCluskey, T.L., et al.: Knowledge Engineering for Planning Roadmap (2003). http://scom.hud.ac.uk/planet/home

  19. McCluskey, T.L., Simpson, R.M.: Knowledge formulation for AI planning. In: Motta, E., Shadbolt, N.R., Stutt, A., Gibbins, N. (eds.) EKAW 2004. LNCS (LNAI), vol. 3257, pp. 449–465. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30202-5_30

    Chapter  Google Scholar 

  20. McCluskey, T.L., Vallati, M.: Embedding automated planning within urban traffic management operations. In: Proceedings of the International Conference on Automated Planning and Scheduling (2017)

    Google Scholar 

  21. McCluskey, T.L., Vaquero, T.S., Vallati, M.: Engineering knowledge for automated planning: towards a notion of quality. In: Proceedings of the Knowledge Capture Conference, K-CAP, pp. 14:1–14:8 (2017)

    Google Scholar 

  22. McCluskey, T., Simpson, R.: Towards an algebraic formulation of domain definitions using parameterised machines. In: Proceedings of the Annual UK PLANSIG Workshop (2005)

    Google Scholar 

  23. Parkinson, S., Longstaff, A.P.: Multi-objective optimisation of machine tool error mapping using automated planning. Expert Syst. Appl. 42(6), 3005–3015 (2015)

    Article  Google Scholar 

  24. Porras, G.C., Guéhéneuc, Y.G.: An empirical study on the efficiency of different design pattern representations in UML class diagrams. Empirical Softw. Eng. 15(5), 493–522 (2010)

    Article  Google Scholar 

  25. Ramírez, M., et al.: Integrated hybrid planning and programmed control for real time UAV maneuvering. In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS, pp. 1318–1326 (2018)

    Google Scholar 

  26. Saaty, T.L., Vargas, L.G.: Models, Methods, Concepts & Applications of the Analytic Hierarchy Process, vol. 175. Springer (2012). https://doi.org/10.1007/978-1-4614-3597-6

  27. Silva, J.R., Silva, J.M., Vaquero, T.S.: Formal knowledge engineering for planning: pre and post-design analysis. In: Vallati, M., Kitchin, D. (eds.) Knowledge Engineering Tools and Techniques for AI Planning, pp. 47–65. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38561-3_3

    Chapter  Google Scholar 

  28. Simpson, R., Mccluskey, T., Long, D., Fox, M.: Generic types as design patterns for planning domain specification. In: AIPS2002 Workshop on Knowledge Engineering Tools and Techniques for AI Planning (2002)

    Google Scholar 

  29. Smith, D., Frank, J., Cushing, W.: The ANML language. In: Workshop on Knowledge Engineering for Planning and Scheduling (KEPS) (2008)

    Google Scholar 

  30. Sohrabi, S., Baier, J.A., McIlraith, S.A.: Preferred explanations: theory and generation via planning. In: Twenty-Fifth AAAI Conference on Artificial Intelligence (2011)

    Google Scholar 

  31. Speer, R., Chin, J., Havasi, C.: Conceptnet 5.5: an open multilingual graph of general knowledge. In: AAAI Conference on Artificial Intelligence, pp. 4444–4451 (2017)

    Google Scholar 

  32. Sreedharan, S., Chakraborti, T., Kambhampati, S.: Handling model uncertainty and multiplicity in explanations via model reconciliation. In: Proceedings of the Twenty-Eighth International Conference on Automated Planning and Scheduling, ICAPS, pp. 518–526 (2018)

    Google Scholar 

  33. Sreedharan, S., Srivastava, S., Smith, D., Kambhampati, S.: Why can’t you do that hal? explaining unsolvability of planning tasks. In: International Joint Conference on Artificial Intelligence (2019)

    Google Scholar 

  34. Thiébaux, S., Coffrin, C., Hijazi, H., Slaney, J.: Planning with MIP for supply restoration in power distribution systems. In: Proceedings of the International Joint Conference on Artificial Intelligence (2013)

    Google Scholar 

  35. Vallati, M., Chrpa, L.: On the robustness of domain-independent planning engines: the impact of poorly-engineered knowledge. In: Proceedings of the 10th International Conference on Knowledge Capture, K-CAP, pp. 197–204 (2019)

    Google Scholar 

  36. Vallati, M., Kitchin, D.E. (eds.): Knowledge Engineering Tools and Techniques for AI Planning. Springer, Heidelberg (2020). https://doi.org/10.1007/978-3-030-38561-3

  37. Vallati, M., McCluskey, L., Chrpa, L.: Towards explanation-supportive knowledge engineering for planning. In: Proceedings of XAIP (2018)

    Google Scholar 

  38. Vallati, M., McCluskey, T.L.: A quality framework for automated planning knowledge models. In: Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART) (2021)

    Google Scholar 

  39. Vaquero, T.S., Romero, V., Tonidandel, F., Silva, J.R.: itSIMPLE 2.0: an integrated tool for designing planning domains. In: Proceedings of the International Conference on Automated Planning and Scheduling, ICAPS, pp. 336–343 (2007)

    Google Scholar 

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Vallati, M., McCluskey, T.L. (2021). In Defence of Design Patterns for AI Planning Knowledge Models. In: Baldoni, M., Bandini, S. (eds) AIxIA 2020 – Advances in Artificial Intelligence. AIxIA 2020. Lecture Notes in Computer Science(), vol 12414. Springer, Cham. https://doi.org/10.1007/978-3-030-77091-4_12

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  • DOI: https://doi.org/10.1007/978-3-030-77091-4_12

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