Abstract
Automated Planning is the subarea of AI devoted to developing algorithms that can solve sequential decision making problems. By taking a formal description of the environment, a planning algorithm generates a plan of actions (also called policy) that can guide an agent to accomplish a certain task. Classical planning assumes the environment is fully-observed and evolves in a deterministic way considering only simple reachability goals (e.g. a set of states to be reached by a plan or policy). In this work, we approach fully-observed non-deterministic planning (FOND) tasks which allow the specification of complex goals such as the preference over policy quality (weak, strong or strong-cyclic) and preferences over states in the paths generated by a policy. To solve this problem we propose formulae in \(\alpha \)-CTL (branching time) temporal logic and use planning as model checking algorithms based on \(\alpha \)-CTL to generate a solution that captures both, agent’s preferences and the desired policy quality. To evaluate the effectiveness of the proposed formulae and algorithms, we run experiments in the Rovers benchmark domain. Up to our knowledge, this is the first work to solve non-deterministic planning problems with preferences using a CTL temporal logic.
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References
Akers, S.B.: Binary decision diagrams. IEEE Trans. Comput. 27(06), 509–516 (1978)
Baier, J.A., Bacchus, F., McIlraith, S.A.: A heuristic search approach to planning with temporally extended preferences. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, pp. 1808–1815 (2007)
Baier, S., McIlraith, S.A.: Htn planning with preferences. In: 21st Int. Joint Conf. on Artificial Intelligence, pp. 1790–1797 (2009)
Bonet, B., De Giacomo, G., Geffner, H., Rubin, S.: Generalized planning: Non-deterministic abstractions and trajectory constraints. arXiv preprint arXiv:1909.12135 [S.l] (2019)
Camacho, A., Triantafillou, E., Muise, C., Baier, J.A., McIlraith, S.A.: Non-deterministic planning with temporally extended goals: Ltl over finite and infinite traces. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
Cimatti, A., Pistore, M., Roveri, M., Traverso, P.: Weak, strong, and strong cyclic planning via symbolic model checking. Artif. Intell. 147(1–2), 35–84 (2003)
Edmund, M., Clarke, J., Grumberg, O., Peled, D.A.: Model checking. MIT Press. [S.l] p. 314 (1999)
Gerevini, A.E., Haslum, P., Long, D., Saetti, A., Dimopoulos, Y.: Deterministic planning in the fifth international planning competition: Pddl3 and experimental evaluation of the planners. Artif. Intell. 173(5–6), 619–668 (2009)
Ghallab, M., Nau, D., Traverso, P.: Automated Planning: theory and practice. Elsevier (2004)
Hoffmann, J.: FF: the fast-forward planning system. AI Mag. 22(3), 57–57 (2001)
Hoffmann, J.: Everything you always wanted to know about planning. In: Annual Conference on Artificial Intelligence, pp. 1–13. Springer (2011)
Hsu, C.W., Wah, B.W., Huang, R., Chen, Y.: Constraint partitioning for solving planning problems with trajectory constraints and goal preferences. In: IJCAI, pp. 1924–1929 (2007)
Kautz, H., Selman, B.: Unifying sat-based and graph-based planning. In: IJCAI, vol. 99, pp. 318–325 (1999)
Kim, J., Banks, C.J., Shah, J.A.: Collaborative planning with encoding of users’ high-level strategies. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
Kindler, E.: Safety and liveness properties: a survey. Bull. Eur. Assoc. Theor. Comput. Sci. 53(268–272), 30 (1994)
Lamport, L.: Proving the correctness of multiprocess programs. IEEE Trans. Software Eng. 2, 125–143 (1977)
de Menezes, M.V., de Barros, L.N., do Lago Pereira, S.: Symbolic regression for non-deterministic actions (2014)
Menezes, Maria Viviane de: Mudanças em Problemas de Planejamento sem Solução. 2014. 127 f. Ph.D. thesis, Universidade de São Paulo, São Paulo (2014)
Muise, C.J., McIlraith, S.A., Beck, C.: Improved non-deterministic planning by exploiting state relevance. In: Twenty-Second International Conference on Automated Planning and Scheduling (2012)
Percassi, F., Gerevini, A.E.: On compiling away pddl3 soft trajectory constraints without using automata. In: Proceedings of the International Conference on Automated Planning and Scheduling, vol. 29, pp. 320–328 (2019)
Pereira, S.d.L.: Planejamento sob incerteza para metas de alcancabilidade estendidas. Ph.D. thesis, Universidade de São Paulo, São Paulo (2007)
Pereira, S.L., de Barros, L.N.: A logic-based agent that plans for extended reachability goals. Auton. Agent. Multi-Agent Syst. 16(3), 327–344 (2008)
Piterman, N., Pnueli, A.: Temporal logic and fair discrete systems. In: Handbook of Model Checking, pp. 27–73 (2018)
Rintanen, J.: Regression for classical and nondeterministic planning. In: ECAI 2008, pp. 568–572. IOS Press (2008)
Santhanam, G.R., Basu, S., Honavar, V.: Representing and reasoning with qualitative preferences: tools and applications. Synthesis Lectures Artif. Intell. Mach. Learn. 10(1), 1–154 (2016)
Santos, R.M.d.: Especificação de preferências de planos usando metas estendidas na lógica alpha-ctl (2019)
Santos, V.B.d., Barros, L.N.d., Pereira, S.d.L., Menezes, M.V.d.: Symbolic fond planning for temporally extended goals. In: Workshop on Knowledge Engineering for Planning and Scheduling (2022)
dos Santos, V.M.B., de Barros, L.N., de Menezes, M.V.: Symbolic planning for strong-cyclic policies. In: 2019 8th Brazilian Conference on Intelligent Systems (BRACIS), pp. 168–173. IEEE (2019)
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Machado, W.C.C., dos Santos, V.B., de Barros, L.N., de Menezes, M.V. (2023). Specifying Preferences over Policies Using Branching Time Temporal Logic. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14195. Springer, Cham. https://doi.org/10.1007/978-3-031-45368-7_9
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