Abstract
In its operating life, an agent that needs to act in real environments is required to deal with rules and constraints that humans ask to satisfy. The set of rules specified by the human might influence the role of the agent without changing its goal or its current task. To this end, classical planning methodologies can be enriched with temporal goals and constraints that enforce non-Markovian properties on past traces. This work aims at exploring the application of real-time dynamic generation of policies whose possible trajectories are compliant with a set of Pure-Past Linear Time Logic rules, introducing novel human-robot interaction modalities for the high-level control of strategies for multiple agents. For proving the effectiveness of the proposed approach, we have carried out an evaluation on a partially observable, unpredictable, and dynamic scenario: the RoboCup soccer competition. In particular, we exploit human indications to condition the robot’s behavior before or during the time of the match, as happens during human soccer matches.
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References
Aeronautiques, C., et al.: PDDL—the planning domain definition language (1998)
Antonioni, E., Riccio, F., Nardi, D.: Improving sample efficiency in behavior learning by using sub-optimal planners for robots. In: Alami, R., Biswas, J., Cakmak, M., Obst, O. (eds.) RoboCup 2021. LNCS (LNAI), vol. 13132, pp. 103–114. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-98682-7_9
Antonioni, E., Suriani, V., Riccio, F., Nardi, D.: Game strategies for physical robot soccer players: a survey. IEEE Trans. Games 13(4), 342–357 (2021)
Antonioni, E., Suriani, V., Solimando, F., Nardi, D., Bloisi, D.D.: Learning from the crowd: improving the decision making process in robot soccer using the audience noise. In: Alami, R., Biswas, J., Cakmak, M., Obst, O. (eds.) RoboCup 2021. LNCS (LNAI), vol. 13132, pp. 153–164. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-98682-7_13
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)
De Giacomo, G., Favorito, M., Fuggitti, F.: Planning for temporally extended goals in pure-past linear temporal logic: a polynomial reduction to standard planning (2022). https://doi.org/10.48550/ARXIV.2204.09960
De Giacomo, G., Fuggitti, F.: FOND4LTL: fond planning for LTL//PLTL/goals as a service (2021)
De Giacomo, G., Rubin, S.: Automata-theoretic foundations of fond planning for LTLF and LDLF goals. In: IJCAI, pp. 4729–4735 (2018)
De Giacomo, G., Vardi, M.Y.: Linear temporal logic and linear dynamic logic on finite traces. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI 2013, pp. 854–860. AAAI Press (2013)
Fox, M., Long, D.: PDDL2.1: an extension to PDDL for expressing temporal planning domains. J. Artif. Intell. Res. 20, 61–124 (2003)
Gastin, P., Oddoux, D.: LTL with past and two-way very-weak alternating automata. In: Rovan, B., VojtĂ¡Å¡, P. (eds.) MFCS 2003. LNCS, vol. 2747, pp. 439–448. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45138-9_38
Gerevini, A., Long, D.: Preferences and soft constraints in PDDL3. In: ICAPS Workshop on Planning with Preferences and Soft Constraints, pp. 46–53 (2006)
Gillet, N., Vallerand, R.J., Amoura, S., Baldes, B.: Influence of coaches’ autonomy support on athletes’ motivation and sport performance: a test of the hierarchical model of intrinsic and extrinsic motivation. Psychol. Sport Exercise 11, 155–161 (2010)
Hofmann, M., GĂ¼rster, F.: GOL-a language to define tactics in robot soccer. In: Proceedings of the 10th Workshop on Humanoid Soccer Robots, in Conjunction with the IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS) (2015)
Reis, L.P., Lau, N.: COACH UNILANG - a standard language for coaching a (Robo) soccer team. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS (LNAI), vol. 2377, pp. 183–192. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45603-1_19
Röfer, T., et al.: B-Human team report and code release 2021 (2021). Only available online http://www.b-human.de/downloads/publications/2021/CodeRelease2021.pdf
Sinclair, D.A., Vealey, R.S.: Effects of coaches’ expectations and feedback on the self-perceptions of athletes. J. Sport Behav. 12, 77 (1989)
Vardi, M.Y.: An automata-theoretic approach to linear temporal logic. In: Moller, F., Birtwistle, G. (eds.) Logics for Concurrency. LNCS, vol. 1043, pp. 238–266. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-60915-6_6
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Musumeci, E., Suriani, V., Antonioni, E., Nardi, D., Bloisi, D.D. (2023). Adaptive Team Behavior Planning Using Human Coach Commands. In: Eguchi, A., Lau, N., Paetzel-PrĂ¼smann, M., Wanichanon, T. (eds) RoboCup 2022: Robot World Cup XXV. RoboCup 2022. Lecture Notes in Computer Science(), vol 13561. Springer, Cham. https://doi.org/10.1007/978-3-031-28469-4_10
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