Abstract
Deception is an essential behavior in many card games. Despite this fact, it is not trivial to capture the intent of a human strategist when making deceptive decisions. That is even harder when dealing with deception in card games, where components of uncertainty, hidden information, luck and randomness introduce the need of case-based decision making. Approaching this problem along with the investigation of the game of Truco, a quite popular game in Southern regions of South America, this work presents an approach that combines active learning and Case-Based Reasoning (CBR) in which agents request a human specialist to review a reused game action retrieved from a case base containing played Truco hands. That happens when the agents are confronted with game situations that are identified as opportunities for deception. The goal is to actively capture problem-solving experiences in which deception can be used, and later employ such case knowledge in the enhancement of the deceptive capabilities of the Truco agents. Experimental results show that the use of the learned cases enabled different kinds of Truco agents to play more aggressively, being more deceptive and performing a larger number of successful bluffs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Buller, D.B., Burgoon, J.K.: Interpersonal deception theory. Commun. Theory 6, 203–242 (1996)
DePaulo, B.M., Lindsay, J.J., Malone, B.E., Muhlenbruck, L., Charlton, K., Cooper, H.: Cues to deception. Psychol. Bull. 129, 74–118 (2003)
Ekman, P.: Telling lies: clues to deceit in the marketplace, politics, and marriage. W.W. Norton & Company, Inc. (2009)
Billings, D., Davidson, A., Schaeffer, J., Szafron, D.: The challenge of poker. Artif. Intell. J. 134, 201–240 (2002)
Mantaras, De., et al.: Retrieval, reuse, revision and retention in case-based reasoning. Knowl. Eng. Rev. 20, 215–240 (2005)
Rubin, J., Watson, I.: Computer poker: a review. Artif. Intell. 175, 958–987 (2011)
Sandven, A., Tessem, B.: A case-based learner for Poker. In: The Ninth Scandinavian Conference on Artificial Intelligence (SCAI 2006), Helsinki, Finland (2006)
Paulus, G.B., Assunção, J.V.C., Silva, L.A.L.: Cases and clusters in reuse policies for decision-making in card games. In: IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI 2019), Portland, OR, pp. 1361–1365 (2019)
Paulus, G.B.: Cases and clusters for the development of decision-making reuse policies in card games (written in Portuguese). In: Programa de Pós-Graduação em Ciência da Computação, vol. Master in Computer Science, p. 132. Universidade Federal de Santa Maria (2020)
Moral, R.C.B., Paulus, G.B., Assunção, J.V.C., Silva, L.A.L.: Investigating case learning techniques for agents to play the card game of Truco. In: XIX Brazilian Symposium on Computer Games and Digital Entertainment (SBGames 2020), Recife, Brazil, pp. 107–116 (2020)
Winne, L.L.: Truco. Ediciones Godot, Ciudad Autónoma de Buenos Aires (2017)
Settles, B.: Active Learning Literature Survey. Department of Computer Sciences, University of Wisconsin–Madison (2009)
Richter, M.M.: Knowledge containers. In: Watson, I. (ed.) Readings in Case-Based Reasoning. Morgan Kaufmann Publishers, San Francisco (2003)
Neto, H.C., Julia, R.M.S.: ACE-RL-Checkers: decision-making adaptability through integration of automatic case elicitation, reinforcement learning, and sequential pattern mining. Knowl. Inf. Syst. 57(3), 603–634 (2018). https://doi.org/10.1007/s10115-018-1175-0
Floyd, M.W., Esfandiari, B.: Supplemental observation acquisition for learning by observation agents. Appl. Intell. 48(11), 4338–4354 (2018). https://doi.org/10.1007/s10489-018-1191-5
Ontanon, S., Floyd, M.: A comparison of case acquisition strategies for learning from observations of state-based experts. In: The 26th International Florida Artificial Intelligence Research Society Conf. (FLAIRS 2013), Florida, USA (2013)
Ross, S., Bagnell, D.: Efficient reductions for imitation learning. In: Yee Whye, T., Mike, T. (eds.) The Thirteenth International Conference on Artificial Intelligence and Statistics, vol. 9, pp. 661–668. PMLR (2010)
Ross, S., Gordon, G., Bagnell, J.A.: A reduction of imitation learning and structured prediction to no-regret online learning. In: The 14th International Conference on Artificial Intelligence and Statistics (AISTATS), Ft. Lauderdale, FL, pp. 627–635 (2011)
Packard, B., Ontanon, S.: Policies for active learning from demonstration. In: 2017 AAAI Spring Symposium Series. Stanford University (2017)
Packard, B., Ontanon, S.: Learning behavior from limited demonstrations in the context of games. In: The 31st Int. Florida Artificial Intelligence Research Society Conf. (FLAIRS 2018), Florida, USA (2018)
Miranda, M., Sánchez-Ruiz, A.A., Peinado, F.: Towards human-like bots using online interactive case-based reasoning. In: Bach, K., Marling, C. (eds.) ICCBR 2019. LNCS (LNAI), vol. 11680, pp. 314–328. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29249-2_21
Floyd, M.W., Esfandiari, B.: An active approach to automatic case generation. In: McGinty, L., Wilson, D.C. (eds.) ICCBR 2009. LNCS (LNAI), vol. 5650, pp. 150–164. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02998-1_12
Rubin, J., Watson, I.: Case-based strategies in computer Poker. AI Commun. 25, 19–48 (2012)
Sobrinho, M.G.: Manual do jogo do Truco Cego (Flor de Abóbora). Martins Livreiro Editora Ltda., Porto Alegre (2004)
ACPC: Annual Computer Poker Competition. http://www.computerpokercompetition.org/ (2018)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Vargas, D.P., Paulus, G.B., Silva, L.A.L. (2021). Active Learning and Case-Based Reasoning for the Deceptive Play in the Card Game of Truco. In: Britto, A., Valdivia Delgado, K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science(), vol 13073. Springer, Cham. https://doi.org/10.1007/978-3-030-91702-9_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-91702-9_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91701-2
Online ISBN: 978-3-030-91702-9
eBook Packages: Computer ScienceComputer Science (R0)