Abstract
We take a simple form of non-adversarial persuasion dialogue in which one participant (the persuader) aims to convince the other (the responder) to accept the topic of the dialogue by asserting sets of beliefs. The responder replies honestly to indicate whether it finds the topic to be acceptable (we make no prescription as to what formalism and semantics must be used for this, only assuming some function for determining acceptable beliefs from a logical knowledge base). Our persuader has a model of the responder, which assigns probabilities to sets of beliefs, representing the likelihood that each set is the responder’s actual beliefs. The beliefs the persuader chooses to assert and the order in which it asserts them (i.e. its strategy) can impact on the success of the dialogue and the success of a particular strategy cannot generally be guaranteed (because of the uncertainty over the responder’s beliefs). We define our persuasion dialogue as a classical planning problem, which can then be solved by an automated planner to generate a strategy that maximises the chance of success given the persuader’s model of the responder; this allows us to exploit the power of existing automated planners, which have been shown to be efficient in many complex domains. We provide preliminary results that demonstrate how the efficiency of our approach scales with the number of beliefs.
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References
Albore, A., Palacios, H., Geffner, H.: Compiling uncertainty away in non-deterministic conformant planning. In: Proc. of 19th European Conf. on Artificial Intelligence, pp. 465–470 (2010)
Besnard, P., Hunter, A.: A logic-based theory of deductive arguments. Artificial Intelligence 128(1-2), 203–235 (2001)
Black, E., Atkinson, K.: Choosing persuasive arguments for action. In: Proc. of 10th Int. Conf. on Autonomous Agents and Multiagent Systems, pp. 905–912 (2011)
Caminada, M.: On the issue of reinstatement in argumentation. In: Fisher, M., van der Hoek, W., Konev, B., Lisitsa, A. (eds.) JELIA 2006. LNCS (LNAI), vol. 4160, pp. 111–123. Springer, Heidelberg (2006)
Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intellegence 77, 321–357 (1995)
Dung, P.M., Kowalski, R.A., Toni, F.: Dialectic proof procedures for assumption-based, admissible argumentation. Artificial Intelligence 170(2), 114–159 (2006)
Egly, U., Gaggl, S.A., Woltran, S.: ASPARTIX: Implementing argumentation frameworks using answer-set programming. In: Garcia de la Banda, M., Pontelli, E. (eds.) ICLP 2008. LNCS, vol. 5366, pp. 734–738. Springer, Heidelberg (2008)
Fox, M., Long, D.: PDDL2.1: An extension to PDDL for expressing temporal planning domains. J. of Artificial Intelligence Research 20, 61–124 (2003)
García, A.J., Simari, G.R.: Defeasible logic programming an argumentative approach. Theory and Practice of Logic Programming 4(1-2), 95–138 (2004)
Hadidi, N., Dimopoulos, Y., Moraitis, P.: Tactics and concessions for argumentation-based negotiation. In: Proc. of the 4th Int. Conf. on Computational Models of Argument, pp. 285–296 (2012)
Hadjinikolis, C., Siantos, Y., Modgil, S., Black, E., McBurney, P.: Opponent modelling in persuasion dialogues. In: Proc. of the 23rd Int. Joint Conf. on Artificial Intelligence, pp. 164–170 (2013)
Hoffmann, J.: The Metric-FF planning system: Translating “ignoring delete lists” to numeric state variables. J. of Artificial Intelligence Research 20, 291–341 (2003)
Hoffmann, J., Nebel, B.: The FF planning system: Fast plan generation through heuristic search. J. of Artificial Intelligence Research 14, 253–302 (2001)
Hunter, A.: Analysis of dialogical argumentation via finite state machines. In: Liu, W., Subrahmanian, V.S., Wijsen, J. (eds.) SUM 2013. LNCS, vol. 8078, pp. 1–14. Springer, Heidelberg (2013)
Modgil, S., Prakken, H.: A general account of argumentation with preferences. Artificial Intelligence 195, 361–397 (2013)
Modgil, S., Toni, F., Bex, F., Bratko, I., Chesñevar, C.I., Dvořák, W., Falappa, M.A., Fan, X., Gaggl, S.A., García, A.J., González, M.P., Gordon, T.F., Leite, J., Možina, M., Reed, C., Simari, G.R., Szeider, S., Torroni, P., Woltran, S.: The added value of argumentation. In: Ossowski, S. (ed.) Agreement Technologies, pp. 357–403. Springer, Netherlands (2013)
Monteserin, A., Amandi, A.: Argumentation–based negotiation planning for autonomous agents. Decision Support Systems 51(3), 532–548 (2011)
Panisson, A.R., Farias, G., Freitas, A., Meneguzzi, F., Vieira, R., Bordini, R.H.: Planning interactions for agents in argumentation-based negotiation. In: Proc. of 11th Int. Workshop on Argumentation in Multi-Agent Systems (2014)
Prakken, H.: Formal systems for persuasion dialogue. The Knowledge Engineering Review 21(02), 163–188 (2006)
Reed, C., Long, D., Fox, M.: An architecture for argumentative dialogue planning. In: Gabbay, D.M., Ohlbach, H.J. (eds.) FAPR 1996. LNCS, vol. 1085, pp. 555–566. Springer, Heidelberg (1996)
Rienstra, T., Thimm, M., Oren, N.: Opponent models with uncertainty for strategic argumentation. In: Proc. of the 23rd Int. Joint Conf. on Artificial Intelligence (2013)
Taig, R., Brafman, R.I.: Compiling conformant probabilistic planning problems into classical planning. In: Proc. of 23rd Int. Conf. on Automated Planning and Scheduling (2013)
Thimm, M.: Strategic argumentation in multi-agent systems. In: Künstliche Intelligenz, Special Issue on Multi-Agent Decision Making (in press, 2014)
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Black, E., Coles, A., Bernardini, S. (2014). Automated Planning of Simple Persuasion Dialogues. In: Bulling, N., van der Torre, L., Villata, S., Jamroga, W., Vasconcelos, W. (eds) Computational Logic in Multi-Agent Systems. CLIMA 2014. Lecture Notes in Computer Science(), vol 8624. Springer, Cham. https://doi.org/10.1007/978-3-319-09764-0_6
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DOI: https://doi.org/10.1007/978-3-319-09764-0_6
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