A Dynamic Epistemic Logic for Abstract Argumentation

This paper introduces a multi-agent dynamic epistemic logic for abstract argumenta- tion. Its main motivation is to build a general framework for modelling the dynamics of a debate, which entails reasoning about goals, beliefs, as well as policies of com- munication and information update by the participants. After locating our proposal and introducing the relevant tools from abstract argumentation, we proceed to build a three-tiered logical approach. At the first level, we use the language of propositional logic to encode states of a multi-agent debate. This language allows to specify which arguments any agent is aware of, as well as their subjective justification status. We then extend our language and semantics to that of epistemic logic, in order to model individuals’ beliefs about the state of the debate, which includes uncertainty about the information available to others. As a third step, we introduce a framework of dynamic epistemic logic and its semantics, which is essentially based on so-called event models with factual change. We provide completeness results for a number of systems and show how existing formalisms for argumentation dynamics and unquantified uncerSynthese tainty can be reduced to their semantics. The resulting framework allows reasoning about subtle epistemic and argumentative updates—such as the effects of different levels of trust in a source—and more in general about the epistemic dimensions of strategic communication

Human-Agent Decision-making: Combining Theory and Practice

Extensive work has been conducted both in game theory and logic to model strategic interaction. An important question is whether we can use these theories to design agents for interacting with people? On the one hand, they provide a formal design specification for agent strategies. On the other hand, people do not necessarily adhere to playing in accordance with these strategies, and their behavior is affected by a multitude of social and psychological factors. In this paper we will consider the question of whether strategies implied by theories of strategic behavior can be used by automated agents that interact proficiently with people. We will focus on automated agents that we built that need to interact with people in two negotiation settings: bargaining and deliberation. For bargaining we will study game-theory based equilibrium agents and for argumentation we will discuss logic-based argumentation theory. We will also consider security games and persuasion games and will discuss the benefits of using equilibrium based agents.Comment: In Proceedings TARK 2015, arXiv:1606.0729

Pareto Optimality and Strategy Proofness in Group Argument Evaluation (Extended Version)

An inconsistent knowledge base can be abstracted as a set of arguments and a defeat relation among them. There can be more than one consistent way to evaluate such an argumentation graph. Collective argument evaluation is the problem of aggregating the opinions of multiple agents on how a given set of arguments should be evaluated. It is crucial not only to ensure that the outcome is logically consistent, but also satisfies measures of social optimality and immunity to strategic manipulation. This is because agents have their individual preferences about what the outcome ought to be. In the current paper, we analyze three previously introduced argument-based aggregation operators with respect to Pareto optimality and strategy proofness under different general classes of agent preferences. We highlight fundamental trade-offs between strategic manipulability and social optimality on one hand, and classical logical criteria on the other. Our results motivate further investigation into the relationship between social choice and argumentation theory. The results are also relevant for choosing an appropriate aggregation operator given the criteria that are considered more important, as well as the nature of agents' preferences

Designing and trusting multi-agent systems for B2B applications

This thesis includes two main contributions. The first one is designing and implementing B usiness-to-B usiness (B2B ) applications using multi-agent systems and computational argumentation theory. The second one is trust management in such multi-agent systems using agents' credibility. Our first contribution presents a framework for modeling and deploying B2B applications, with autonomous agents exposing the individual components that implement these applications. This framework consists of three levels identified by strategic, application, and resource, with focus here on the first two levels. The strategic level is about the common vision that independent businesses define as part of their decision of partnership. The application level is about the business processes, which are virtually integrated as result of this common vision. Since conflicts are bound to arise among the independent applications/agents, the framework uses a formal model based upon computational argumentation theory through a persuasion protocol to detect and resolve these conflicts. Termination, soundness, and completeness properties of this protocol are presented. Distributed and centralized coordination strategies are also supported in this framework, which is illustrated with an online purchasing case study followed by its implementation in Jadex, a java-based platform for multi-agent systems. An important issue in such open multi-agent systems is how much agents trust each other. Considering the size of these systems, agents that are service providers or customers in a B2B setting cannot avoid interacting with others that are unknown or partially known regarding to some past experience. Due to the fact that agents are self-interested, they may jeopardize the mutual trust by not performing the actions as they are supposed to. To this end, our second contribution is proposing a trust model allowing agents to evaluate the credibility of other peers in the environment. Our multi-factor model applies a number of measurements in trust evaluation of other party's likely behavior. After a period of time, the actual performance of the testimony agent is compared against the information provided by interfering agents. This comparison process leads to both adjusting the credibility of the contributing agents in trust evaluation and improving the system trust evaluation by minimizing the estimation error

KEMNAD: A Knowledge Engineering Methodology for Negotiating Agent Development

Automated negotiation is widely applied in various domains. However, the development of such systems is a complex knowledge and software engineering task. So, a methodology there will be helpful. Unfortunately, none of existing methodologies can offer sufficient, detailed support for such system development. To remove this limitation, this paper develops a new methodology made up of: (1) a generic framework (architectural pattern) for the main task, and (2) a library of modular and reusable design pattern (templates) of subtasks. Thus, it is much easier to build a negotiating agent by assembling these standardised components rather than reinventing the wheel each time. Moreover, since these patterns are identified from a wide variety of existing negotiating agents(especially high impact ones), they can also improve the quality of the final systems developed. In addition, our methodology reveals what types of domain knowledge need to be input into the negotiating agents. This in turn provides a basis for developing techniques to acquire the domain knowledge from human users. This is important because negotiation agents act faithfully on the behalf of their human users and thus the relevant domain knowledge must be acquired from the human users. Finally, our methodology is validated with one high impact system

Strategies in Case-Based Argumentation-Based Negotiation: An Application for the Tourism Domain

[EN] Negotiation is a key solution to find an agreement between conflicting parties especially during the purchase journey. This paper treats the negotiations between a travel agency and its customers in the domain of tourism. Both automated negotiation and argumentation are gathered to create a framework for automated agents, presenting a travel agency and its customers, to negotiate a trip and exchange arguments. Agents take advantage of their past experiences and use Case-Based Reasoning to select the best strategy to follow. We represent agents using two types of profiles, Argumentative profile that represents agents¿ ways of reasoning and Preference profile that embodies customers¿ preferences in the domain of tourism.Bouslama, R.; Jordán, J.; Heras, S.; Amor, NB. (2020). Strategies in Case-Based Argumentation-Based Negotiation: An Application for the Tourism Domain. Springer. 205-217. https://doi.org/10.1007/978-3-030-51999-5_17S205217Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)Adnan, M.H.M., Hassan, M.F., Aziz, I., Paputungan, I.V.: Protocols for agent-based autonomous negotiations: a review. In: ICCOINS, pp. 622–626. IEEE (2016)Amgoud, L., Parsons, S.: Agent dialogues with conflicting preferences. In: Meyer, J.-J.C., Tambe, M. (eds.) ATAL 2001. LNCS (LNAI), vol. 2333, pp. 190–205. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45448-9_14Amgoud, L., Prade, H.: Generation and evaluation of different types of arguments in negotiation. In: NMR, pp. 10–15 (2004)Bouslama, R., Ayachi, R., Ben Amor, N.: A new generic framework for argumentation-based negotiation using case-based reasoning. In: Medina, J., et al. (eds.) IPMU 2018. CCIS, vol. 854, pp. 633–644. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91476-3_52Bouslama, R., Ayachi, R., Ben Amor, N.: A new generic framework for mediated multilateral argumentation-based negotiation using case-based reasoning. In: Kern-Isberner, G., Ognjanović, Z. (eds.) ECSQARU 2019. LNCS (LNAI), vol. 11726, pp. 14–26. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29765-7_2Dimopoulos, Y., Moraitis, P.: Advances in argumentation based negotiation. In: Negotiation and Argumentation in Multi-agent Systems: Fundamentals, Theories, Systems and Applications, pp. 82–125 (2014)Hadidi, N., Dimopoulos, Y., Moraitis, P.: Tactics and concessions for argumentation-based negotiation. In: Computational Models of Argument: Proceedings of COMMA 2012, vol. 245, pp. 285–296 (2012)Hadoux, E., Hunter, A.: Strategic sequences of arguments for persuasion using decision trees. In: AAAI (2017)Heras, S., Jordán, J., Botti, V., Julián, V.: Argue to agree: a case-based argumentation approach. IJAR 54(1), 82–108 (2013)Heras, S., Jordán, J., Botti, V., Julián, V.: Case-based strategies for argumentation dialogues in agent societies. Inf. Sci. 223, 1–30 (2013)Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Sierra, C., Wooldridge, M.: Automated negotiation: prospects, methods and challenges. Int. J. Group Decis. Negot. 10(2), 199–215 (2001)Lazar, C.M.: Internet-an aid for e-tourism. Ecoforum J. 8(1), 1–4 (2019)Lopes, F., Novais, A.Q., Coelho, H.: Bilateral negotiation in a multi-agent energy market. In: Huang, D.-S., Jo, K.-H., Lee, H.-H., Kang, H.-J., Bevilacqua, V. (eds.) ICIC 2009. LNCS, vol. 5754, pp. 655–664. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04070-2_71Park, S., Tussyadiah, I., Mazanec, J., Fesenmaier, D.: Travel personae of american pleasure travelers: a network analysis. J. Travel Tour. Mark. 27, 797–811 (2010)Rahwan, I., Ramchurn, S.D., Jennings, N.R., Mcburney, P., Parsons, S., Sonenberg, L.: Argumentation-based negotiation. KER 18(4), 343–375 (2003)Rahwan, I., Sonenberg, L., McBurney, P.: Bargaining and argument-based negotiation: some preliminary comparisons. In: Rahwan, I., Moraïtis, P., Reed, C. (eds.) ArgMAS 2004. LNCS (LNAI), vol. 3366, pp. 176–191. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-32261-0_12Sierra, C., Jennings, N.R., Noriega, P., Parsons, S.: A framework for argumentation-based negotiation. In: Singh, M.P., Rao, A., Wooldridge, M.J. (eds.) ATAL 1997. LNCS, vol. 1365, pp. 177–192. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0026758Soh, L.K., Tsatsoulis, C.: Agent-based argumentative negotiations with case-based reasoning. In: AAAI Fall Symposium Series on Negotiation Methods for Autonomous Cooperative Systems, pp. 16–25 (2001)Sycara, K.P.: Persuasive argumentation in negotiation. Theory Decis. 28(3), 203–242 (1990). https://doi.org/10.1007/BF00162699Walton, D.: Argumentation Schemes for Presumptive Reasoning. Routledge, Abingdon (2013

Impact of Argument Type and Concerns in Argumentation with a Chatbot

Conversational agents, also known as chatbots, are versatile tools that have the potential of being used in dialogical argumentation. They could possibly be deployed in tasks such as persuasion for behaviour change (e.g. persuading people to eat more fruit, to take regular exercise, etc.) However, to achieve this, there is a need to develop methods for acquiring appropriate arguments and counterargument that reflect both sides of the discussion. For instance, to persuade someone to do regular exercise, the chatbot needs to know counterarguments that the user might have for not doing exercise. To address this need, we present methods for acquiring arguments and counterarguments, and importantly, meta-level information that can be useful for deciding when arguments can be used during an argumentation dialogue. We evaluate these methods in studies with participants and show how harnessing these methods in a chatbot can make it more persuasive

Manipulation in group argument evaluation.

Given an argumentation framework and a group of agents, the individuals may have divergent opinions on the status of the arguments. If the group needs to reach a common po- sition on the argumentation framework, the question is how the individual evaluations can be mapped into a collective one. This problem has been recently investigated in [1]. In this paper, we study under which conditions these operators are Pareto optimal and whether they are manipulable.Collective decision making; Argumentation; Judgment aggregation; Social choice theory;