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

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

Arguing Using Opponent Models

Peer reviewedPostprin

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

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

Implicit dialogical premises, explanation as argument: a corpus-based reconstruction

This paper focuses on an explanation in a newspaper article: why new European Union citizens will come to the UK from Eastern Europe (e.g., because of available jobs). Using a corpus-based method of analysis, I show how regular target readers have been positioned to generate premises in dialogue with the explanation propositions, and thus into an understanding of the explanation as an argument, one which contains a biased conclusion not apparent in the text. Employing this method, and in particular ‘corpus comparative statistical keywords’, I show how two issues can be freshly looked at: implicit premise recovery; the argument/explanation distinction

Argumentation-based design rationale - the sharpest tools in the box

In this paper the three main argumentation-based design rationale methodologies - IBIS, QOC and DRL – will be discussed with illustrations of particular points drawn from a working example. The areas of scope, expressiveness in terms of design space and argumentation representation and the resulting usability by human and computer will be examined. Particular attention is paid to how the development of the artifact is being controlled by the evaluation of intentions and objectives that allow consistent goals throughout the design to be formulated, evaluated and modified. Furthermore, decision making within an argumentative context is highlighted