Context-Aware Multi-Agent Planning in intelligent environments

A system is context-aware if it can extract, interpret and use context information and adapt its functionality to the current context of use. Multi-agent planning generalizes the problem of planning in domains where several agents plan and act together, and share resources, activities, and goals. This contribution presents a practical extension of a formal theoretical model for Context-Aware Multi-Agent Planning based upon an argumentationbased defeasible logic. Our framework, named CAMAP, is implemented on a platform for open multiagent systems and has been experimentally tested, among others, in applications of ambient intelligence in the field of health-care. CAMAP is based on a multi-agent partial-order planning paradigm in which agents have diverse abilities, use an argumentation-based defeasible contextual reasoning to support their own beliefs and refute the beliefs of the others according to their context knowledge during the plan search process. CAMAP shows to be an adequate approach to tackle ambient intelligence problems as it gathers together in a single framework the ability of planning while it allows agents to put forward arguments that support or argue upon the accuracy, unambiguity and reliability of the context-aware information.This work is mainly supported by the Spanish Ministry of Science and Education under the FPU Grant Reference AP2009-1896 awarded to Sergio Pajares Ferrando, and Projects, TIN2011-27652-C03-01, and Consolider Ingenio 2010 CSD2007-00022.Pajares Ferrando, S.; Onaindia De La Rivaherrera, E. (2013). Context-Aware Multi-Agent Planning in intelligent environments. Information Sciences. 227:22-42. https://doi.org/10.1016/j.ins.2012.11.021S224222

Argumentation-based dialogues over cooperative plans

If autonomous agents operating with other agents in open systems are to fulfil their goals and design objectives, the need to discuss and agree upon plans of action is imperative. In this thesis I present work covering both theoretical research and practical development related to the use of argumentation-based dialogues as a way to coordinate actions in multi-agent planning scenarios. The necessity of coordination in multi-agent systems requires the development of mechanisms to propose, modify, share, monitor, and argue about plans. In this thesis I present an argumentation scheme to propose multi-agent plans and associated critical questions to critique the proposal. Such a detailed consideration of multi-agent plan composition contains the right characteristics to enable the justification of plans.This research builds upon research on practical reasoning for action proposals and considers multi-agent plan proposals where plans require several agents for their execution. A dialogue game protocol is also presented which is based on proposal framework. The protocol allows agents to engage in dialogues to agree on and modify plans based on persuasion and deliberation protocols. The detail encompassed by the argumentation scheme and critical questions means that there is a large number of critical questions, and so dialogues may be very lengthy. To overcome this issue, I investigated the issue of strategies for use with this dialogue game in terms of the different possible orderings in which critiques can be posed. The thesis presents an implementation that realises the theoretical framework in terms of a agents engaging in simulated dialogues to share and agree on a plan. The experiments allow us to investigate the effects of such strategies in terms of the number of questions issued to reach an agreement. Overall, the framework presented in this thesis allow agents to engage in dialogues over cooperative plan proposals in a structured way using well-founded argumentative principles

An empirical study of argumentation schemes in deliberative dialogue

Publisher PD

Learning policy constraints through dialogue

Publisher PD

Argumentation schemes for collaborative planning

Postprin

Dispute Resolution Using Argumentation-Based Mediation

Mediation is a process, in which both parties agree to resolve their dispute by negotiating over alternative solutions presented by a mediator. In order to construct such solutions, mediation brings more information and knowledge, and, if possible, resources to the negotiation table. The contribution of this paper is the automated mediation machinery which does that. It presents an argumentation-based mediation approach that extends the logic-based approach to argumentation-based negotiation involving BDI agents. The paper describes the mediation algorithm. For comparison it illustrates the method with a case study used in an earlier work. It demonstrates how the computational mediator can deal with realistic situations in which the negotiating agents would otherwise fail due to lack of knowledge and/or resources.Comment: 6 page

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