Abstract. Many companies are using MapReduce applications to process very large amounts of data. Static optimization of such applications is complex because they are based on user-defined operations, called map and reduce, which prevents some algebraic optimization. In order to optimize the task allocation, several systems collect data from previous runs and predict the performance doing job profiling. However they are not e↵ective during the learning phase, or when a new type of job or data set appears. In this paper, we present an adaptive multi-agent system for large data sets analysis with MapReduce. We do not preprocess data and we adopt a dynamic approach, where the reducer agents interact during the job. In order to decrease the workload of the most loaded reducer -and so the execution time -we propose a task re-allocation based on negotiation.

Abstract. Several recent works in the area of Artificial Intelligence focus on computational models of argumentation-based negotiation. However, even if computational models of arguments are used to encompass the reasoning of interacting agents, this logical approach does not come with an effective strategy for agents engaged in negotiations. In this paper we propose a realisation of the Minimal Concession (MC) strategy which has been theoretically validated. The main contribution of this paper is the integration of this intelligent strategy in a practical application by means of assumption-based argumentation. We claim here that the outcome of negotiations, which are guaranteed to terminate, is an optimal agreement (when possible) if the agents adopt the MC strategy.

representing knowledge, goals, and decisions. Preferences are attached to goals. These concrete data structures consist of information providing the backbone of arguments. Due to the abductive nature of practical commonsense reasoning, arguments are built by reasoning backwards. Moreover, arguments are defined as tree-like structures. Our framework is equipped with a computational counterpart (in the form of a formal mapping from it into a set of assumption-based argumentation frameworks). Indeed, we provide the mechanism for solving a decision problem, modeling the intuition that high-ranked goals are preferred to low-ranked goals which can be withdrawn. Thus, we give a clear semantics to the decisions. In this way, our framework suggests some decisions and provides an interactive and intelligible explanation of this choice. Our implementation, called MARGO, is a tool for multi-attribute qualitative decision-making as required, for instance in agent-based negotiation or in service-oriented agents. In a more practical context, our framework is amenable to industrial applications. In particular, MARGO has been used within the the ArguGRID project 1 for service selection and sevice negotiation. The paper is organised as follows. Section 2 introduces the basic notions of argumentation in the background of our work. Section 3 defines the core of our proposal, i.e. our argumentation-based framework for decision making. Firstly, we define the framework which captures decision problems. Secondly, we define the arguments. Thirdly, we formalize the interactions amongst arguments in order to define our AF (Argumentation Framework). Finally, we provide the computational counterpart of our framework. Section 4 outlines the implementation of our AF and its usage for service-oriented agents. Finally, section 5 discusses some related works and section 6 concludes with some directions for future work.

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