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Title: Robot Teams for Multi-Objective Tasks
Tutor: Nardi, Daniele
Keywords: multi-robot
game theory
Issue Date: 2008
Abstract: Artificial Intelligence research has developed, during the last fifty years, a large variety of tools aimed at establishing rational behaviors for cognitive entities, called agents. This dissertation addresses the problem of producing rational behaviors for a team of agents pursuing possibly different objectives. The problem can be decomposed into the following two research issues: i) multi-agent behavior and execution modelling, and, ii) multi-objective problem solving. Our resarch focus on multi-agent systems has been modelling distributed execution of asynchronous plans composed of actions of uncertain duration, possibly coordinated through direct communication. The distributed execution and the communication costs require to model the dynamics of knowledge when asynchronously distributed in the system under the effect of local and communication actions. The second research focus of this thesis, has been multi-objective problem solving. The introduction of multiple objectives in planning domains, allows us to generalize classical multi-agent planning, thus augmenting the class of solvable problems. Multi-objective formulations allow an incomplete, and possibly contradictory, description of goals, and are frequent in many practical applications. For example, consider the case where requests to a system come from a large community of users or from the members of a research group studying different aspects of a complex problem. This thesis provides three main contributions. The first contribution consists of two formal tools for modelling multi-agent systems. One, for planning, and, one, for distributed execution. Each model defines a class of languages based on single-agent action languages and Petri nets, respectively. The second contribution addresses two multi-objective issues: solution concept and solving techniques. First, we define a novel solution concept which is, to our knowledge, the first refinement of Pareto optimality for any multi-objective problem. Second, we provide a sound and complete algorithm for solving it. Finally, the third contribution is a case study on the Urban Search And Rescue (USAR) robotic problem, presented in three formulations of increasing complexity. USAR, in its classical formulation, is a multi-objective problem where the objectives are: exploration, mapping, and victim detection.
Research interests: artificial intelligence, multi-robot systems, multi-agent systems, mechanism design, crowd-sourcing, game-theory, applications for the cloud
Skills short description: I am owner and director of the R&D Lab at Algorithmica Srl, where I am also PI for the cloud-tm project ( I obtained my masters degree (Magna cum Laude) in 2004, and my PhD, both under the supervision of by Prof. Daniele Nardi. I obtained, in 2004 , a PhD fellowship at DIS and then, and, in 2005, a Marie Curie Fellowship for training and doing research in the excellence site of the "Albert Ludwigs" University of Freiburg, under the supervision of Prof. Bernhard Nebel. In 2006, I have been World Champion at RoboCup: the main international scientific competition for autonomous robots. In particular, I won the Rescue Virtual Robots league and the First Prize in the "Best in Class in Autonomy" competition in the Robocup Rescue Real Robots League. Moreover, I developed PNP, a general and expressive language for representing behaviours. PNPs have been used in various application domains (i.e. soccer, search and rescue, and surveillance) and on several robotic platforms (i.e. wheeled, quadruped and humanoid). PNP obtained the “Best Robotic Demo Award” at AAMAS'08, the leading conference on multi-agent systems, with a demonstration of robust cooperative behaviours of two quadruped robots in a soccer domain.
Personal skills keywords: programming
algorithm design
application design
scientific writing
business management

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