Modeling and Control of the Collaborative Behavior of Adaptive Autonomous Agents

Student   Mirgita Frasheri
Advisors   Alessandro V. Papadopoulos
Mikael Ekström
Baran Çürüklü
Faculty Reviewer   Ada Diaconescu, Telecom Paris, Institut Polytechnique de Paris, France
Grading Committee   Bengt Lennartson, Chalmers University of Technology, Sweden
Lars Karlsson, Örebro University, Sweden
Anna Syberfeldt, Högskolan i Skövde, Sweden
Markus Bohlin, Mälardalen University, Sweden (reserve)
Defence   Mälardalen University, Västerås, Sweden
Room U2-024 and Teams meeting (Link will be made public)
June 12, 2020 10:00
Abstract   Research on autonomous agents and vehicles has gained momentum in the past years, which is reflected in the extensive body of literature and the investment of big players of the industry in the development of products such as self-driving cars. Additionally, these systems are envisioned to continuously communicate and cooperate with one another in order to adapt to dynamic circumstances and unforeseeable events, and as a result will they fulfil their goals even more efficiently. The facilitation of such dynamic collaboration and the modelling of interactions between different actors (software agents, humans) remains an open challenge.

This thesis tackles the problem of enabling dynamic collaboration by investigating the automated adjustment of autonomy of different agents, called Adaptive Autonomy (AA). An agent, in this context, is a software able to process and react to sensory inputs in the environment in which it is situated in, and is additionally capable of autonomous actions. In this work, the AA of agents is driven by their willingness to interact with other agents, that captures the disposition of an agent to give and ask for help, based on different factors that represent the agent’s state and its interests. The AA approach to collaboration is used in two different domains: (i) the hunting mobile search problem, and (ii) the coverage problem of mobile wireless sensor networks. In both cases, the proposed approach is compared to state-of-art methods. Furthermore, the thesis contributes on a conceptual level by combining and integrating the AA approach – which is purely distributed – with a high-level mission planner, in order to exploit the ability of dealing with local and contingent problems through the AA approach, while minimizing the requests for a replan to the mission planner.
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Thesis   Thesis
Included Papers   Paper A: TAMER: Task Allocation in Multi-robot Systems Through an Entity-Relationship Model .
Paper B: Adaptive Autonomy in a Search and Rescue Scenario .
Paper C: GLocal: Comparison of Centralized Planning and Agent-Based Coordination .
Paper D: Modeling the Willingness to Interact in Cooperative Multi-Robot Systems .
Paper E: Adaptive Autonomy in Wireless Sensor Networks.
Publications   Complete list of publications

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Last modified: 2023-10-04 09:34:11 +0200