In this paper, I present my ongoing research on temporal defeasible argumentation-based multi-agent planning. In multi-agent planning a team of agents share a set of goals but have diverse abilities and temporal beliefs, which vary over time. In order to plan for these goals, agents start a stepwise dialogue consisting of exchanges of temporal plan proposals, plus temporal arguments against them, where both, actions with different duration, and temporal defeasible arguments, need to be integrated. This thesis proposes a computational framework for this research on multi-agent planning.

However, little is known regarding which algorithm is heuristic graph search algorithms. The analysis better in practice or the actual benefits of heuristic information is focused on the influence of heuristic information, in multiobjective search performance.

In heterogeneous multi-agent systems trust is necessary to improve interactions by enabling agents to choose good partners. Most trust models work by taking, in addition to direct experiences, other agents' communicated evaluations into account. However, in an open MAS other agents may use different trust models and the evaluations they communicate are based on different principles: as such they are meaningless without some form of alignment. My doctoral research gives a formal definition of this problem and proposes two methods of achieving an alignment.

The scalability of algorithms for solving Markov Decision Processes (MDPs) has been a limiting factor for MDPs as a modeling tool. This dissertation develops theoretical and empirical techniques for solving larger MDPs than was possible before, and aims to demonstrate the achieved progress by applying these new algorithms to a real-world problem.

Distributed Constraint Optimization Problems (DCOPs) can be optimally solved by distributed search algorithms, such as ADOPT and BnB-ADOPT. In centralized solving, maintaining soft arc consistency during search has proved to be beneficial for performance. In this thesis we aim to explore the maintenance of different levels of soft arc consistency in distributed search when solving DCOPs.

Finally, explore possible methods for decision making in general, have received a lot uses of combinatorial aggregation in sequential voting, of attention in the AI community in recent years. The reasons and discuss theoretical generalisations to more complex logical for this focus are clear: SCT provides tools for the analysis of languages and practical applications. Particularly close to the interests of AI is the to study binary aggregation procedures, inspired by research problem of social choice in combinatorial domains (Chevaleyre in AI. As long as we do not know the intended application of et al., 2008), where the space of alternatives the individuals the model, there is no appropriate set of axioms to concentrate have to choose from has a combinatorial structure. Instead, we prove characterisation results concerning one Definition 1.

Human Robot Interaction (HRI) is an active field of integrating and embedding different techniques in artificial intelligence. This paper describes my research topic on: Control of Robotic Systems for Safe Interaction with Human Operators. It consists of online motion generation for robotic manipulators interacting with dynamic obstacles and humans using a moving horizon scheme, modeling and long term prediction of human motion using probabilistic models and reachability analysis, and development of an HRI demonstration platform.

One of the main research problems in Structural Bioinformatics is the analysis and prediction of three-dimensional structures (3-D) of polypeptides or proteins. The 1990’s Genome projects resulted in a large increase in the number of protein sequences. However, the number of identified 3-D protein structures has not followed the same trend.The determination of protein structure is experimentally expensive and time consuming. This makes scientists largely dependent on computational methods that can predict correct 3-D protein structures only from extended and full amino acid sequences. Several computational methodologies and algorithms have been proposed as a solution to the Protein Structure Prediction (PSP) problem. We briefly describe the AI techniques we have been used to tackle this problem.

This paper presents the current state of this research work, aimed to develop a methodology for designing Adaptive Virtual Organizations. This paper includes both completed and remaining work on this topic.

To encompass them, several extensions of Dung's argumentation framework (AF) [Dung, This extended research abstract describes an 1995] have been proposed, but the most general, as shown in argumentation-based approach to modelling articulated [Baroni et al., 2011], is the Argumentation Framework with decision making contexts. The approach Recursive Attacks (AF RA) formalism [Baroni et al., 2009b; encompasses a variety of argument and attack 2011]. In[Baroni et al., 2009a; 2010b] we showed how to organise schemes aimed at representing basic knowledge arguments that are instances of argument schemes in and reasoning patterns for decision support.