Agent-based negotiation teams are negotiation parties formed by more than a single individual. Individuals unite as a single negotiation party because they share a common goal that is related to a negotiation with one or several opponents. My research goal is providing agent-based computational models for negotiation teams in multi-agent systems.

Distributed constraint optimization problems (DCOPs) are a model for representing multi-agent systems in which agents cooperate to optimize a global objective. The DCOP model has two main advantages: it can represent a wide range of problem domains, and it supports the development of generic algorithms to solve them. Firstly, this paper presents some advances in both complete and approximate DCOP algorithms. Secondly, it explains that the DCOP model makes a number of unrealistic assumptions that severely limit its range of application. Finally, it points out hints on how to tackle such limitations.

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.

Unfortunately, this example is a useful analogy can be treated within a unified framework along for many situations in real life, where (individually) rational with seemingly unrelated problems in the fields of judgment behavior leads to a disaster for the society.

Temporal regulations are needed to express commitments The proposal of Elisa Marengo's thesis is to extend to achieve something and in a specified order. For commitment protocols in order to (i) allow for expressing instance, an insurance company commits to paying an innetwork commitments to temporal regulations, and surgeon for a procedure only after a covered patient (ii) to supply a tool for expressing laws, conventions has undergone the procedure. Patterns of interaction, instead, and the like, in order to specify legal interactions.

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.

HAPTIC is grounded in game theory and probabilistic modeling. It has been proved that My thesis contributes to the field of multi-agent HAPTIC agents learn other agents' behaviors reliably using systems by proposing a novel trust-based decision direct observations. One shortcoming of HAPTIC is that it model for supply chain management.

Quantitative prediction problems involving both spatial and temporal components have appeared prominently in several disparate research areas including finance, supply chain management, and civil engineering. Unfortunately, either the spatial or temporal aspect tends to dominate the other in many prediction formulations. We briefly examine the underlying formulations used in spatial and temporal prediction. Then, we outline a method that combines these approaches and improves prediction results in high-dimensional economic domains by integrating multivariate and time series techniques which require minimal tuning but achieve superior performance compared to previous methods. We present preliminary results in the context of the Trading Agent Competition for Supply Chain Management.

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.

My work focuses on building computational agents that assist people in managing their activities in environments in which tempo and complexity outstrip people’s cognitive capacity,such as in coordinating rescue teams in the aftermath of a disaster, or in helping people with dementia manage their everyday lives. A critical challenge faced in such environments is not only that individuals must factor complicated constraints into deciding how and when to act on their own goals, but also that their decisions are further constrained by choices made by others with whom they interact, such as between cooperating teams in disaster relief or between patients and caregivers in an assisted-living facility. An additional challenge in such situations is that the interests of individuals, such as privacy and autonomy, along with slow, costly, uncertain,or otherwise problematic communication may further limitindividuals’ abilities to work together. My work assumes that a computational agent is associated with each individual, and that these agents will work together efficiently to manage individual and joint activities, while maintaining autonomy and privacy to the extent possible.