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Network security games (NSGs) are widely used in security related domain to model the interaction between the attacker and the defender. However, due to the complex graph structure of the entire network, finding a Nash equilibrium even when the attacker ...

In this thesis, we aim at endowing robots with mechanisms to learn multimodal representations from sensory data and to allow them to execute tasks considering different subsets of available perceptions. We address the learning of these representations ...

I study settings of collective decision making where the members of a group may report intrinsically incomplete opinions. In such contexts, we need to design aggregation mechanisms that satisfy normatively desirable properties, are effective in ...

When robots act in an environment, there will be temporal uncertainty over the execution of their actions, i.e. the duration of an action and the time it takes place will be stochastic. The presence of multiple robots in the environment contributes ...

I present an overview of my research which investigates how models of human behavior can inform the design of new algorithms and interfaces. Specifically, I show how precise, testable computational methods and behavioral experiments can be used to ...

Numerous real-world problems involve multiple interacting entities and are inherently multi-objective in nature. Multi-objective multi-agent systems are a suitable paradigm to model such settings Despite the rising interest in this field, it has become ...

Our PhD research is concerned with the task of achieving cooperation in a system of competitive agents which cannot be explicitly controlled. To this end, it examines the problem from the system's point of view, without restricting the agents' behavior ...

Multiuser privacy (MP) is reported to cause concern among the users of online services, such as social networks, which do not support collective privacy management. In this research, informed by previous work and empirical studies in privacy, artificial ...

The process of determining the appropriate set of norms, referred to as synthesis, for a multiagent system has predominantly been carried out offline by the designers of the system. Of recent, there have been a few approaches that synthesise norms ...

The problem of promoting cooperative behaviour in a complex dynamical network of interacting individuals (e.g. social and epidemic networks or networks of opinion) has been intensely investigated across diverse fields of behavioural, social and ...

Coalition formation games aim at predicting the cooperative behavior of agents when forming alliances. Agents entertain preferences over coalition structures, and the goal is to find a coalition structure that is good for both individual agents and the ...

The problem of multi-agent resource allocation is important and well-studied within AI and economics. The general assumption is that the amount of each resource is known beforehand. However, many real-world problems, the exact amount of each resource ...

Recent advances in fields such as computer vision and natural language processing have created new opportunities for developing agents that can automatically interpret their environment. Concurrently, advances in artificial intelligence have made the ...

Recent years have seen a push towards deploying fully autonomous robots in large, complex domains such as autonomous driving, space exploration, and service robots. However, legal, ethical, or technical constraints have limited the extent of these ...

Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows to reach the required levels of robustness and persistency. The aim of my PhD thesis is to develop new ...

This paper presents part of the work developed so far within the scope of my PhD and suggests possible future research directions. My thesis tackles the problem of multi-robot coordination under uncertainty over the long-term. We present a preliminary ...

While reinforcement learning (RL) has helped artificial agents solve challenging tasks, high sample complexity is still a major concern. Inter-agent teaching -- endowing agents with the ability to respond to instructions from others -- has been ...

This paper presents a survey of issues relating to explainability in Human-Agent Systems. We consider fundamental questions about the Why, Who, What, When and How of explainability. First, we define explainability and its relationship to the related ...

Many real-world decision problems are inherently multi-objective in nature and concern multiple actors, making multi-objective multi-agent systems a key domain to study. We argue that trade-offs between conflicting objective functions should be analysed ...

The literature on norm emergence and normative MAS considers norms from two perspectives, namely: prescriptive norms using deontic concepts, and emergent norms that capture preference behaviour. We find that both perspectives lend themselves naturally ...