no code implementations • 26 Oct 2016 • Sina Sajadmanesh, Sina Jafarzadeh, Seyed Ali Osia, Hamid R. Rabiee, Hamed Haddadi, Yelena Mejova, Mirco Musolesi, Emiliano De Cristofaro, Gianluca Stringhini
In this paper, we present a large-scale study of recipes published on the web and their content, aiming to understand cuisines and culinary habits around the world.
no code implementations • 13 Mar 2017 • Fani Tsapeli, Peter Tino, Mirco Musolesi
The abundance of data produced daily from large variety of sources has boosted the need of novel approaches on causal inference analysis from observational data.
no code implementations • 23 Oct 2017 • Benjamin Baron, Mirco Musolesi
Our everyday interactions with pervasive systems generate traces that capture various aspects of human behavior and enable machine learning algorithms to extract latent information about users.
BIG-bench Machine Learning Interpretable Machine Learning +1
no code implementations • 16 Nov 2017 • Gatis Mikelsons, Matthew Smith, Abhinav Mehrotra, Mirco Musolesi
We characterize the mobility patterns of individuals using the GPS metrics presented in the literature and employ these metrics as input to the network.
no code implementations • 27 Mar 2018 • Beatrice Perez, Mirco Musolesi, Gianluca Stringhini
Metadata are associated to most of the information we produce in our daily interactions and communication in the digital world.
no code implementations • 26 Sep 2018 • Nicolas Anastassacos, Mirco Musolesi
Using social dilemmas as the training ground, we present a novel learning architecture, Learning through Probing (LTP), where agents utilize a probing mechanism to incorporate how their opponent's behavior changes when an agent takes an action.
no code implementations • 8 Feb 2019 • Nicolas Anastassacos, Stephen Hailes, Mirco Musolesi
Social dilemmas have been widely studied to explain how humans are able to cooperate in society.
no code implementations • 11 Dec 2019 • Alessio Pagani, Abhinav Mehrotra, Mirco Musolesi
In this paper, we design and evaluate six different graph input representations (i. e., representations of the network paths), by considering the network's topological and temporal characteristics, for being used as inputs for machine learning models to learn the behavior of urban networks paths.
1 code implementation • 30 Jan 2020 • Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
In this work, we formulate the construction of a graph as a decision-making process in which a central agent creates topologies by trial and error and receives rewards proportional to the value of the target objective.
no code implementations • 4 May 2020 • Mariflor Vega-Carrasco, Jason O'sullivan, Rosie Prior, Ioanna Manolopoulou, Mirco Musolesi
Our approach is an alternative to standard label-switching techniques and provides a single posterior summary set of topics, as well as associated measures of uncertainty.
no code implementations • 15 Feb 2021 • Nicolas Anastassacos, Julian García, Stephen Hailes, Mirco Musolesi
We use a simple model of reinforcement learning to show that reputation mechanisms generate two coordination problems: agents need to learn how to coordinate on the meaning of existing reputations and collectively agree on a social norm to assign reputations to others based on their behavior.
no code implementations • 6 Apr 2021 • Giorgio Franceschelli, Mirco Musolesi
There is a growing interest in the area of machine learning and creativity.
no code implementations • 19 May 2021 • Giorgio Franceschelli, Mirco Musolesi
In this article, we consider a set of key questions in the area of generative deep learning for the arts, including the following: is it possible to use copyrighted works as training set for generative models?
1 code implementation • 12 Jun 2021 • Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Public goods games represent insightful settings for studying incentives for individual agents to make contributions that, while costly for each of them, benefit the wider society.
no code implementations • 12 Jun 2021 • Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
We tackle the problem of goal-directed graph construction: given a starting graph, a budget of modifications, and a global objective function, the aim is to find a set of edges whose addition to the graph achieves the maximum improvement in the objective (e. g., communication efficiency).
no code implementations • 16 Sep 2021 • Alberto Jesu, Victor-Alexandru Darvariu, Alessandro Staffolani, Rebecca Montanari, Mirco Musolesi
The growing number of applications of Reinforcement Learning (RL) in real-world domains has led to the development of privacy-preserving techniques due to the inherently sensitive nature of data.
1 code implementation • NeurIPS 2021 • Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
In particular, we define a Markov Decision Process which incrementally generates an mIS, and adopt a planning method to search for equilibria, outperforming existing methods.
no code implementations • 16 Jan 2022 • Giorgio Franceschelli, Mirco Musolesi
Measuring machine creativity is one of the most fascinating challenges in Artificial Intelligence.
no code implementations • 25 May 2022 • Ho Long Fung, Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
An often neglected issue in multi-agent reinforcement learning (MARL) is the potential presence of unreliable agents in the environment whose deviations from expected behavior can prevent a system from accomplishing its intended tasks.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • 26 May 2022 • Christoffel Doorman, Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
A key problem in network theory is how to reconfigure a graph in order to optimize a quantifiable objective.
no code implementations • 12 Sep 2022 • Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Network flow problems, which involve distributing traffic such that the underlying infrastructure is used effectively, are ubiquitous in transportation and logistics.
no code implementations • 2 Dec 2022 • Francesco De Lellis, Marco Coraggio, Giovanni Russo, Mirco Musolesi, Mario di Bernardo
One of the major challenges in Deep Reinforcement Learning for control is the need for extensive training to learn the policy.
no code implementations • 19 Jan 2023 • Nayana Dasgupta, Mirco Musolesi
In the natural world, direct punishment is an ubiquitous social mechanism that has been shown to benefit the emergence of cooperation within populations.
2 code implementations • 20 Jan 2023 • Elizaveta Tennant, Stephen Hailes, Mirco Musolesi
In particular, we believe that an interesting and insightful starting point is the analysis of emergent behavior of Reinforcement Learning (RL) agents that act according to a predefined set of moral rewards in social dilemmas.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 27 Mar 2023 • Giorgio Franceschelli, Mirco Musolesi
Finally, we examine the societal impact of these technologies with a particular focus on the creative industries, analyzing the opportunities offered by them, the challenges arising by them and the potential associated risks, from both legal and ethical points of view.
no code implementations • 1 Jun 2023 • Lorenz Wolf, Mirco Musolesi
Existing modular Reinforcement Learning (RL) architectures are generally based on reusable components, also allowing for ``plug-and-play'' integration.
no code implementations • 31 Jul 2023 • Giorgio Franceschelli, Mirco Musolesi
Generative Artificial Intelligence (AI) is one of the most exciting developments in Computer Science of the last decade.
no code implementations • 21 Aug 2023 • Giovanni Minelli, Mirco Musolesi
Robust coordination skills enable agents to operate cohesively in shared environments, together towards a common goal and, ideally, individually without hindering each other's progress.
no code implementations • 11 Sep 2023 • Edmund R. Hunt, Chris Baber, Mehdi Sobhani, Sanja Milivojevic, Sagir Yusuf, Mirco Musolesi, Patrick Waterson, Sally Maynard
Defining and measuring trust in dynamic, multiagent teams is important in a range of contexts, particularly in defense and security domains.
no code implementations • 20 Oct 2023 • Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Identifying causal structure is central to many fields ranging from strategic decision-making to biology and economics.
1 code implementation • 16 Nov 2023 • Francesco De Lellis, Marco Coraggio, Giovanni Russo, Mirco Musolesi, Mario di Bernardo
In addressing control problems such as regulation and tracking through reinforcement learning, it is often required to guarantee that the acquired policy meets essential performance and stability criteria such as a desired settling time and steady-state error prior to deployment.
no code implementations • 28 Nov 2023 • Olivia Macmillan-Scott, Mirco Musolesi
This article provides a survey of rationality and irrationality in artificial intelligence, and sets out the open questions in this area.
no code implementations • 4 Dec 2023 • Elizaveta Tennant, Stephen Hailes, Mirco Musolesi
In particular, we present three case studies of recent works which use learning from experience (i. e., Reinforcement Learning) to explicitly provide moral principles to learning agents - either as intrinsic rewards, moral logical constraints or textual principles for language models.
no code implementations • 21 Jan 2024 • Charles Westphal, Stephen Hailes, Mirco Musolesi
Identifying the most suitable variables to represent the state is a fundamental challenge in Reinforcement Learning (RL).
1 code implementation • 14 Feb 2024 • Olivia Macmillan-Scott, Mirco Musolesi
Do large language models (LLMs) display rational reasoning?
no code implementations • 7 Mar 2024 • Elizaveta Tennant, Stephen Hailes, Mirco Musolesi
In multi-agent (social) environments, complex population-level phenomena may emerge from interactions between individual learning agents.
no code implementations • 12 Mar 2024 • Giorgio Franceschelli, Mirco Musolesi
The Overfitted Brain hypothesis suggests dreams happen to allow generalization in the human brain.
no code implementations • 9 Apr 2024 • Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Graphs are a natural representation for systems based on relations between connected entities.