Search Results for author:
Found 38 papers, 7 papers with code
Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective
Graphs are a natural representation for systems based on relations between connected entities.
Do Agents Dream of Electric Sheep?: Improving Generalization in Reinforcement Learning through Generative Learning
The Overfitted Brain hypothesis suggests dreams happen to allow generalization in the human brain.
Dynamics of Moral Behavior in Heterogeneous Populations of Learning Agents
In multi-agent (social) environments, complex population-level phenomena may emerge from interactions between individual learning agents.
(Ir)rationality and Cognitive Biases in Large Language Models
Do large language models (LLMs) display rational reasoning?
Information-Theoretic State Variable Selection for Reinforcement Learning
Identifying the most suitable variables to represent the state is a fundamental challenge in Reinforcement Learning (RL).
Learning Machine Morality through Experience and Interaction
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.
(Ir)rationality in AI: State of the Art, Research Challenges and Open Questions
This article provides a survey of rationality and irrationality in artificial intelligence, and sets out the open questions in this area.
Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learning
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.
Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery
Identifying causal structure is central to many fields ranging from strategic decision-making to biology and economics.
Steps Towards Satisficing Distributed Dynamic Team Trust
Defining and measuring trust in dynamic, multiagent teams is important in a range of contexts, particularly in defense and security domains.
CoMIX: A Multi-agent Reinforcement Learning Training Architecture for Efficient Decentralized Coordination and Independent Decision Making
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.
Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges
Generative Artificial Intelligence (AI) is one of the most exciting developments in Computer Science of the last decade.
Heterogeneous Knowledge for Augmented Modular Reinforcement Learning
Existing modular Reinforcement Learning (RL) architectures are generally based on reusable components, also allowing for ``plug-and-play'' integration.
On the Creativity of Large Language Models
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.
Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning
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
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Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning Systems
In the natural world, direct punishment is an ubiquitous social mechanism that has been shown to benefit the emergence of cooperation within populations.
CT-DQN: Control-Tutored Deep Reinforcement Learning
One of the major challenges in Deep Reinforcement Learning for control is the need for extensive training to learn the policy.
Graph Neural Modeling of Network Flows
Network flow problems, which involve distributing traffic such that the underlying infrastructure is used effectively, are ubiquitous in transportation and logistics.
Dynamic Network Reconfiguration for Entropy Maximization using Deep Reinforcement Learning
A key problem in network theory is how to reconfigure a graph in order to optimize a quantifiable objective.
Trust-based Consensus in Multi-Agent Reinforcement Learning Systems
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
DeepCreativity: Measuring Creativity with Deep Learning Techniques
Measuring machine creativity is one of the most fascinating challenges in Artificial Intelligence.
Solving Graph-based Public Goods Games with Tree Search and Imitation Learning
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.
Reinforcement Learning on Encrypted Data
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.
Solving Graph-based Public Good Games with Tree Search and Imitation Learning
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.
Planning Spatial Networks with Monte Carlo Tree Search
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).
Copyright in Generative Deep Learning
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?
Creativity and Machine Learning: A Survey
There is a growing interest in the area of machine learning and creativity.
Cooperation and Reputation Dynamics with Reinforcement Learning
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.
Modelling Grocery Retail Topic Distributions: Evaluation, Interpretability and Stability
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.
Goal-directed graph construction using reinforcement learning
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.
Graph Input Representations for Machine Learning Applications in Urban Network Analysis
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.
Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning
Social dilemmas have been widely studied to explain how humans are able to cooperate in society.
Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas
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.
You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information
Metadata are associated to most of the information we produce in our daily interactions and communication in the digital world.
Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities
We characterize the mobility patterns of individuals using the GPS metrics presented in the literature and employ these metrics as input to the network.
Interpretable Machine Learning for Privacy-Preserving Pervasive Systems
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
Probabilistic Matching: Causal Inference under Measurement Errors
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.
Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web
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.
