Eduardo Alonso

AIJ Journal 2025 Journal Article

Algebras of actions in an agent's representations of the world

• Alexander Dean
• Eduardo Alonso
• Esther Mondragón

Learning efficient representations allows robust processing of data, data that can then be generalised across different tasks and domains, and it is thus paramount in various areas of Artificial Intelligence, including computer vision, natural language processing and reinforcement learning, among others. Within the context of reinforcement learning, we propose in this paper a mathematical framework to learn representations by extracting the algebra of the transformations of worlds from the perspective of an agent. As a starting point, we use our framework to reproduce representations from the symmetry-based disentangled representation learning (SBDRL) formalism proposed by [1] and prove that, although useful, they are restricted to transformations that respond to the properties of algebraic groups. We then generalise two important results of SBDRL –the equivariance condition and the disentangling definition– from only working with group-based symmetry representations to working with representations capturing the transformation properties of worlds for any algebra, using examples common in reinforcement learning and generated by an algorithm that computes their corresponding Cayley tables. Finally, we combine our generalised equivariance condition and our generalised disentangling definition to show that disentangled sub-algebras can each have their own individual equivariance conditions, which can be treated independently, using category theory. In so doing, our framework offers a rich formal tool to represent different types of symmetry transformations in reinforcement learning, extending the scope of previous proposals and providing Artificial Intelligence developers with a sound foundation to implement efficient applications.

AAMAS Conference 2025 Conference Paper

Predictive Improvement through Latent Space Optimisation

• Alexander McCaffrey
• Eduardo Alonso
• Esther Mondragon

Efficient exploration remains a challenge in reinforcement learning (RL), especially in stochastic or complex environments. We introduce Predictive Improvement through Latent space OpTimisation (PILOT), an intrinsically motivated RL algorithm that rewards actions leading to improvements in the agent’s environmental dynamics model. PILOT optimizes an intrinsic reward signal based on epistemic uncertainty reduction, thereby encouraging structured exploration. Our evaluations against benchmark intrinsic motivation algorithms in challenging environments show that PILOT achieves superior performance and exhibits robustness to stochastic distractions.

AAAI Conference 2021 Short Paper

HetSAGE: Heterogenous Graph Neural Network for Relational Learning (Student Abstract)

• Vince Jankovics
• Michael Garcia Ortiz
• Eduardo Alonso

This paper aims to bridge this gap between neuro-symbolic learning (NSL) and graph neural networks (GNN) approaches and provide a comparative study. We argue that the natural evolution of NSL leads to GNNs, while the logic programming foundations of NSL can bring powerful tools to improve the way information is represented and pre-processed for the GNN. In order to make this comparison, we propose HetSAGE, a GNN architecture that can efficiently deal with the resulting heterogeneous graphs that represent typical NSL learning problems. We show that our approach outperforms the state-of-the-art on 3 NSL tasks: CORA, MUTA188 and MovieLens.

JAAMAS Journal 2007 Journal Article

Multi-agent learning

• Eduardo Alonso

KER Journal 2001 Journal Article

Learning in multi-agent systems

• Eduardo Alonso
• MARK D'INVERNO
• Daniel Kudenko
• Michael Luck
• JASON NOBLE

In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as software programs or robots, and their interaction and coordination in such diverse areas as robotics (Kitano et al., 1997), information retrieval and management (Klusch, 1999), and simulation (Gilbert & Conte, 1995). When designing agent systems, it is impossible to foresee all the potential situations an agent may encounter and specify an agent behaviour optimally in advance. Agents therefore have to learn from, and adapt to, their environment, especially in a multi-agent setting.