Prakash Panangaden

Doina Precup

2024-04-18

Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)

Polynomial Lawvere Logic

Giorgio Bacci

Radu Mardare

Gordon D. Plotkin

2024-02-05

ArXiv (prépublication)

Policy Gradient Methods in the Presence of Symmetries and State Abstractions

Sahand Rezaei-Shoshtari

Rosie Zhao

David Meger

Doina Precup

Behavioural pseudometrics for continuous-time diffusions

Linan Chen

Florence Clerc

2023-12-27

ArXiv (prépublication)

Propositional Logics for the Lawvere Quantale

Giorgio Bacci

Radu Mardare

Gordon Plotkin

2023-11-23

Electronic Notes in Theoretical Informatics and Computer Science (publié)

A Kernel Perspective on Behavioural Metrics for Markov Decision Processes

Pablo Samuel Castro

Tyler Kastner

Mark Rowland

We present a novel perspective on behavioural metrics for Markov decision processes via the use of positive definite kernels. We define a ne… (voir plus)w metric under this lens that is provably equivalent to the recently introduced MICo distance (Castro et al., 2021). The kernel perspective enables us to provide new theoretical results, including value-function bounds and low-distortion finite-dimensional Euclidean embeddings, which are crucial when using behavioural metrics for reinforcement learning representations. We complement our theory with strong empirical results that demonstrate the effectiveness of these methods in practice.

2023-06-19

TMLR (accepté)

openreview.net

Optimal Approximate Minimization of One-Letter Weighted Finite Automata

Clara Lacroce

Borja Balle

Guillaume Rabusseau

2023-05-31

ArXiv (prépublication)

Behavioural equivalences for continuous-time Markov processes

Linan Chen

Florence Clerc

2023-03-30

Mathematical Structures in Computer Science (publié)

Augmenting Human Selves Through Artificial Agents – Lessons From the Brain

Georg Northoff

Maia Fraser

John Griffiths

Dimitris A. Pinotsis

Rosalyn Moran

Karl Friston

Much of current artificial intelligence (AI) and the drive toward artificial general intelligence (AGI) focuses on developing machines for f… (voir plus)unctional tasks that humans accomplish. These may be narrowly specified tasks as in AI, or more general tasks as in AGI – but typically these tasks do not target higher-level human cognitive abilities, such as consciousness or morality; these are left to the realm of so-called “strong AI” or “artificial consciousness.” In this paper, we focus on how a machine can augment humans rather than do what they do, and we extend this beyond AGI-style tasks to augmenting peculiarly personal human capacities, such as wellbeing and morality. We base this proposal on associating such capacities with the “self,” which we define as the “environment-agent nexus”; namely, a fine-tuned interaction of brain with environment in all its relevant variables. We consider richly adaptive architectures that have the potential to implement this interaction by taking lessons from the brain. In particular, we suggest conjoining the free energy principle (FEP) with the dynamic temporo-spatial (TSD) view of neuro-mental processes. Our proposed integration of FEP and TSD – in the implementation of artificial agents – offers a novel, expressive, and explainable way for artificial agents to adapt to different environmental contexts. The targeted applications are broad: from adaptive intelligence augmenting agents (IA’s) that assist psychiatric self-regulation to environmental disaster prediction and personal assistants. This reflects the central role of the mind and moral decision-making in most of what we do as humans.

2022-06-23

Frontiers in Computational Neuroscience (publié)

Towards an AAK Theory Approach to Approximate Minimization in the Multi-Letter Case

Clara Lacroce

Guillaume Rabusseau

We study the approximate minimization problem of weighted finite automata (WFAs): given a WFA, we want to compute its optimal approximation … (voir plus)when restricted to a given size. We reformulate the problem as a rank-minimization task in the spectral norm, and propose a framework to apply Adamyan-Arov-Krein (AAK) theory to the approximation problem. This approach has already been successfully applied to the case of WFAs and language modelling black boxes over one-letter alphabets \citep{AAK-WFA,AAK-RNN}. Extending the result to multi-letter alphabets requires solving the following two steps. First, we need to reformulate the approximation problem in terms of noncommutative Hankel operators and noncommutative functions, in order to apply results from multivariable operator theory. Secondly, to obtain the optimal approximation we need a version of noncommutative AAK theory that is constructive. In this paper, we successfully tackle the first step, while the second challenge remains open.

2022-06-01

ArXiv (prépublication)

Bisimulation metrics and norms for real-weighted automata

Borja Balle

Pascale Gourdeau

2022-01-01

Information and Computation (publié)