Portrait de Prakash Panangaden

Prakash Panangaden

Membre académique principal
prakash.panangaden@mila.quebec
Professeur titulaire, McGill University, École d'informatique
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Biographie

Prakash Panangaden a étudié la physique à l'Indian Institute of Technology de Kanpur, en Inde. Il a obtenu une maîtrise en physique de l'Université de Chicago, où il a étudié l'émission stimulée des trous noirs. Il a ensuite obtenu un doctorat en physique de l'Université du Wisconsin-Milwaukee, dans lequel il s’est penché sur la théorie quantique des champs dans un espace-temps courbe. Il a été professeur adjoint d'informatique à l'Université Cornell, où il a principalement travaillé sur la sémantique des langages de programmation concurrents. Depuis 1990, il travaille à l'Université McGill. Au cours des 25 dernières années, il s'est intéressé à de nombreux aspects des processus de Markov : équivalence des processus, caractérisation logique, approximation et métrique. Récemment, il a travaillé sur l'utilisation des métriques pour améliorer l'apprentissage des représentations. Il a également publié des articles sur la physique, l'information quantique et les mathématiques pures. Il est membre de la Société royale du Canada et de l'Association for Computing Machinery (ACM).

Étudiants actuels

Jonathan Colaço Carr
Maîtrise recherche - McGill University
Co-superviseur⋅e :
Doina Precup
jonathan.colaco-carr@mila.quebec

Publications

Polynomial Lawvere Logic
Giorgio Bacci
Radu Mardare
Prakash Panangaden
Gordon D. Plotkin
2024-02-05
ArXiv (prépublication)
doi.org
arxiv.org
Behavioural pseudometrics for continuous-time diffusions
Linan Chen
Florence Clerc
Prakash Panangaden
2023-12-27
ArXiv (prépublication)
doi.org
arxiv.org
Propositional Logics for the Lawvere Quantale
Giorgio Bacci
Radu Mardare
Prakash Panangaden
Gordon Plotkin
2023-11-23
Electronic Notes in Theoretical Informatics and Computer Science (publié)
doi.org
arxiv.org
Conditions on Preference Relations that Guarantee the Existence of Optimal Policies
Jonathan Colaco Carr
Prakash Panangaden
Doina Precup
2023-11-03
ArXiv (prépublication)
doi.org
arxiv.org
A Kernel Perspective on Behavioural Metrics for Markov Decision Processes
Pablo Samuel Castro
Tyler Kastner
Prakash Panangaden
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)
doi.org
openreview.net
Optimal Approximate Minimization of One-Letter Weighted Finite Automata
Clara Lacroce
Borja Balle
Prakash Panangaden
Guillaume Rabusseau
2023-05-31
ArXiv (prépublication)
doi.org
arxiv.org
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
Prakash Panangaden
Sahand Rezaei-Shoshtari
Rosie Zhao
David Meger
Doina Precup
2023-05-09
ArXiv (prépublication)
doi.org
arxiv.org
Behavioural equivalences for continuous-time Markov processes
Linan Chen
Florence Clerc
Prakash Panangaden
2023-03-30
Mathematical Structures in Computer Science (publié)
doi.org
Augmenting Human Selves Through Artificial Agents – Lessons From the Brain
Georg Northoff
Maia Fraser
John Griffiths
Dimitris A. Pinotsis
Prakash Panangaden
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)
doi.org
Bisimulation metrics and norms for real-weighted automata
Borja Balle
Pascale Gourdeau
Prakash Panangaden
2022-01-01
Information and Computation (publié)
doi.org
Bisimulation metrics and norms for real-weighted automata
Borja Balle
Pascale Gourdeau
Prakash Panangaden
2020-11-01
Information and Computation (published)
doi.org
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms
Philip. Amortila
Doina Precup
Prakash Panangaden
Marc Gendron-Bellemare
We present a distributional approach to theoretical analyses of reinforcement learning algorithms for constant step-sizes. We demonstrate it… (voir plus)s effectiveness by presenting simple and unified proofs of convergence for a variety of commonly-used methods. We show that value-based methods such as TD(
2020-03-27
ArXiv (preprint)
arxiv.org
arxiv.org