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Active inference is a leading theory of perception, learning and decision making, which can be applied to neuroscience, robotics, psychology, and machine learning. Active inference is based on the expected free energy, which is mostly…

Artificial Intelligence · Computer Science 2024-02-23 Théophile Champion , Howard Bowman , Dimitrije Marković , Marek Grześ

We define a property of intelligent systems, which we call Reflexivity. In human beings, it is one aspect of consciousness, and an element of deliberation. We propose a conjecture, that this property is conditioned by a topological property…

Artificial Intelligence · Computer Science 2016-04-29 Pascal Faudemay

The long-term resilient property of ecosystems has been quantified as ecological robustness (RECO) in terms of the energy transfer over food webs. The RECO of resilient ecosystems favors a balance of food webs' network efficiency and…

Systems and Control · Electrical Eng. & Systems 2023-10-03 Hao Huang , Katherine R. Davis , H. Vincent Poor

In this paper we criticize the robustness measure traditionally employed to assess the performance of machine learning models deployed in adversarial settings. To mitigate the limitations of robustness, we introduce a new measure called…

Machine Learning · Computer Science 2021-12-07 Stefano Calzavara , Lorenzo Cazzaro , Claudio Lucchese , Federico Marcuzzi , Salvatore Orlando

Can the right requirements boost developer satisfaction and happiness? We believe they can. In keeping with this issue's theme, "Well-Being for Resilience: Developers Thrive," we discuss the connection between the three keywords,…

Software Engineering · Computer Science 2024-06-07 Markus Borg , Daniel Graziotin

Network systems are one of the most active research areas in the engineering community as they feature a paradigm shift from centralized to distributed control and computation. When dealing with network systems, a fundamental challenge is…

Systems and Control · Computer Science 2018-02-26 D. Senejohnny , S. Sundaram , C. De Persis , P. Tesi

We study the model robustness against adversarial examples, referred to as small perturbed input data that may however fool many state-of-the-art deep learning models. Unlike previous research, we establish a novel theory addressing the…

Machine Learning · Computer Science 2020-06-11 Shufei Zhang , Kaizhu Huang , Zenglin Xu

Active inference is a mathematical framework for understanding how agents (biological or artificial) interact with their environments, enabling continual adaptation and decision-making. It combines Bayesian inference and free energy…

Artificial Intelligence · Computer Science 2024-10-02 Rithvik Prakki

When biological communities use signaling structures for complex coordination, 'free-riders' emerge. The free-riding agents do not contribute to the community resources (signals), but exploit them. Most models of such 'selfish' behavior…

Populations and Evolution · Quantitative Biology 2023-03-29 Aamir Sahil Chandroth , Nithya Ramakrishnan , Sanjay Chandrasekharan

The revelation principle has been known in the economics society for decades. In this paper, I will investigate it from an energy perspective, i.e., considering the energy consumed by agents and the designer in participating a mechanism.…

Computer Science and Game Theory · Computer Science 2015-04-21 Haoyang Wu

Resilience is a concept of rising interest in computer science and software engineering. For systems in which correctness w.r.t. a safety condition is unachievable, fast recovery is demanded. We investigate resilience problems of graph…

Software Engineering · Computer Science 2021-12-22 Okan Özkan , Nick Würdemann

The free energy principle has been proposed as a unifying account of brain function. It is closely related, and in some cases subsumes, earlier unifying ideas such as Bayesian inference, predictive coding, and active learning. This article…

Neurons and Cognition · Quantitative Biology 2022-11-30 Samuel J. Gershman

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different…

Dynamical Systems · Mathematics 2022-05-05 S. Salcedo-Sanz , D. Casillas-Pérez , J. Del Ser , C. Casanova-Mateo , L. Cuadra , M. Piles , G. Camps-Valls

When a malfunction causes a control system to lose authority over a subset of its actuators, achieving a task may require spending additional energy in order to compensate for the effect of uncontrolled inputs. To understand this increase…

Optimization and Control · Mathematics 2025-05-13 Ram Padmanabhan , Melkior Ornik

Living systems exhibit a range of fundamental characteristics: they are active, self-referential, self-modifying systems. This paper explores how these characteristics create challenges for conventional scientific approaches and why they…

Emerging Technologies · Computer Science 2025-08-18 Samson Abramsky , Wolfgang Banzhaf , Leo S. D. Caves , Michael Levin , Penousal Machado , Charles Ofria , Susan Stepney , Roger White

The goal of these lecture notes is to review the problem of free energy minimization as a unified framework underlying the definition of maximum entropy modelling, generalized Bayesian inference, learning with latent variables, statistical…

Signal Processing · Electrical Eng. & Systems 2020-12-01 Sharu Theresa Jose , Osvaldo Simeone

We describe a unified framework within which we can build survival models. The motivation for this work comes from a study on the prediction of relapse among breast cancer patients treated at the Curie Institute in Paris, France. Our focus…

Methodology · Statistics 2014-05-28 Cécile Chauvel , John O'Quigley

Understanding why we age is a long-lived open problem in evolutionary biology. Aging is prejudicial to the individual and evolutionary forces should prevent it, but many species show signs of senescence as individuals age. Here, I will…

Populations and Evolution · Quantitative Biology 2012-01-24 André C. R. Martins

The free energy functional has recently been proposed as a variational principle for bounded rational decision-making, since it instantiates a natural trade-off between utility gains and information processing costs that can be…

Machine Learning · Statistics 2012-05-18 Pedro A. Ortega , Daniel A. Braun

We show that the Random Energy Model has interesting rejuvenation properties in its frozen phase. Different `susceptibilities' to temperature changes, for the free-energy and for other (`magnetic') observables, can be computed exactly.…

Condensed Matter · Physics 2009-11-07 Marta Sales , Jean-Philippe Bouchaud
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