Information Theory of Decisions and Actions

By Naftali Tishby, Daniel Polani. 2011

322 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Molecular Biology

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Citations in the corpus

Information seeking, curiosity and attention: computational and neural mechanisms (2013, positive): “Balancing the costs and benefits of information sampling can also be cast in an information theoretic perspective (Tishby and Polani 2011).”
Trading Value and Information in MDPs (2012, background): “This treatment gives rise to a tradeoff between value and information, which differs from standard MDP as the information term is an explicit function of the unknown policy itself [10].”
Life as we know it (2013, unknown): “The resulting active inference is closely related to formulations in embodied cognition and artificial intelligence; for example, the use of predictive information [22–24] and earlier homeokinetic formulations [25].”
Non-parametric Policy Search with Limited Information Loss (2017, background): “Firstly, from the point of view of maximizing the cumulative reward under constraints on the communication bandwidth or data processing capacity (Tishby and Polani, 2011), or, equivalently, minimizing the policy complexity under a constraint on the policy’s value (Still and Precup, 2012).”
Exploiting Hierarchy for Learning and Transfer in KL-regularized RL (2019, background): “Another closely related regularization for RL is using information bottleneck (Tishby & Polani, 2011; Still & Precup, 2012; Rubin et al., 2012; Ortega & Braun, 2013; Tiomkin & Tishby, 2017).”
The anatomy of choice: active inference and agency (2013, background): “Important examples here include artiﬁcial curiosity (Schmidhuber, 1991), empowerment (Klyubin et al., 2005), information to go (Tishby and Polani, 2011) and computational complexity (Ortega and Braun, 2011, 2013).”
Causal Analysis of Agent Behavior for AI Safety (2021, background): “(2) These dependencies are illustrated in the causal Bayesian network of Figure 1b describing the perception-action loop (Tishby & Polani, 2011).”
The graphical brain: Belief propagation and active inference (2017, background): “This distinction is important because active inference deals with how the generative model of a process and the process per se are coupled in a circular fashion to describe the perception-action cycle (Fuster, 2004; Tishby & Polani, 2010).”
Particle Value Functions (2017, background): “Note also that the literature on KL regularized control (Todorov, 2006; Kappen, 2005; Tishby & Polani, 2011) gives a different perspective on risk sensitive control, which mirrors the relationship between variational inference and maximum likelihood.”
PAC-Bayes-Bernstein Inequality for Martingales and its Application to Multiarmed Bandits (2011, unknown): “Some potential advantages of applying PAC-Bayesian analysis in reinforcement learning were recently pointed out by several researchers, including Tishby and Polani (2010) and Fard and Pineau (2010).”
Information-Theoretic Bounded Rationality (2015, background): “…2009, Theodorou et al., 2010, Theodorou, 2015, Still, 2009, Still and Precup, 2012, van den Broek et al., 2010, Friston, 2010, Peters et al., 2010, Tishby and Polani, 2011, Kappen et al., 2012, Neumann and Peters, 2012, Rawlik and Toussaint, 2012, Rubin et al., 2012, Fox and Tishby, 2012, Neumann…”
A free energy principle for a particular physics (2019, background): “and formulations of epistemic foraging in terms of information theory and artificial curiosity (Bialek et al., 2001; Itti and Baldi, 2009; Schmidhuber, 2010; Still and Precup, 2012; Sun et al., 2011; Tishby and Polani, 2010). In the present formulation, internal states come to parameterise beliefs a”