On information-optimal scripting of actions

Best paper award.Animals and humans encounter many tasks which permit ritualized behaviours, essentially fixed action sequences or “scripts”, similar to options known from Reinforcement Learning, but proceeding without intermediate decisions. While running a script, they proceed in an open-loop fashion. However even when these are already known, an agent needs to decide whether to perform a basic action or to trigger a script regarding the particular task. Here we study if including such scripts (i.e. behaviour rituals) is advantageous from the point of view of the relevant information required to take the decision to start such a script depending on the tasks. To achieve this, we modify the relevant information framework including sequences of basic actions to the possible actions

Information parsimony in collaborative interaction

We investigate the information processing cost associated with performing a collaborative dyadic task at a specific utility level. We build our approach on the Relevant Information formalism, which combines Shannon's Information Theory and Markov Decision Processes, for modelling a dyadic interaction scenario in which two agents with independent controllers move an object together with fully redundant control. Results show that increasing dyad's collaboration decreases the information intake and vice versa, antagonistic behavior puts a strain on the information bandwidth capacity. The key role of the particular embodiment of the environment in this trade-off is demonstrated in a series of simulations with informationally parsimonious optimal controllers.Peer reviewedFinal Published versio

Information-theoretic Sensorimotor Foundations of Fitts' Law

© 2019 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published is accessible via https://doi.org/10.1145/3290607.3313053We propose a novel, biologically plausible cost/fitness function for sensorimotor control, formalized with the information-theoretic principle of empowerment, a task-independent universal utility. Empowerment captures uncertainty in the perception-action loop of different nature (e.g. noise, delays, etc.) in a single quantity. We present the formalism in a Fitts' law type goal-directed arm movement task and suggest that empowerment is one potential underlying determinant of movement trajectory planning in the presence of signal-dependent sensorimotor noise. Simulation results demonstrate the temporal relation of empowerment and various plausible control strategies for this specific task

Digested Information as an Information Theoretic Motivation for Social Interaction

Within a universal agent-world interaction framework, based on Information Theory and Causal Bayesian Networks, we demonstrate how every agent that needs to acquire relevant information in regard to its strategy selection will automatically inject part of this information back into the environment. We introduce the concept of 'Digested Information' which both quantifies, and explains this phenomenon. Based on the properties of digested information, especially the high density of relevant information in other agents actions, we outline how this could motivate the development of low level social interaction mechanisms, such as the ability to detect other agents.Information Theory, Collective Behaviour, Inadvertent Social Information, Infotaxis, Digested Information, Bayesian Update

Informational drives for sensor evolution

© 2012 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) licenseIt has been hypothesized that the evolution of sensors is a pivotal driver for the evolution of organisms, and especially, as a crucial part of the perception-action loop, a driver for cognitive development. The questions of why and how this is the case are important: what are the principles that push the evolution of sensorimotor systems? An interesting aspect of this problem is the co-option of sensors for functions other than those originally driving their development (e.g. the auditive sense of bats being employed as a 'visual' modality). Even more striking is the phenomenon found in nature of sensors being driven to the limits of precision, while starting from much simpler beginnings. While a large potential for diversification and exaptation is visible in the observed phenotypes, gaining a deeper understanding of why and how this can be achieved is a significant problem. In this present paper, we will introduce a formal and generic information-theoretic model for understanding potential drives of sensor evolution, both in terms of improving sensory ability and in terms of extending and/or shifting sensory function

Causal Blankets: Theory and Algorithmic Framework

We introduce a novel framework to identify perception-action loops (PALOs) directly from data based on the principles of computational mechanics. Our approach is based on the notion of causal blanket, which captures sensory and active variables as dynamical sufficient statistics -- i.e. as the "differences that make a difference." Moreover, our theory provides a broadly applicable procedure to construct PALOs that requires neither a steady-state nor Markovian dynamics. Using our theory, we show that every bipartite stochastic process has a causal blanket, but the extent to which this leads to an effective PALO formulation varies depending on the integrated information of the bipartition.Peer reviewe

Discovering Motion Flow by Temporal-Informational Correlations in Sensors

A method is presented for adapting the sensors of a robot to its current environment and to learn motion flow detection by observing the informational relations between sensors and actuators. Examples are shown where the robot learns to detect motion flow from sensor data generated by its own movement

A Simple Modularity Measure for Search Spaces based on Information Theory

Within the context of Artificial Life the question about the role of modularity has turned out to be crucial, especially with regard to the problem of evolvability. In order to be able to observe the development of modular structure, appropriate modularity measures are important. We introduce a continuous measure based on information theory which can characterize the coupling among subsystems in a search problem. In order to illustrate the concepts developed, they are applied to a very simple and intuitive set of combinatorial problems similar to scenarios used in the seminal work by Simon (1969). It is shown that this measure is closely related to the classification of search problems in terms of Separability, Non-Decomposability and Modular Interdependency as introduced in (Watson and Pollack, 2005)