arXiv subjects

Daniel Polani

Publications and source records attributed to Daniel Polani.

42 records · Page 3Linked to original sources

arXiv2013

Changing the Environment based on Intrinsic Motivation

One of the remarkable feats of intelligent life is that it restructures the world it lives in for its own benefit. This extended abstract outlines how the information-theoretic principle of empowerment, as an intrinsic motivation, can be used to restructure the environment an agent lives in. We present a first qualitative evaluation of how an agent in a 3d-gridworld builds a staircase-like structure, which reflects the agent's embodiment.

nlin.AO↗

arXiv2013

Empowerment -- an Introduction

This book chapter is an introduction to and an overview of the information-theoretic, task independent utility function "Empowerment", which is defined as the channel capacity between an agent's actions and an agent's sensors. It quantifies how much influence and control an agent has over the world it can perceive. This book chapter discusses the general idea behind empowerment as an intrinsic motivation and showcases several previous applications of empowerment to demonstrate how empowerment can be applied to different sensor-motor configuration, and how the same formalism can lead to different observed behaviors. Furthermore, we also present a fast approximation for empowerment in the continuous domain.

cs.AI↗

arXiv2012

A Bivariate Measure of Redundant Information

We define a measure of redundant information based on projections in the space of probability distributions. Redundant information between random variables is information that is shared between those variables. But in contrast to mutual information, redundant information denotes information that is shared about the outcome of a third variable. Formalizing this concept, and being able to measure it, is required for the non-negative decomposition of mutual information into redundant and synergistic information. Previous attempts to formalize redundant or synergistic information struggle to capture some desired properties. We introduce a new formalism for redundant information and prove that it satisfies all the properties necessary outlined in earlier work, as well as an additional criterion that we propose to be necessary to capture redundancy. We also demonstrate the behaviour of this new measure for several examples, compare it to previous measures and apply it to the decomposition of transfer entropy.

cs.IT↗

arXiv2012

Empowerment for Continuous Agent-Environment Systems

This paper develops generalizations of empowerment to continuous states. Empowerment is a recently introduced information-theoretic quantity motivated by hypotheses about the efficiency of the sensorimotor loop in biological organisms, but also from considerations stemming from curiosity-driven learning. Empowemerment measures, for agent-environment systems with stochastic transitions, how much influence an agent has on its environment, but only that influence that can be sensed by the agent sensors. It is an information-theoretic generalization of joint controllability (influence on environment) and observability (measurement by sensors) of the environment by the agent, both controllability and observability being usually defined in control theory as the dimensionality of the control/observation spaces. Earlier work has shown that empowerment has various interesting and relevant properties, e.g., it allows us to identify salient states using only the dynamics, and it can act as intrinsic reward without requiring an external reward. However, in this previous work empowerment was limited to the case of small-scale and discrete domains and furthermore state transition probabilities were assumed to be known. The goal of this paper is to extend empowerment to the significantly more important and relevant case of continuous vector-valued state spaces and initially unknown state transition probabilities. The continuous state space is addressed by Monte-Carlo approximation; the unknown transitions are addressed by model learning and prediction for which we apply Gaussian processes regression with iterated forecasting. In a number of well-known continuous control tasks we examine the dynamics induced by empowerment and include an application to exploration and online model learning.

cs.AI↗

arXiv2012

Learning RoboCup-Keepaway with Kernels

We apply kernel-based methods to solve the difficult reinforcement learning problem of 3vs2 keepaway in RoboCup simulated soccer. Key challenges in keepaway are the high-dimensionality of the state space (rendering conventional discretization-based function approximation like tilecoding infeasible), the stochasticity due to noise and multiple learning agents needing to cooperate (meaning that the exact dynamics of the environment are unknown) and real-time learning (meaning that an efficient online implementation is required). We employ the general framework of approximate policy iteration with least-squares-based policy evaluation. As underlying function approximator we consider the family of regularization networks with subset of regressors approximation. The core of our proposed solution is an efficient recursive implementation with automatic supervised selection of relevant basis functions. Simulation results indicate that the behavior learned through our approach clearly outperforms the best results obtained earlier with tilecoding by Stone et al. (2005).

cs.AI↗

arXiv2010

Optimal strategies for fighting persistent bugs

Some microbial organisms are known to randomly slip into and out of hibernation, irrespective of environmental conditions [1]. In a (genetically) uniform population a typically very small subpopulation becomes metabolically inactive whereas the majority subpopulation remains active and grows. Bacteria such as E. coli, Staphylococcus aureus (MRSA-superbug), Mycobacterium tuberculosis, and Pseudomonas aeruginosa [1-3] show persistence. It can render bacteria less vulnerable in adverse environments [1, 4, 5] and their effective eradication through medication more difficult [2, 3, 6]. Here we show that medication treatment regimes may have to be modified when persistence is taken into account and characterize optimal approaches assuming that the total medication dose is constrained. The determining factors are cumulative toxicity, eradication power of the medication and bacterial response timescales. Persistent organisms have to be fought using tailored eradication strategies which display two fundamental characteristics. Ideally, the treatment time should be significantly longer than in the case of persistence with the medication uniformly spread out over time; however, if treatment time has to be limited, then the application of medication has to be concentrated towards the beginning and end of the treatment. These findings deviate from current clinical practice, and may therefore help to optimize and simplify treatments. Our use of multi-objective optimization [7] to map out the optimal strategies can be generalized to other related problems.

q-bio.OT↗