Classifying types of gesture and inferring intent

In order to infer intent from gesture, a rudimentary classification of types of gestures into five main classes is introduced. The classification is intended as a basis for incorporating the understanding of gesture into human-robot interaction (HRI). Some requirements for the operational classification of gesture by a robot interacting with humans are also suggested

Evolution in Asynchronous Cellular Automata

Final Accepted Versio

On straight words and minimal permutators in finite transformation semigroups.

“The original publication is available at www.springerlink.com”. Copyright SpringerMotivated by issues arising in computer science, we investigate the loop-free paths from the identity transformation and corresponding straight words in the Cayley graph of a finite transformation semigroup with a fixed generator set. Of special interest are words that permute a given subset of the state set. Certain such words, called minimal permutators, are shown to comprise a code, and the straight ones comprise a finite code. Thus, words that permute a given subset are uniquely factorizable as products of the subset's minimal permutators, and these can be further reduced to straight minimal permutators. This leads to insight into structure of local pools of reversibility in transformation semigroups in terms of the set of words permuting a given subset. These findings can be exploited in practical calculations for hierarchical decompositions of finite automata. As an example we consider groups arising in biological systems

Sensor Adaptation and Development in Robots by Entropy Maximization of Sensory Data

A method is presented for adapting the sensors of a robot to the statistical structure of its current environment. This enables the robot to compress incoming sensory information and to find informational relationships between sensors. The method is applied to creating sensoritopic maps of the informational relationships of the sensors of a developing robot, where the informational distance between sensors is computed using information theory and adaptive binning. The adaptive binning method constantly estimates the probability distribution of the latest inputs to maximize the entropy in each individual sensor, while conserving the correlations between different sensors. Results from simulations and robotic experiments with visual sensors show how adaptive binning of the sensory data helps the system to discover structure not found by ordinary binning. This enables the developing perceptual system of the robot to be more adapted to the particular embodiment of the robot and the environment

Learning how to do things with imitation

In this paper we discuss how agents can learn to do things by imitating other agents. Especially we look at how the use of different metrics and sub-goal granularity can affect the imitation results. We use a computer model of a chess world as a test-bed to also illustrate issues that arise when there is dissimilar embodiment between the demonstrator and the imitator agents

General self-motivation and strategy identification : Case studies based on Sokoban and Pac-Man

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.In this paper, we use empowerment, a recently introduced biologically inspired measure, to allow an AI player to assign utility values to potential future states within a previously unencountered game without requiring explicit specification of goal states. We further introduce strategic affinity, a method of grouping action sequences together to form "strategies," by examining the overlap in the sets of potential future states following each such action sequence. We also demonstrate an information-theoretic method of predicting future utility. Combining these methods, we extend empowerment to soft-horizon empowerment which enables the player to select a repertoire of action sequences that aim to maintain anticipated utility. We show how this method provides a proto-heuristic for nonterminal states prior to specifying concrete game goals, and propose it as a principled candidate model for "intuitive" strategy selection, in line with other recent work on "self-motivated agent behavior." We demonstrate that the technique, despite being generically defined independently of scenario, performs quite well in relatively disparate scenarios, such as a Sokoban-inspired box-pushing scenario and in a Pac-Man-inspired predator game, suggesting novel and principle-based candidate routes toward more general game-playing algorithms.Peer reviewedFinal Accepted Versio

Organization of the information flow in the perception-action loop of evolved agents

Sensor evolution in nature aims at improving the acquisition of information from the environment and is intimately related with selection pressure towards adaptivity and robustness. Recent work in the area aims at studying the perception-action loop in a formalized information-theoretic manner This paves the way towards a principled and general understanding of the mechanisms guiding the evolution of sensors in nature and provides insights into the design of mechanisms of artificial sensor evolution. In our paper we study the perception-action loop of agents. We evolve finite-state automata as agent controllers to solve an information acquisition task in a simple virtual world and study how the information flow is organized by evolution. Our analysis of the evolved automata and the information flow provides insight into how evolution organizes sensoric information acquisition, memory, processing and action selection. In addition, the results are compared to ideal information extraction schemes following from the Information Bottleneck principle

Tracking Information Flow through the Environment: Simple Cases of Stigmerg

Recent work in sensor evolution aims at studying the perception-action loop in a formalized information-theoretic manner. By treating sensors as extracting information and actuators as having the capability to "imprint" information on the environment we can view agents as creating, maintaining and making use of various information flows. In our paper we study the perception-action loop of agents using Shannon information flows. We use information theory to track and reveal the important relationships between agents and their environment. For example, we provide an information-theoretic characterization of stigmergy and evolve finite-state automata as agent controllers to engage in stigmergic communication. Our analysis of the evolved automata and the information flow provides insight into how evolution organizes sensoric information acquisition, implicit internal and external memory, processing and action selection

Evolutionary robustness of differentiation in genetic regulatory networks

We investigate the ability of artificial Genetic Regulatory Networks (GRNs) to evolve differentiation. The proposed GRN model supports non-linear interaction between regulating factors, thereby facilitating the realization of complex regulatory logics. As a proof of concept we evolve GRNs of this kind to follow different pathways, producing two kinds of periodic dynamics in response to minimal differences in external input. Furthermore we find that successive increases in environmental pressure for differentiation, allowing a lineage to adapt gradually, compared to an immediate requirement for a switch between behaviors, yields better results on average. Apart from better success there is also less variability in performance, the latter indicating an increase in evolutionary robustness