University of Hertfordshire

By the same authors

Action and perception for spatiotemporal patterns

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Documents

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Original languageEnglish
Title of host publicationProceedings of the Fourteenth European Conference on Artificial LifeEuropean Conference on Artificial Life (ECAL) 2017
Subtitle of host publicationCreate, play, experiment, discover: revealing the experimental power of virtual worlds
PublisherMIT Press
Pages68
Number of pages75
Volume14
ISBN (Print)978-0-262-34633-7
DOIs
Publication statusPublished - Sep 2017
EventEuropean Conference on Artificial Life 2017: Create, play, experiment, discover: revealing the experimental power of virtual worlds - Lyon Tech Campus, Lyon, France
Duration: 4 Sep 20178 Sep 2017
Conference number: 14th
https://project.inria.fr/ecal2017/

Conference

ConferenceEuropean Conference on Artificial Life 2017
Abbreviated titleECAL 2017
CountryFrance
CityLyon
Period4/09/178/09/17
Internet address

Abstract

This is a contribution to the formalization of the concept of agents in multivariate Markov chains. Agents are commonly defined as entities that act, perceive, and are goal-directed. In a multivariate Markov chain (e.g. a cellular automaton) the transition matrix completely determines the dynamics. This seems to contradict the possibility of acting entities within such a system. Here we present definitions of actions and per- ceptions within multivariate Markov chains based on entity- sets. Entity-sets represent a largely independent choice of a set of spatiotemporal patterns that are considered as all the entities within the Markov chain. For example, the entity- set can be chosen according to operational closure conditions or complete specific integration. Importantly, the perception- action loop also induces an entity-set and is a multivariate Markov chain. We then show that our definition of actions leads to non-heteronomy and that of perceptions specialize to the usual concept of perception in the perception-action loop.

ID: 15052973