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dc.contributor.authorBiehl, Martin Andreas
dc.date.accessioned2017-04-13T10:40:58Z
dc.date.available2017-04-13T10:40:58Z
dc.date.issued2017-04-13
dc.identifier.urihttp://hdl.handle.net/2299/17955
dc.description.abstractThis thesis is a contribution to the formalisation of the notion of an agent within the class of finite multivariate Markov chains. In accordance with the literature agents are are seen as entities that act, perceive, and are goaldirected. We present a new measure that can be used to identify entities (called i-entities). The intuition behind this is that entities are spatiotemporal patterns for which every part makes every other part more probable. The measure, complete local integration (CLI), is formally investigated within the more general setting of Bayesian networks. It is based on the specific local integration (SLI) which is measured with respect to a partition. CLI is the minimum value of SLI over all partitions. Upper bounds are constructively proven and a possible lower bound is proposed. We also prove a theorem that shows that completely locally integrated spatiotemporal patterns occur as blocks in specific partitions of the global trajectory. Conversely we can identify partitions of global trajectories for which every block is completely locally integrated. These global partitions are the finest partitions that achieve a SLI less or equal to their own SLI. We also establish the transformation behaviour of SLI under permutations of the nodes in the Bayesian network. We then go on to present three conditions on general definitions of entities. These are most prominently not fulfilled by sets of random variables i.e. the perception-action loop, which is often used to model agents, is too restrictive a setting. We instead propose that any general entity definition should in effect specify a subset of the set of all spatiotemporal patterns of a given multivariate Markov chain. Any such definition will then define what we call an entity set. The set of all completely locally integrated spatiotemporal patterns is one example of such a set. Importantly the perception-action loop also naturally induces such an entity set. We then propose formal definitions of actions and perceptions for arbitrary entity sets. We show that these are generalisations of notions defined for the perception-action loop by plugging the entity-set of the perception-action loop into our definitions. We also clearly state the properties that general entity-sets have but the perception-action loop entity set does not. This elucidates in what way we are generalising the perception-action loop. Finally we look at some very simple examples of bivariate Markov chains. We present the disintegration hierarchy, explain it via symmetries, and calculate the i-entities. Then we apply our definitions of perception and action to these i-entities.en_US
dc.language.isoenen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectartificial lifeen_US
dc.subjectcellular automataen_US
dc.subjectagentsen_US
dc.subjectagencyen_US
dc.subjectspatiotemporal patternsen_US
dc.subjectentitiesen_US
dc.subjectidentityen_US
dc.subjectintegrationen_US
dc.subjectactionen_US
dc.subjectperceptionen_US
dc.subjectsensorimotor loopen_US
dc.subjectsensor-motor loopen_US
dc.subjectperception-action loopen_US
dc.subjectspecific local integrationen_US
dc.subjectcomplete local integrationen_US
dc.titleFormal Approaches to a Definition of Agentsen_US
dc.typeinfo:eu-repo/semantics/doctoralThesisen_US
dc.type.qualificationlevelDoctoralen_US
dc.type.qualificationnamePhDen_US
herts.preservation.rarelyaccessedtrue


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