
Formal approaches to a definition of agents
This thesis contributes to the formalisation of the notion of an agent w...
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Prediction of Synchrostate Transitions in EEG Signals Using Markov Chain Models
This paper proposes a stochastic model using the concept of Markov chain...
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Sensitivity analysis for finite Markov chains in discrete time
When the initial and transition probabilities of a finite Markov chain i...
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Selective Monitoring
We study selective monitors for labelled Markov chains. Monitors observe...
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A SemiMarkov Chain Approach to Modeling Respiratory Patterns Prior to Extubation in Preterm Infants
After birth, extremely preterm infants often require specialized respira...
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Pathentropy maximized Markov chains for dimensionality reduction
Stochastic kernel based dimensionality reduction methods have become pop...
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Pairwise Choice Markov Chains
As datasets capturing human choices grow in richness and scaleparticu...
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Action and perception for spatiotemporal patterns
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 goaldirected. 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 perceptions within multivariate Markov chains based on entitysets. Entitysets 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 entityset can be chosen according to operational closure conditions or complete specific integration. Importantly, the perceptionaction loop also induces an entityset and is a multivariate Markov chain. We then show that our definition of actions leads to nonheteronomy and that of perceptions specialize to the usual concept of perception in the perceptionaction loop.
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