Action and perception for spatiotemporal patterns

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 perceptions within multivariate Markov chains based on entitysets. 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 entityset can be chosen according to operational closure conditions or complete specific integration. Importantly, the perceptionaction 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.

Semantic information, autonomous agency and non-equilibrium statistical physics

“…As mentioned in the Introduction, our work is conceptually inspired by work on autonomous agents, and our approach in fact suggests a possible formal and quantitative definition of autonomous agency: a physical system is an autonomous agent to the extent that it has a large measure of semantic information. From this point of view, finding timescales and system/environment decompositions that maximize measures of semantic information provides a way to automatically identify agents in the physical world (see also [141][142][143][144]). Exploring these possibilities, including which semantic information measures (value of information, the amount of semantic information, thermodynamic multiplier, etc.)…”

Investigating Transformational Complexity: Counting Functions a Region Induces on Another in Elementary Cellular Automata

“…e number of such equivalence classes is identical to the number of functions we count here. is construction of equivalence classes has been used to capture perception of a stochastic process agent model in [8]. Note however that it is not clear in how far the output cells can be seen as the future state of an agent whose current state is the volume state.…”

Causal Blankets: Theory and Algorithmic Framework

“…Therefore, a minimal D-BaSS distinguishes only "differences that make a difference" for the future dynamics, generalising the construction presented in Ref. [6,Definition 1] for Markovian dynamical systems, and being closely related to the notion of sensory equivalence presented in Ref. [3].…”

“…In contrast, our framework follows Refs. [3,6] and does not assume active and sensory variables as given, but discovers them directly from the data. As a matter of fact, the "sensory" (S) and "active" (A) variables of CBs correspond (due to Definition 2) to minimal sufficient statistics that mediate the interdependencies between the past and future of X and Y .…”