Constructing the Basic Umwelt of Artificial Agents: An Information-Theoretic Approach

Capdepuy, Philippe; Polani, Daniel; Nehaniv, Chrystopher L.

doi:10.1007/978-3-540-74913-4_38

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…Recurrent neural networks, Kalman filters and Bayesian predictors were successfully used to learn action-effect rules [7]. Capdepuy et al [28] proposed an information-theoretic mechanism to create an internal representation of the agent's environment, and subsequently, they developed an information-theoretic anticipation framework to identify relevant relationships between events [29]. They also evaluated an active exploration strategy, in which an agent gradually collects samples of the interaction with the environment, such that the prediction accuracy becomes maximized [30].…”

Section: Introductionmentioning

confidence: 99%

Effects of Anticipation in Individually Motivated Behaviour on Survival and Control in a Multi-Agent Scenario with Resource Constraints

Guckelsberger

Polani

2014

Entropy

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Self-organization and survival are inextricably bound to an agent's ability to control and anticipate its environment. Here we assess both skills when multiple agents compete for a scarce resource. Drawing on insights from psychology, microsociology and control theory, we examine how different assumptions about the behaviour of an agent's peers in the anticipation process affect subjective control and survival strategies. To quantify control and drive behaviour, we use the recently developed information-theoretic quantity of empowerment with the principle of empowerment maximization. In two experiments involving extensive simulations, we show that agents develop risk-seeking, risk-averse and mixed strategies, which correspond to greedy, parsimonious and mixed behaviour. Although the principle of empowerment maximization is highly generic, the emerging strategies are consistent with what one would expect from rational individuals with dedicated utility models. Our results support empowerment maximization as a universal drive for guided self-organization in collective agent systems.

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Section: Introductionmentioning

confidence: 99%

Effects of Anticipation in Individually Motivated Behaviour on Survival and Control in a Multi-Agent Scenario with Resource Constraints

Guckelsberger

Polani

2014

Entropy

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“…Not only do they provide a unified and 'coordinate-free' framework for identifying limits on control [17,18] and for studying how information flows [3] are structured under various scenarios [8,10], but they also seem to be useful for guiding the self-organization of behaviour [19,2,12], the emergence of internal representations [9,5] and interactive learning [14].…”

Section: Introductionmentioning

confidence: 99%

Perception–action loops of multiple agents: informational aspects and the impact of coordination

2011

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Embodied agents can be conceived as entities perceiving and acting upon an external environment. Probabilistic models of this perception-action loop have paved the way to the investigation of information-theoretic aspects of embodied cognition. This formalism allows (i) to identify information flows and their limits under various scenarios and constraints, and (ii) to use informational quantities in order to induce the self-organization of the agent's behaviour without any externally specified drives. This paper extends the perception-action loop formalism to multiple agents. The multiple-access channel model is presented and used to identify the relationships between informational quantities of two agents interacting in the same environment. The central question investigated in this paper is the impact of coordination. Information-theoretic limits on what can be achieved with and without coordination are identified. For this purpose, different abstract channels are studied, along with a concrete example of agents interacting in space. It is shown that, under some conditions, self-organizing systems based on information-theoretic quantities have a tendency to spontaneously generate coordinated behaviour. Moreover, in the perspective of engineering such systems to achieve specific tasks, these information-theoretic limits put constraints on the amount of coordination that is required to perform the task, and consequently on the mechanisms that underly self-organization in the system.

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Hierarchical Behaviours: Getting the Most Bang for Your Bit

Dijk

Polani

Nehaniv

2011

Lecture Notes in Computer Science

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Hierarchical structuring of behaviour is prevalent in natural and artificial agents and can be shown to be useful for learning and performing tasks. To progress systematic understanding of these benefits we study the effect of hierarchical architectures on the required information processing capability of an optimally acting agent. We show that an information-theoretical approach provides important insights into why factored and layered behaviour structures are beneficial.

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Constructing the Basic Umwelt of Artificial Agents: An Information-Theoretic Approach

Abstract: ???The original publication is available at www.springerlink.com???. Copyright Springer [Full text of this article is not available in the UHRA

Cited by 8 publications

References 4 publications

Effects of Anticipation in Individually Motivated Behaviour on Survival and Control in a Multi-Agent Scenario with Resource Constraints

Effects of Anticipation in Individually Motivated Behaviour on Survival and Control in a Multi-Agent Scenario with Resource Constraints

Perception–action loops of multiple agents: informational aspects and the impact of coordination

Hierarchical Behaviours: Getting the Most Bang for Your Bit

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