Abstract-Recent neurobiological findings suggest that the brain solves perceptual decision-making tasks by means of a dynamic competition in which evidence is accumulated in favor of the alternatives. However, it is unclear if and how the same process applies in more complex, real-world tasks such as the categorization of ambiguous visual scenes and what elements are considered as evidence in this case. Furthermore, dynamic decision models typically consider evidence accumulation as a passive process disregarding the role of active perception strategies. In this article, we adopt the principles of dynamic competition and active vision for the realization of a biologicallymotivated computational model, which we test in a visual categorization task. Furthermore, our system uses predictive power of the features as the main dimension for both evidence accumulation and the guidance of active vision. Comparison of human and synthetic data in a common experimental set-up suggests that the proposed model captures essential aspects of how the brain solves perceptual ambiguities in time. Our results point to the importance of the proposed principles of dynamic competition, parallel specification and selection of multiple alternatives, prediction, and active guidance of perceptual strategies for perceptual decision-making and the solution of perceptual ambiguities, and suggest that they could apply to both the simple perceptual decision problems studied in neuroscience and the more complex ones addressed by vision research.
The Widely scalable Mobile Underwater Sonar Technology (WiMUST) project is an H2020 Research and Innovation Action funded by the European Commission. The project aims at developing a system of cooperative autonomous underwater vehicles (AUVs) for geotechnical surveying and geophysical exploration. The paper describes the main objectives of the project, gives an overview of the methodologies adopted to achieve them, and summarizes the work done in the first year of R&D work.
Empowerment is an information-theoretic measure representing the capacity of an agent to affect its environment. It quantifies its ability to inject information in the environment via its actions and to recapture this information through its sensors. In a nutshell, it measures the number of future options available and perceivable by the agent. Originally, the definition of empowerment does not depend on any particular extrinsic goal and it is determined only by the interaction of the agent with the world and the structure of its action-perception cycle. In this paper we introduce a new formalism that combines empowerment maximization with externally specifiable goaldirected behaviour. This has two main implications: on the one hand, the study of the relationship between empowerment optimization and goal-directedness, to investigate to which extent these two desirable behaviours can co-exist; on the other hand, from a more operational point of view, the derivation of a method to generate a behaviour (i.e., a policy of a Markov decision process) that is both empowered and goal-directed, in order to design agents capable of being as "empowered" as possible when facing any extrinsic task. Finally, we study how this hybrid policy is able to handle problems of uncertain or changing goals and delayed goal commitment.
Seeking goals carried out by agents with a level of competency requires an “understanding” of the structure of their world. While abstract formal descriptions of a world structure in terms of geometric axioms can be formulated in principle, it is not likely that this is the representation that is actually employed by biological organisms or that should be used by biologically plausible models. Instead, we operate by the assumption that biological organisms are constrained in their information processing capacities, which in the past has led to a number of insightful hypotheses and models for biologically plausible behaviour generation. Here we use this approach to study various types of spatial categorizations that emerge through such informational constraints imposed on embodied agents. We will see that geometrically-rich spatial representations emerge when agents employ a trade-off between the minimisation of the Shannon information used to describe locations within the environment and the reduction of the location error generated by the resulting approximate spatial description. In addition, agents do not always need to construct these representations from the ground up, but they can obtain them by refining less precise spatial descriptions constructed previously. Importantly, we find that these can be optimal at both steps of refinement, as guaranteed by the successive refinement principle from information theory. Finally, clusters induced by these spatial representations via the information bottleneck method are able to reflect the environment’s topology without relying on an explicit geometric description of the environment’s structure. Our findings suggest that the fundamental geometric notions possessed by natural agents do not need to be part of their a priori knowledge but could emerge as a byproduct of the pressure to process information parsimoniously.
Passive sensory processing is often insufficient to guide biological organisms in complex environments. Rather, behaviourally relevant information can be accessed by performing so-called epistemic actions that explicitly aim at unveiling hidden information. However, it is still unclear how an autonomous agent can learn epistemic actions and how it can use them adaptively. In this work, we propose a definition of epistemic actions for POMDPs that derive from their characterizations in cognitive science and classical planning literature. We give theoretical insights about how partial observability and epistemic actions can affect the learning process and performance in the extreme conditions of model-free and memory-free reinforcement learning where hidden information cannot be represented. We finally investigate these concepts using an integrated eye-arm neural architecture for robot control, which can use its effectors to execute epistemic actions and can exploit the actively gathered information to efficiently accomplish a seek-and-reach task.
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