How and why actions are selected: action selection and the dark room problem (original) (raw)
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A role for consciousness in action selection
2013
This paper argues that conscious attention exists not so much for selecting an immediate action as for focusing learning of the action-selection mechanisms and predictive models on tasks and environmental contingencies likely to affect the conscious agent. It is perfectly possible to build this sort of system into machine intelligence, but it is not strictly necessary unless the intelligence needs to learn and is resource-bounded with respect to the rate of learning vs. the rate of relevant environmental change.
Past and present environments: The evolution of decision making
2010
Evolutionary biology distinguishes between proximal and ultimate goals. The single ultimate goal, driving all of evolution, is reproduction-specifically, increasing the proportion of one's genetic representation in future generations. Survival is only important insofar as it leads to increased reproduction for oneself or one's kin. There are many proximal goals, some more closely related to survival, such as finding food and avoiding predators, and others more associated with reproduction, such as finding mates and protecting offspring (see . Different species will evolve different sets of proximal goals depending on their biological setting including the ecology in which they are enmeshed and the life history they have evolved to lead (e.g., . For example, for sea anemones that simply release sperm and eggs into the water, parental care is not an issue, whereas for humans with internal fertilization and few, initially helpless, offspring, it is a major adaptive concern. Members of species with parental care are faced with the goal of identifying one's offspring, so that an individual's care and resources are directed toward genetic kin rather than another's offspring. The mind is filled with domain-specific decision mechanisms that have evolved by natural selection for achieving these proximal goals, and evolutionary psychology is dedicated to identifying and understanding those mechanisms . The purpose of this paper is to lay out a framework for how these decision mechanisms can be studied within evolutionary psychology, emphasize the often-neglected role of the decision environment when studying human behavior and cognition, and provide an illustrative example of how an evolutionary perspective can help us detect more of the adaptive decision making capacities humans possess.
The ecology of action selection: insights from artificial life
Philosophical Transactions of the Royal Society B: Biological Sciences, 2007
The problem of action selection has two components: what is selected and how is it selected? To understand what is selected, it is necessary to distinguish between behavioural and mechanistic levels of description. Animals do not choose between behaviours per se ; rather, behaviour reflects interactions among brains, bodies and environments. To understand what guides selection, it is useful to take a normative perspective that evaluates behaviour in terms of a fitness metric. This perspective, rooted in behavioural ecology, can be especially useful for understanding apparently irrational choice behaviour. This paper describes a series of models that use artificial life (AL) techniques to address the above issues. We show that successful action selection can arise from the joint activity of parallel, loosely coupled sensorimotor processes. We define a class of AL models that help to bridge the ecological approaches of normative modelling and agent- or individual-based modelling (IBM)...
In this paper, we argue for a theoretical separation of the free-energy principle from Helmholtzian accounts of the predictive brain. The free-energy principle is a theoretical framework capturing the imperative for biological self-organization in information-theoretic terms. The free-energy principle has typically been connected with a Bayesian theory of predictive coding, and the latter is often taken to support a Helmholtzian theory of perception as unconscious inference. If our interpretation is right, however, a Helmholtzian view of perception is incompatible with Bayesian predictive coding under the free-energy principle. We argue that the free energy principle and the ecological and enactive approach to mind and life make for a much happier marriage of ideas. We make our argument based on three points. First we argue that the free energy principle applies to the whole animal–environment system, and not only to the brain. Second, we show that active inference, as understood by the free-energy principle, is incompatible with unconscious inference understood as B Jelle Bruineberg 123 Synthese analagous to scientific hypothesis-testing, the main tenet of a Helmholtzian view of perception. Third, we argue that the notion of inference at work in Bayesian predictive coding under the free-energy principle is too weak to support a Helmholtzian theory of perception. Taken together these points imply that the free energy principle is best understood in ecological and enactive terms set out in this paper.
Levels and Types of Action Selection: The Action Selection Soup
Adaptive Behavior, 2009
Action selection (AS) is defined as the process where an action is selected among a number of alternatives. This definition, however, does not sufficiently describe what an action is. What is the unit of selection in the first place? We maintain that the artificial intelligence (AI) accounts of AS typically mix and merge two AS situations that indeed are qualitatively different. Most of the accounts actually deal only with one type of AS but purport to cover both types of AS. We propose three dimensions along which the commonalities and the differences between various AS accounts can be analyzed, and use these for a preliminary conceptualization of what we call a two-system action selection account. In particular, we identify two qualitatively different AS situations whose architectures, we suggest, can be designed inspired by neuroscience models of the basal ganglia (BG) and the cerebellum, respectively.
Bayes Optimality of Human Perception, Action and Learning: Behavioural and Neural Evidence
Lecture Notes in Computer Science, 2014
The primary role of any biological nervous system (including the human) is to process incoming information in a way that allows motor choices to be made that increases the subjective utility of the organism. Or put slightly differently, "to make sure good things happen". There are a number of ways that such a process can be done, but one possible hypothesis is that the human nervous system has been optimized to maximize the use of available resources, thus approximating optimal computations. In the following I will discuss the possibility of the nervous system performing such computations in perception, action and learning, and the behavioural and neural evidence supporting such ideas.
Whatever Next: Predictive Brains, Situated Agents, and the Future of Cognitive Science
Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success. This target article critically examines this "hierarchical prediction machine" approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sects. 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency.
A Role for Action Selection in Consciousness: An Investigation of a Second-Order Darwinian Mind
CEUR Workshop Proceedings, 2016
—We investigate a small footprint cognitive architecture comprised of two reactive planner instances. The first interacts with the world via sensor and behaviour interfaces. The second monitors the first, and dynamically adjusts its plan in accordance with some predefined objective function. We show that this configuration produces a Darwinian mind, yet aware of its own operation and performance, and able to maintain performance as the environment changes. We identify this architecture as a second-order Darwinian mind, and discuss the philosophical implications for the study of consciousness. We use the Instinct Robot World agent based modelling environment, which in turn uses the Instinct Planner for cognition.