There are some signs that a resurgence of interest in modeling constitutive autonomy is underway. This paper contributes to this recent development by exploring the possibility of using evolutionary robotics, traditionally only used as a generative mechanism for the study of embodied-embedded cognitive systems, to generate simulation models of constitutively autonomous systems. Such systems, which are autonomous in the sense that they self-constitute an identity under precarious conditions, have so far been elusive. The challenges and opportunities involved in such an endeavor are explicated in terms of a concrete model. While we conclude that this model fails to fully satisfy all the organizational criteria that are required for constitutive autonomy, it nevertheless serves to illustrate that evolutionary robotics at least has the potential to become a valuable tool for generating such models.

Key words: artificial life, autopoiesis, evolutionary robotics, autonomy.

Some concerns have recently been raised with regard to the sufficiency of current embodied AI for advancing our scientific understanding of intentional agency. While from an engineering or computer science perspective this limitation might not be relevant, it is of course highly relevant for AI researchers striving to build accurate models of natural cognition. We argue that the biological foundations of enactive cognitive science can provide the conceptual tools that are needed to diagnose more clearly the shortcomings of current embodied AI. In particular, taking an enactive perspective points to the need for AI to take seriously the organismic roots of autonomous agency and sense-making. We identify two necessary systemic requirements, namely constitutive autonomy and adaptivity, which lead us to introduce two design principles of enactive AI. It is argued that the development of such enactive AI poses a significant challenge to current methodologies. However, it also provides a promising way of eventually overcoming the current limitations of embodied AI, especially in terms of providing fuller models of natural embodied cognition. Finally, some practical implications and examples of the two design principles of enactive AI are also discussed.

In the field of artificial life there is no agreement on what defines ‘autonomy’. This makes it difficult to measure progress made towards understanding as well as engineering autonomous systems. Here, we review the diversity of approaches and categorize them by introducing a conceptual distinction between behavioral and constitutive autonomy. Differences in the autonomy of artificial and biological agents tend to be marginalized for the former and treated as absolute for the latter. We argue that with this distinction the apparent opposition can be resolved.

We introduce Radical Interactionism (RI), which extends Franklin et al.’s (2013) Cognitive Cycles as Cognitive Atoms (CCCA) proposal in their discussion on conceptual commitments in cognitive models. Similar to the CCCA commitment, the RI commitment acknowledges the indivisibility of the perception-action cycle. However, it also reifies the perception-action cycle as sensorimotor interaction and uses it to replace the traditional notions of observation and action. This complies with constructivist epistemology, which suggests that knowledge of reality is constructed from regularities observed in sensorimotor experience. We use the LIDA cognitive architecture as an example to examine the implications of RI on cognitive models. We argue that RI permits self- programming and constitutive autonomy, which have been acknowledged as desirable cognitive capabilities in artificial agents.

Key words: constructivist learning, sensorimotor modeling, self-programming, constitutive autonomy, self-motivation, Enaction.

A novel way to model an agent interacting with an environment is introduced, called an Enactive Markov Decision Process (EMDP). An EMDP keeps perception and action embedded within sensorimotor schemes rather than dissociated, in compliance with theories of embodied cognition. Rather than seeking a goal associated with a reward, as in reinforcement learning, an EMDP agent learns to master the sensorimotor contingencies offered by its coupling with the environment. In doing so, the agent exhibits a form of intrinsic motivation related to the autotelic principle (Steels), and a value system attached to interactions called “interactional motivation.” This modeling approach allows the design of agents capable of autonomous self-programming, which provides rudimentary constitutive autonomy – a property that theoreticians of enaction consider necessary for autonomous sense-making (e.g., Froese & Ziemke). A cognitive architecture is presented that allows the agent to discover, memorize, and exploit spatio-sequential regularities of interaction, called Enactive Cognitive Architecture (ECA). In our experiments, behavioral analysis shows that ECA agents develop active perception and begin to construct their own ontological perspective on the environment. Relevance: This publication relates to constructivism by the fact that the agent learns from input data that does not convey ontological information about the environment. That is, the agent learns by actively experiencing its environment through interaction, as opposed to learning by registering observations directly characterizing the environment. This publication also relates to enactivism by the fact that the agent engages in self-programming through its experience from interacting with the environment, rather than executing pre-programmed behaviors.

This paper introduces an original model to provide software agents and robots with the capacity of learning by interpreting regularities in their stream of sensorimotor experience rather than by exploiting data that would give them ontological information about a predefined domain. Specifically, this model pulls inspiration from: a) the movement of embodied cognition, b) the philosophy of knowledge, c) constructivist epistemology, and d) the theory of enaction. Respectively to these four influences: a) Our agents discover their environment through their body’s active capacity of experimentation. b) They do not know their environment “as such” but only “as they can experience it.” c) They construct knowledge from regularities of sensorimotor experience. d) They have some level of constitutive autonomy. Technically, this model differs from the traditional perception/cognition/action model in that it rests upon atomic sensorimotor experiences rather than separating percepts from actions. We present algorithms that implement this model, and we describe experiments to validate these algorithms. These experiments show that the agents exhibit a certain form of intelligence through their behaviors, as they construct proto-ontological knowledge of the phenomena that appear to them when they observe persistent possibilities of sensorimotor experiences in time and space. These results promote a theory of artificial intelligence without ontological data about a presupposed reality. An application includes a more robust way of creating robots capable of constructing their own knowledge and goals in the real world, which could be initially unknown to them and un-modeled by their designers.

Key words: artificial intelligence, embodied cognition, constructivist learning, enaction, hierarchical sequence learning, trace-based reasoning, cognitive architecture, self-motivation

This paper introduces an original model to provide software agents and robots with the capacity of learning by interpreting regularities in their stream of sensorimotor experience rather than by exploiting data that would give them ontological information about a predefined domain. Specifically, this model pulls inspiration from: a) the movement of embodied cognition, b) the philosophy of knowledge, c) constructivist epistemology, and d) the theory of enaction. Respectively to these four influences: a) Our agents discover their environment through their body’s active capacity of experimentation. b) They do not know their environment “as such” but only “as they can experience it.” c) They construct knowledge from regularities of sensorimotor experience. d) They have some level of constitutive autonomy. Technically, this model differs from the traditional perception/cognition/action model in that it rests upon atomic sensorimotor experiences rather than separating percepts from actions. We present algorithms that implement this model, and we describe experiments to validate these algorithms. These experiments show that the agents exhibit a certain form of intelligence through their behaviors, as they construct proto-ontological knowledge of the phenomena that appear to them when they observe persistent possibilities of sensorimotor experiences in time and space. These results promote a theory of artificial intelligence without ontological data about a presupposed reality. An application includes a more robust way of creating robots capable of constructing their own knowledge and goals in the real world, which could be initially unknown to them and un-modeled by their designers.

Key words: artificial intelligence, embodied cognition, constructivist learning, enaction, hierarchical sequence learning, trace-based reasoning, cognitive architecture, self-motivation.

The enactive AI framework wants to overcome the sense-making limitations of embodied AI by drawing on the bio-systemic foundations of enactive cognitive science. While embodied AI tries to ground meaning in sensorimotor interaction, enactive AI adds further requirements by grounding sensorimotor interaction in autonomous agency. At the core of this shift is the requirement for a truly intrinsic value function. We suggest that empowerment, an information-theoretic quantity based on an agents embodiment, represents such a function. We highlight the role of empowerment maximisation in satisfying the requirements of enactive AI, i.e. establishing constitutive autonomy and adaptivity, in detail. We then argue that empowerment, grounded in a precarious existence, allows an agent to enact a world based on the relevance of environmental features in respect to its own identity.

In the enactive paradigm of cognitive science, development plays a crucial role in the realization of cognition. This position runs counter to the computational functionalism upon which cognitivist and classical artificial intelligence systems are founded, especially the assumption that cognition can be achieved by embedding pre-formed knowledge. The enactive stance involves a progressive phased transition from cognitive capacity to cognitive capability, highlighting the role of development in extending the timescale of a cognitive agent’s prospective abilities and in expanding its repertoire of effective action. We review briefly some necessary conditions for cognitive development, drawing on examples from developmental psychology, illustrating the ideas by looking at the ontogenesis of instrumental helping and collaboration in infants, and identifying some of the essential elements of a developmental cognitive architecture. We then focus on the fact that enactive systems are operationally-closed, autonomous, and self-maintaining. Consequently, there are organizational constitutive processes at play as well as behavioural ones. Reconciling these complementary processes poses a significant challenge for the creation of complete model of development that must show how constitutive autonomy is compatible with and may even give rise to behavioural autonomy. We conclude by drawing attention to recent research which could provide a way of addressing this challenge.

Key words: behavioural autonomy cognitive architecture constitutive autonomy development enaction ontogeny phylogeny value systems

The reciprocal coupling of perception and action in cognitive agents has been firmly established: perceptions guide action but so too do actions influence what is perceived. While much has been said on the implications of this for the agent’s external behavior, less attention has been paid to what it means for the internal bodily mechanisms which underpin cognitive behavior. In this article, we wish to redress this by reasserting that the relationship between cognition, perception, and action involves a constitutive element as well as a behavioral element, emphasizing that the reciprocal link between perception and action in cognition merits a renewed focus on the system dynamics inherent in constitutive biological autonomy. Our argument centers on the idea that cognition, perception, and action are all dependent on processes focussed primarily on the maintenance of the agent’s autonomy. These processes have an inherently circular nature – self-organizing, self-producing, and self-maintaining – and our goal is to explore these processes and suggest how they can explain the reciprocity of perception and action. Specifically, we argue that the reciprocal coupling is founded primarily on their endogenous roles in the constitutive autonomy of the agent and an associated circular causality of global and local processes of self-regulation, rather than being a mutual sensory-motor contingency that derives from exogenous behavior. Furthermore, the coupling occurs first and foremost via the internal milieu realized by the agent’s organismic embodiment. Finally, we consider how homeostasis and the related concept of allostasis contribute to this circular self-regulation.