Tom Ziemke

Enactive artificial intelligence: Investigating the systemic organization of life and mind

The embodied and situated approach to artificial intelligence (AI) has matured and become a viable alternative to traditional computationalist approaches with respect to the practical goal of building artificial agents, which can behave in a robust and flexible manner under changing real-world conditions. Nevertheless, 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.

On the Role of Emotion in Embodied Cognitive Architectures : From Organisms to Robots

The computational modeling of emotion has been an area of growing interest in cognitive robotics research in recent years, but also a source of contention regarding how to conceive of emotion and how to model it. In this paper, emotion is characterized as (a) closely connected to embodied cognition, (b) grounded in homeostatic bodily regulation, and (c) a powerful organizational principle—affective modulation of behavioral and cognitive mechanisms—that is ‘useful’ in both biological brains and robotic cognitive architectures. We elaborate how emotion theories and models centered on core neurological structures in the mammalian brain, and inspired by embodied, dynamical, and enactive approaches in cognitive science, may impact on computational and robotic modeling. In light of the theoretical discussion, work in progress on the development of an embodied cognitive-affective architecture for robots is presented, incorporating aspects of the theories discussed.

On the role of emotion in biological and robotic autonomy

This paper reviews some of the differences between notions of biological and robotic autonomy, and how these differences have been reflected in discussions of embodiment, grounding and other concepts in AI and autonomous robotics. Furthermore, the relations between homeostasis, emotion and embodied cognition are discussed as well as recent proposals to model their interplay in robots, which reflects a commitment to a multi-tiered affectively/emotionally embodied view of mind that takes organismic embodiment more serious than usually done in biologically inspired robotics.

Theories and computational models of affordance and mirror systems: An integrative review

Neuroscientific and psychological data suggest a close link between affordance and mirror systems in the brain. However, we still lack a full understanding of both the individual systems and their interactions. Here, we propose that the architecture and functioning of the two systems is best understood in terms of two challenges faced by complex organisms, namely: (a) the need to select among multiple affordances and possible actions dependent on context and high-level goals and (b) the exploitation of the advantages deriving from a hierarchical organisation of behaviour based on actions and action-goals. We first review and analyse the psychological and neuroscientific literature on the mechanisms and processes organisms use to deal with these challenges. We then analyse existing computational models thereof. Finally we present the design of a computational framework that integrates the reviewed knowledge. The framework can be used both as a theoretical guidance to interpret empirical data and design new experiments, and to design computational models addressing specific problems debated in the literature.

Sentence processing: linking language to motor chains

A growing body of evidence in cognitive science and neuroscience points towards the existence of a deep interconnection between cognition, perception and action. According to this embodied perspective language is grounded in the sensorimotor system and language understanding is based on a mental simulation process (Jeannerod, 2007; Gallese, 2008; Barsalou, 2009). This means that during action words and sentence comprehension the same perception, action, and emotion mechanisms implied during interaction with objects are recruited. Among the neural underpinnings of this simulation process an important role is played by a sensorimotor matching system known as the mirror neuron system (Rizzolatti and Craighero, 2004). Despite a growing number of studies, the precise dynamics underlying the relation between language and action are not yet well understood. In fact, experimental studies are not always coherent as some report that language processing interferes with action execution while others find facilitation. In this work we present a detailed neural network model capable of reproducing experimentally observed influences of the processing of action-related sentences on the execution of motor sequences. The proposed model is based on three main points. The first is that the processing of action-related sentences causes the resonance of motor and mirror neurons encoding the corresponding actions. The second is that there exists a varying degree of crosstalk between neuronal populations depending on whether they encode the same motor act, the same effector or the same action-goal. The third is the fact that neuronal populations’ internal dynamics, which results from the combination of multiple processes taking place at different time scales, can facilitate or interfere with successive activations of the same or of partially overlapping pools.

Robot-assisted therapy for autism spectrum disorders with (partially) autonomous control: Challenges and outlook

Robot-assisted therapy (RAT) is an emerging field that has already seen some success and is likely to develop in the future. One particular application area is within therapies for autism spectrum disorders, in which the viability of the approach has been demonstrated.The present paper is a vision paper with the aim of identifying research directions in the near future of RAT. Specifically, we argue that the next step in such therapeutic scenarios is the development of more substantial levels of autonomy which would allow the robot to adapt to the individual needs of children over longer periods of time (while remaining under the ultimate supervision of a therapist). We argue that this requires new advances on the level of robot controllers as well as the ability to infer and classify intentions, goals and emotional states of the robot’s interactants. We show that the state of the art in a number of relevant disciplines is now at the point at which such an endeavour can be approached in earnest.

Social Situatedness of Natural and Artificial Intelligence: Vygotsky and Beyond ¤

The concept of “social situatedness,” that is, the idea that the development of individual intelligence requires a social (and cultural) embedding, has recently received much attention in cognitive science and artificial intelligence research, in particular work on social or epigenetic robotics. The work of Lev Vygotsky, who put forward this view as early as the 1920s, has influenced the discussion to some degree but still remains far from well known. This article therefore is aimed at giving an overview of his cognitive development theory and a discussion of its relation to more recent work in primatology and socially situated artificial intelligence, in particular humanoid robotics.

Neuromodulation of Reactive Sensorimotor Mappings as a Short-Term Memory Mechanism in Delayed Response Tasks

This article addresses the relation between memory, representation, and adaptive behavior. More specifically, it demonstrates and discusses the use of synaptic plasticity, realized through neuromodulation of sensorimotor mappings, as a short-term memory mechanism in delayed response tasks. A number of experiments with extended sequential cascaded networks, that is, higher-order recurrent neural nets, controlling simple robotic agents in six different delayed response tasks are presented. The focus of the analysis is on how short-term memory is realized in such control networks through the dynamic modulation of sensorimotor mappings (rather than through feedback of neuronal activation, as in conventional recurrent nets), and how these internal dynamics interact with environmental/behavioral dynamics. In particular, it is demonstrated in the analysis of the last experimental scenario how this type of network can make very selective use of feedback/memory, while as far as possible limiting itself to the use of reactive sensorimotor mechanisms and occasional switches between them.

Adaptive Behavior in Autonomous Agents

This paper provides an overview of the bottom-up approach to artificial intelligence (AI), commonly referred to as behavior-oriented AI. The behavior-oriented approach, with its focus on the interaction between autonomous agents and their environments, is introduced by contrasting it with the traditional approach of knowledge-based AI. Different notions of autonomy are discussed, and key problems of generating adaptive and complex behavior are identified. A number of techniques for the generation of behavior are introduced and evaluated regarding their potential for realizing different aspects of autonomy as well as adaptivity and complexity of behavior. It is concluded that, in order to realize truly autonomous and intelligent agents, the behavior-oriented approach will have to focus even more on lifelike qualities in both agents and environments.

Extracting rules from expert operators to support situation awareness in maritime surveillance

In maritime surveillance, supporting operatorspsila situation awareness is a very important issue for enabling the possibility to detect anomalous behaviour. We present a user study which conceptualises knowledge to be implemented in a rule-based application aiming at supporting situation awareness. Participatory observations were used as a method for extracting operatorspsila knowledge. The result of the user study is in the form of a number of identified rules emerging from organisational factors, group thinking and individual experience. A description of the rule-based prototype is presented a long with the result from the user study. This is also discussed together with the applicability of rule based systems and how to support situation awareness.