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50 Years of Artificial Intelligence: Essays Dedicated to the 50th Anniversary of Artificial Intelligence
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AI in the 21 Century – With Historical Reflections
Max Lungarella; Fumiya Iida; Josh C. Bongard; Rolf Pfeifer
The discipline of Artificial Intelligence (AI) was born in the summer of 1956 at Dartmouth College in Hanover, New Hampshire. Half of a century has passed, and AI has turned into an important field whose influence on our daily lives can hardly be overestimated. The original view of intelligence as a computer program – a set of algorithms to process symbols – has led to many useful applications now found in internet search engines, voice recognition software, cars, home appliances, and consumer electronics, but it has not yet contributed significantly to our understanding of natural forms of intelligence. Since the 1980s, AI has expanded into a broader study of the interaction between the body, brain, and environment, and how intelligence emerges from such interaction. This advent of embodiment has provided an entirely new way of thinking that goes well beyond artificial intelligence proper, to include the study of intelligent action in agents other than organisms or robots. For example, it supplies powerful metaphors for viewing corporations, groups of agents, and networked embedded devices as intelligent and adaptive systems acting in highly uncertain and unpredictable environments. In addition to giving us a novel outlook on information technology in general, this broader view of AI also offers unexpected perspectives into how to think about ourselves and the world around us. In this chapter, we briefly review the turbulent history of AI research, point to some of its current trends, and to challenges that the AI of the 21 century will have to face.
- Historical and Philosphical Issues | Pp. 1-8
The Physical Symbol System Hypothesis: Status and Prospects
Nils J. Nilsson
I analyze some of the attacks against the Physical Symbol System Hypothesis—attacks based on the presumed need for symbol-grounding and non-symbolic processing for intelligent behavior and on the supposed non-computational and “mindless” aspects of brains.
- Historical and Philosphical Issues | Pp. 9-17
Fifty Years of AI: From Symbols to Embodiment - and Back
There are many stories to tell about the first fifty years of AI. One story is about AI as one of the big forces of innovation in information technology. It is now forgotten that initially computers were just viewed as calculating machines. AI has moved that boundary, by projecting visions on what might be possible, and by building technologies to realise them. Another story is about the applications of AI. Knowledge systems were still a rarity in the late seventies but are now everywhere, delivered through the web. Knowledge systems routinely deal with financial and legal problem solving, diagnosis and maintenance of power plants and transportation networks, symbolic mathematics, scheduling, etc. The innovative aspects of search engines like Google are almost entirely based on the information extraction, data mining, semantic networks and machine learning techniques pioneered in AI. Popular games like SimCity are straightforward applications of multi-agent systems. Sophisticated language processing capacities are now routinely embedded in text processing systems like Microsoft’s Word. Tens of millions of people use AI technology every day, often without knowing it or without wondering how these information systems can do all these things. In this essay I will focus however on another story: AI as a contributor to the scientific study of mind.
- Historical and Philosphical Issues | Pp. 18-28
2006: Celebrating 75 Years of AI - History and Outlook: The Next 25 Years
When Kurt Gödel layed the foundations of theoretical computer science in 1931, he also introduced essential concepts of the theory of Artificial Intelligence (AI). Although much of subsequent AI research has focused on heuristics, which still play a major role in many practical AI applications, in the new millennium AI theory has finally become a full-fledged formal science, with important optimality results for embodied agents living in unknown environments, obtained through a combination of theory Gödel and probability theory. Here we look back at important milestones of AI history, mention essential recent results, and speculate about what we may expect from the next 25 years, emphasizing the significance of the ongoing dramatic hardware speedups, and discussing Gödel-inspired, self-referential, self-improving universal problem solvers.
- Historical and Philosphical Issues | Pp. 29-41
Evolutionary Humanoid Robotics: Past, Present and Future
Evolutionary robotics is a methodology for the creation of auto- nomous robots using evolutionary principles. Humanoid robotics is concerned specifically with autonomous robots that are human-like in that they mimic the body or aspects of the sensory, processing and/or motor functions of humans to a greater or lesser degree. We investigate how these twin strands of advanced research in the field of autonomous mobile robotics have progressed over the last decade or so, and their current recent convergence in the new field of evolutionary humanoid robotics. We describe our current work in the evolution of controllers for bipedal locomotion in a simulated humanoid robot using an accurate physics simulator, and briefly discuss the effects of changes in robot mobility and of environmental changes. We then describe our current work in the implementation of these simulated robots using the Bioloid robot platform. We conclude with a look at possible visions for the future.
- Historical and Philosphical Issues | Pp. 42-52
Philosophical Foundations of AI
David Vernon; Dermot Furlong
Artificial Intelligence was born in 1956 as the off-spring of the newly-created cognitivist paradigm of cognition. As such, it inherited a strong philosophical legacy of functionalism, dualism, and positivism. This legacy found its strongest statement some 20 years later in the physical symbol systems hypothesis, a conjecture that deeply influenced the evolution of AI in subsequent years. Recent history has seen a swing away from the functionalism of classical AI toward an alternative position that re-asserts the primacy of embodiment, development, interaction, and, more recently, emotion in cognitive systems, focussing now more than ever on enactive models of cognition. Arguably, this swing represents a true paradigm shift in our thinking. However, the philosophical foundations of these approaches — phenomenology — entail some far-reaching ontological and epistemological commitments regarding the nature of a cognitive system, its reality, and the role of its interaction with its environment. The goal of this paper is to draw out the full philosophical implications of the phenomenological position that underpins the current paradigm shift towards enactive cognition.
- Historical and Philosphical Issues | Pp. 53-62
On the Role of AI in the Ongoing Paradigm Shift within the Cognitive Sciences
This paper supports the view that the ongoing shift from orthodox to embodied-embedded cognitive science has been significantly influenced by the experimental results generated by AI research. Recently, there has also been a noticeable shift toward enactivism, a paradigm which radicalizes the embodied-embedded approach by placing autonomous agency and lived subjectivity at the heart of cognitive science. Some first steps toward a clarification of the relationship of AI to this further shift are outlined. It is concluded that the success of enactivism in establishing itself as a mainstream cognitive science research program will depend less on progress made in AI research and more on the development of a phenomenological pragmatics.
- Historical and Philosphical Issues | Pp. 63-75
On the Information Theoretic Implications of Embodiment – Principles and Methods
Rolf Pfeifer; Max Lungarella; Olaf Sporns; Yasuo Kuniyoshi
Embodied intelligent systems are naturally subject to physical constraints, such as forces and torques (due to gravity and friction), energy requirements for propulsion, and eventual damage and degeneration. But embodiment implies far more than just a set of limiting physical constraints; it directly supports the selection and processing of information. Here, we focus on an emerging link between information and embodiment, that is, on how embodiment actively supports and promotes intelligent information processing by exploiting the dynamics of the interaction between an embodied system and its environment. In this light the claim that “intelligence requires a body” means that embodied systems actively induce information structure in sensory inputs, hence greatly simplifying the major challenge posed by the need to process huge amounts of information in real time. The structure thus induced crucially depends on the embodied system’s morphology and materials. From this perspective, behavior informs and shapes cognition as it is the outcome of the dynamic interplay of physical and information theoretic processes, and not the end result of a control process that can be understood at any single level of analysis. This chapter reviews the recent literature on embodiment, elaborates some of the underlying principles, and shows how robotic systems can be employed to characterize and quantify the notion of information structure.
- Information Theory and Quantification | Pp. 76-86
Development Via Information Self-structuring of Sensorimotor Experience and Interaction
Chrystopher L. Nehaniv; Naeem Assif Mirza; Lars Olsson
We describe how current work in Artificial Intelligence is using rigorous tools from information theory, namely and to organize the self-structuring of sensorimotor perception, motor control, and experiential episodes with extended temporal horizon. Experience is operationalized from an embodied agent’s own perspective as the flow of values taken by its sensors and effectors (and possibly other internal variables) over a temporal window. Such methods allow an embodied agent to acquire the sensorimotor fields and control structure of its own body, and are being applied to pursue autonomous scaffolded proximal development in the zone between the familiar experience and the unknown.
- Information Theory and Quantification | Pp. 87-98
How Information and Embodiment Shape Intelligent Information Processing
Daniel Polani; Olaf Sporns; Max Lungarella
Embodied artificial intelligence is based on the notion that cognition and action emerge from interactions between brain, body and environment. This chapter sketches a set of foundational principles that might be useful for understanding the emergence (“discovery”) of intelligence in biological and artificial embodied systems. Special emphasis is placed on information as a crucial resource for organisms and on information theory as a promising descriptive and predictive framework linking morphology, perception, action and neural control.
- Information Theory and Quantification | Pp. 99-111
Preliminary Considerations for a Quantitative Theory of Networked Embodied Intelligence
Fabio P. Bonsignorio
This paper exposes and discusses the concept of ’networked embodied cognition’, based on natural embodied neural networks, with some considerations on the nature of natural collective intelligence and cognition, and with reference to natural biological examples, evolution theory, neural network science and technology results, network robotics. It shows that this could be the method of cognitive adaptation to the environment most widely used by living systems and most fit to the deployment of artificial robotic networks. Some preliminary ideas about the development of a quantitative framework are shortly discussed. On the basis of the work of many people a few approximate simple quantitative relations are derived between information metrics of the phase space behavior of the agent dynamical system and those of the cognition system perceived by an external observer.
- Information Theory and Quantification | Pp. 112-123
A Quantitative Investigation into Distribution of Memory and Learning in Multi Agent Systems with Implicit Communications
Roozbeh Daneshvar; Abdolhossein Sadeghi Marascht; Hossein Aminaiee; Caro Lucas
In this paper we have investigated a group of multi agent systems (MAS) in which the agents change their environment and this change has the potential to trigger behaviors in other agents of the group in another time or another position in the environment. The structure makes it possible to conceptualize the group as a super organism incorporating the agents and the environment such that new behaviors are observed from the whole group as a result of the specific distribution of agents in that environment. This distribution exists in many aspects like a super memory (or even a super brain) that exists in the environment and is not limited to memories of the individuals. There is a distributed decision making that is done by the group of agents which, in a higher level consists of both individual and group decision makings, and can be viewed as emergent rather than consciously planned. As the agents change the environment, they decrease the error for the group and hence a distributed learning is also forming between the agents of the group. This implicit learning is related to the implicit memory existing in the environment. These two interpretations of memory and learning are assessed with experimental results where two robots perform a task while they are not aware of their global behavior.
- Information Theory and Quantification | Pp. 124-133
AI in Locomotion: Challenges and Perspectives of Underactuated Robots
Fumiya Iida; Rolf Pfeifer; André Seyfarth
This article discusses the issues of adaptive autonomous navigation as a challenge of artificial intelligence. We argue that, in order to enhance the dexterity and adaptivity in robot navigation, we need to take into account the decentralized mechanisms which exploit physical system-environment interactions. In this paper, by introducing a few underactuated locomotion systems, we explain (1) how mechanical body structures are related to motor control in locomotion behavior, (2) how a simple computational control process can generate complex locomotion behavior, and (3) how a motor control architecture can exploit the body dynamics through a learning process. Based on the case studies, we discuss the challenges and perspectives toward a new framework of adaptive robot control.
- Morphology and Dynamics | Pp. 134-143
On the Task Distribution Between Control and Mechanical Systems
Akio Ishiguro; Masahiro Shimizu
This paper introduces our robotic case study which is intended to intensively investigate the neural-body coupling, , how the task distribution between control and mechanical systems should be achieved, so as to emerge useful functionalities. One of the significant features of this case study is that we have employed a collective behavioral approach. More specifically, we have focused on an “embodied” coupled nonlinear oscillator system by which we have generated one of the most primitive yet flexible locomotion, , amoeboid locomotion, in the hope that this primitiveness allows us to investigate the neural-body coupling effectively. Experiments we have conducted strongly support that there exists an “ecologically-balanced” task distribution, under which significant abilities such as real-time adaptivity emerge.
- Morphology and Dynamics | Pp. 144-153
Bacteria Integrated Swimming Microrobots
Bahareh Behkam; Metin Sitti
A new approach of integrating biological microorganisms such as bacteria to an inorganic robot body for propulsion in low velocity or stagnant flow field is proposed in this paper with the ultimate goal of fabricating a few hundreds of micrometer size swimming robots. To show the feasibility of this approach, bacteria are attached to microscale objects such as 10 micron polystyrene beads by blotting them in a bacteria swarm plate. Randomly attached bacteria are shown to propel the beads at an average speed of approximately 15 μm/sec stochastically. Using chemical stimuli, bacteria flagellar propulsion is halted by introducing copper ions into the motility medium of the beads, while ethylenediaminetetraacetic acid is used to resume their motion. Thus, repeatable on/off motion control of the bacteria integrated mobile beads was shown. On-board chemical motion control, steering, wireless communication, sensing, and position detection are few of the future challenges for this work. Small or large numbers of these microrobots can potentially enable hardware platforms for self-organization, swarm intelligence, distributed control, and reconfigurable systems in the future.
- Morphology and Dynamics | Pp. 154-163
Adaptive Multi-modal Sensors
Kyle I. Harrington; Hava T. Siegelmann
Compressing real-time input through bandwidth constrained connections has been studied within robotics, wireless sensor networks, and image processing. When there are bandwidth constraints on real-time input the amount of information to be transferred will always be greater than the amount that can be transferred per unit of time. We propose a system that utilizes a local diffusion process and a reinforcement learning-based memory system to establish a real-time prediction of an entire input space based upon partial observation. The proposed system is optimized for dealing with multi-dimension input spaces, and maintains the ability to react to rare events. Results show the relation of loss to quality and suggest that at higher resolutions gains in quality are possible.
- Morphology and Dynamics | Pp. 164-173
What Can AI Get from Neuroscience?
Steve M. Potter
The human brain is the best example of intelligence known, with unsurpassed ability for complex, real-time interaction with a dynamic world. AI researchers trying to imitate its remarkable functionality will benefit by learning more about neuroscience, and the differences between Natural and Artificial Intelligence. Steps that will allow AI researchers to pursue a more brain-inspired approach to AI are presented. A new approach that bridges AI and neuroscience is described, Embodied Cultured Networks. Hybrids of living neural tissue and robots, called hybrots, allow detailed investigation of neural network mechanisms that may inform future AI. The field of neuroscience will also benefit tremendously from advances in AI, to deal with their massive knowledge bases and help understand Natural Intelligence.
- Neurorobotics | Pp. 174-185
Dynamical Systems in the Sensorimotor Loop: On the Interrelation Between Internal and External Mechanisms of Evolved Robot Behavior
Martin Hülse; Steffen Wischmann; Poramate Manoonpong; Arndt von Twickel; Frank Pasemann
This case study demonstrates how the synthesis and the analysis of minimal recurrent neural robot control provide insights into the exploration of embodiment. By using structural evolution, minimal recurrent neural networks of general type were evolved for behavior control. The small size of the neural structures facilitates thorough investigations of behavior relevant neural dynamics and how they relate to interactions of robots within the sensorimotor loop. We argue that a clarification of dynamical neural control mechanisms in a reasonable depth allows quantitative statements about the effects of the sensorimotor loop and suggests general qualitative implications about the embodiment of autonomous robots and biological systems as well.
- Neurorobotics | Pp. 186-195
Adaptive Behavior Control with Self-regulating Neurons
Keyan Zahedi; Frank Pasemann
It is claimed that synaptic plasticity of neural controllers for autonomous robots can enhance the behavioral properties of these systems. Based on homeostatic properties of so called self-regulating neurons, the presented mechanism will vary the synaptic strength during the robot interaction with the environment, due to driving sensor inputs and motor outputs. This is exemplarily shown for an obstacle avoidance behavior in simulation.
- Neurorobotics | Pp. 196-205
Brain Area V6A: A Cognitive Model for an Embodied Artificial Intelligence
Fattori Patrizia; Breveglieri Rossella; Marzocchi Nicoletta; Maniadakis Michail; Galletti Claudio
We found that single neurons in the parietal area V6A of the macaque brain deal with all the components of reaching and grasping actions: locating in space the object target of action, directing the eyes toward it, sensing where the arm is in space, directing the arm toward the spatial location where the object is in order to reach and grasp it, adapting the grip to the object shape and size. The knowledge of how the brain codes simple visuomotor acts can be useful to build artificially-intelligent systems that have to interact with objects, localize them, direct their arm toward them, and grasp them with their gripper. Single cell recordings can also be useful in understanding how to perform more complex visuomotor tasks, like interacting with human beings, exchanging objects with them, and acting in an ever changing environment.
- Neurorobotics | Pp. 206-220
The Man-Machine Interaction: The Influence of Artificial Intelligence on Rehabilitation Robotics
Alejandro Hernández Arieta; Ryu Kato; Wenwei Yu; Hiroshi Yokoi
We are leaving in a world where the interaction with intelligent machines is an every day life event. The advances in artificial intelligence had allowed the development of adaptive machines that can modify its internal parameters to adjust their behavior according to the changing environment. One field that has profit from this is rehabilitation and prosthetics. In this respect, is our interest to evaluate the effects that this interaction has on the user. In this study, we use an f-MRI (functional Magnetic Resonance Imaging) device to measure the changes on the motor and sensory cortex of a right hand amputee’s using an EMG controlled Adaptable prosthetic hand with tactile feedback. Our results show the improvement in the adaptation to the prosthetic device, also, our experiments point to a possible modification of the body schema, generating an illusion of belonging of the robot hand to the human body.
- Neurorobotics | Pp. 221-231
Tests of Machine Intelligence
Shane Legg; Marcus Hutter
Although the definition and measurement of intelligence is clearly of fundamental importance to the field of artificial intelligence, no general survey of definitions and tests of machine intelligence exists. Indeed few researchers are even aware of alternatives to the Turing test and its many derivatives. In this paper we fill this gap by providing a short survey of the many tests of machine intelligence that have been proposed.
- Machine Intelligence, Cognition, and Natural Language Processing | Pp. 232-242
A Hierarchical Concept Oriented Representation for Spatial Cognition in Mobile Robots
Shrihari Vasudevan; Stefan Gächter; Ahad Harati; Roland Siegwart
Robots are rapidly evolving from factory work-horses to robot-companions. The future of robots, as our companions, is highly dependent on their abilities to understand, interpret and represent the environment in an efficient and consistent fashion, in a way that is compatible to humans. The work presented here is oriented in this direction. It suggests a hierarchical, concept oriented, probabilistic representation of space for mobile robots. A salient aspect of the proposed approach is that it is holistic - it attempts to create a consistent link from the sensory information the robot acquires to the human-compatible spatial concepts that the robot subsequently forms, while taking into account both uncertainty and incompleteness of perceived information. The approach is aimed at increasing spatial awareness in robots.
- Machine Intelligence, Cognition, and Natural Language Processing | Pp. 243-256
Anticipation and Future-Oriented Capabilities in Natural and Artificial Cognition
Empirical evidence indicates that grounded in the sensorimotor neural apparatus are crucially involved in several low and high level cognitive functions, including attention, motor control, planning, and goal-oriented behavior. A unitary theoretical framework is emerging that emphasizes how capabilities enable social abilities, too, including joint attention, imitation, perspective taking and communication. We argue that anticipation will be a key element for bootstrapping high level cognitive functions in cognitive robotics, too. We thus propose the challenge of understanding how anticipatory representations, that serve for and not only with the present, develop in situated agents.
- Machine Intelligence, Cognition, and Natural Language Processing | Pp. 257-270
Computer-Supported Human-Human Multilingual Communication
Alex Waibel; Keni Bernardin; Matthias Wölfel
Computers have become an essential part of modern life, providing services in a multiplicity of ways. Access to these services, however, comes at a price: human attention is bound and directed toward a technical artifact in a human-machine interaction setting at the expense of time and attention for other humans. This paper explores a new class of computer services that support human- interaction and communication and . Computers in the Human Interaction Loop (CHIL), require consideration of all communication modalities, multimodal integration and more robust performance. We review the technologies and several CHIL services providing human-human support. Among them, we specifically highlight advanced computer services for communication.
- Machine Intelligence, Cognition, and Natural Language Processing | Pp. 271-287
A Paradigm Shift in Artificial Intelligence: Why Social Intelligence Matters in the Design and Development of Robots with Human-Like Intelligence
The chapter discusses a recent paradigm shift in the field of Artificial Intelligence regarding the nature of human intelligence and its implications for the design and development of intelligent robots. It will be argued that social intelligence is not a mere ‘add-on’ to intelligent robot behaviour for the practical purpose of enabling the robot to interact smoothly with other robots or people, but that social intelligence might be a stepping stone towards more human-like, embodied artificial intelligence. The argument is supported by discussions in primatology highlighting the social origins of primate intelligence. The chapter also discusses challenges and opportunities provided by socially intelligent robots, with implications for our future.
- Human-Like Intelligence: Motivation, Emotions, and Consciousness | Pp. 288-302
Intrinsically Motivated Machines
Frédéric Kaplan; Pierre-Yves Oudeyer
Children seem intrinsically motivated to manipulate, to explore, to test, to learn and they look for activities and situations that provide such learning opportunities. Inspired by research in developmental psychology and neuroscience, some researchers have started to address the problem of designing intrinsic motivation systems. A robot controlled by such systems is able to autonomously explore its environment not to fulfil predefined tasks but driven by an incentive to search for situations where learning happens efficiently. In this paper, we present the origins of these intrinsically motivated machines, our own research in this novel field and we argue that intrinsic motivation might be a crucial step towards machines capable of life-long learning and open-ended development.
- Human-Like Intelligence: Motivation, Emotions, and Consciousness | Pp. 303-314
Curious and Creative Machines
I recently gave a robot demonstration to a class of 1-grade elementary school children. In the school’s gymnasium hall, a few dozen 6-year-olds gathered enthusiastically around a few shiny machines with plenty of sensors and actuators, demonstrating patterns of locomotion. “These robots learned how to move by themselves” – I explained. “Some even developed their own shape”, I said, pointing at a set of 3D-printed plastic robots whose morphology and control evolved in simulation.
- Human-Like Intelligence: Motivation, Emotions, and Consciousness | Pp. 315-319
Applying Data Fusion in a Rational Decision Making with Emotional Regulation
Benjamin Fonooni; Behzad Moshiri; Caro Lucas
This paper focuses on designing a goal based rational component of a believable agent which has to interact with facial expressions with humans in communicative scenarios like teaching. One of the main concerns of the proposed model is to define interactions among rationality, personality and emotion in order to fulfill the idea of making rational decisions with emotional regulation. Our research aims are directed towards improving decision making process by means of applying Data Fusion techniques, especially Ordered Weighted Averaging (OWA) operator as a goal selection mechanism. Also the issue of obtaining weights for OWA aggregation is discussed. Finally the suggested algorithm is tested and results are provided with a real benchmark.
- Human-Like Intelligence: Motivation, Emotions, and Consciousness | Pp. 320-331
How to Build Consciousness into a Robot: The Sensorimotor Approach
J. Kevin O’Regan
The problem of consciousness has been divided by philosophers into the problem of Access Consciousness and the problem of Phenomenal Consciousness or "raw feel". In this chapter it is suggested that Access Consciousness is something that we can logically envisage building into a robot because it is a cognitive capacity giving rise to behaviors or behavioral tendencies or potentials. A few examples are given of how this is being done in current research. On the other hand, Phenomenal Consciousness or "raw feel" is problematic, since we do not know what we really mean by "feel". It is suggested that three main properties are what characterize feel: the fact that feels are different from each other, that there is structure in these differences, and that feels have sensory presence. It is then shown how, by taking the sensorimotor approach it is possible to account for these properties in a natural way and furthermore to make counter-intuitive empirical predictions which have recently been confirmed. In conclusion it is claimed that when we take the sensorimotor approach to feel, building raw feel into a robot becomes a theoretical possibility, even if we are a long way from actually attaining it.
- Human-Like Intelligence: Motivation, Emotions, and Consciousness | Pp. 332-346
A Human-Like Robot Torso ZAR5 with Fluidic Muscles: Toward a Common Platform for Embodied AI
Ivo Boblan; Rudolf Bannasch; Andreas Schulz; Hartmut Schwenk
“Without embodiment artificial intelligence is nothing.” Algorithms in the field of artificial intelligence are mostly tested on a computer instead of testing on a real platform. Our anthropomorphic robot ZAR5 (in German Zwei-Arm-Roboter in the 5 version) is the first biologically inspired and completely artificial muscle driven robot torso that can be fully controlled by a data suit and two five finger data gloves. The underlying biological principles of sensor technology, signal processing, control architecture und actuator technology of our robot platform meet the requirements of biological based technical realization and support a distributed programming and control as well as an online self-adaptation and relearning processing. The following elaboration focuses on biological inspiration for the embodiment of artificial intelligence, gives a short insight into technical realisation of a humanoid robot, which is of high importance in this context, and accentuates highlights relating to a possible paradigm shift in artificial intelligence.
- Robot Platforms | Pp. 347-357
The Cognitive Humanoid Robot: An Open-System Research Platform for Enactive Cognition
Giulio Sandini; Giorgio Metta; David Vernon
This paper describes a multi-disciplinary initiative to promote collaborative research in enactive artificial cognitive systems by developing the : a open-systems 53 degree-of-freedom cognitive humanoid robot. At 94 cm tall, the is the same size as a three year-old child. It will be able to crawl on all fours and sit up, its hands will allow dexterous manipulation, and its head and eyes are fully articulated. It has visual, vestibular, auditory, and haptic sensory capabilities. As an open system, the design and documentation of all hardware and software is licensed under the Free Software Foundation GNU licences so that the system can be freely replicated and customized. We begin this paper by outlining the enactive approach to cognition, drawing out the implications for phylogenetic configuration, the necessity for ontogenetic development, and the importance of humanoid embodiment. This is followed by a short discussion of our motivation for adopting an open-systems approach. We proceed to describe the mechanical and electronic specifications, its software architecture, its cognitive architecture. We conclude by discussing the phylogeny, the robot’s intended innate abilities, and an scenario for ontogenesis based on human neo-natal development.
- Robot Platforms | Pp. 358-369
Intelligent Mobile Manipulators in Industrial Applications:Experiences and Challenges
Hansruedi Früh; Philipp Keller; Tino Perucchi
This paper describes how industrial applications were targeted and successfully implemented by robotic manipulators that have been developed from studies in embodied artificial intelligent systems. The goal was to design mobile, flexible and self-learning manipulators that allow to perform multiple tasks with very short preparation time, a reasonable working speed and, at the same time, in a human-like manner. The advantages and disadvantages of these solutions compared to traditional industrial robot applications had to be considered continuously to concentrate on the right market segments, applications and customers. Thus, in addition to develop the appropriate requirements of real-time executions, risk analyses and usability, studies were established and implemented in collaboration with scientists, integrators and end customers. Acceptance, impacts of the revolution in personal intelligent robotics as well as challenges to overcome in the future are discussed.
- Robot Platforms | Pp. 370-385
The Dynamic Darwinian Diorama: A Landlocked Archipelago Enhances Epistemology
This paper discusses the relevance of embedding dramatic scenarios and expressive language into methodologies employed in the research and development of biochemical and/or electronic sentient beings. The author demonstrates how integrating imagined modalities into current practices can afford a profound and positive effect on outcomes.
- Art and AI | Pp. 386-398