Catálogo de publicaciones - libros

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.

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.

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.

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.

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.

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.

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.

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.

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.