4,964 research outputs found
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
Enaction-Based Artificial Intelligence: Toward Coevolution with Humans in the Loop
This article deals with the links between the enaction paradigm and
artificial intelligence. Enaction is considered a metaphor for artificial
intelligence, as a number of the notions which it deals with are deemed
incompatible with the phenomenal field of the virtual. After explaining this
stance, we shall review previous works regarding this issue in terms of
artifical life and robotics. We shall focus on the lack of recognition of
co-evolution at the heart of these approaches. We propose to explicitly
integrate the evolution of the environment into our approach in order to refine
the ontogenesis of the artificial system, and to compare it with the enaction
paradigm. The growing complexity of the ontogenetic mechanisms to be activated
can therefore be compensated by an interactive guidance system emanating from
the environment. This proposition does not however resolve that of the
relevance of the meaning created by the machine (sense-making). Such
reflections lead us to integrate human interaction into this environment in
order to construct relevant meaning in terms of participative artificial
intelligence. This raises a number of questions with regards to setting up an
enactive interaction. The article concludes by exploring a number of issues,
thereby enabling us to associate current approaches with the principles of
morphogenesis, guidance, the phenomenology of interactions and the use of
minimal enactive interfaces in setting up experiments which will deal with the
problem of artificial intelligence in a variety of enaction-based ways
Robot pain: a speculative review of its functions
Given the scarce bibliography dealing explicitly with robot pain, this chapter has enriched its review with related research works about robot behaviours and capacities in which pain could play a role. It is shown that all such roles Âżranging from punishment to intrinsic motivation and planning knowledgeÂż can be formulated within the unified framework of reinforcement learning.Peer ReviewedPostprint (author's final draft
Why it is important to build robots capable of doing science
Science, like any other cognitive activity, is grounded in the sensorimotor interaction of our bodies with the environment. Human embodiment thus constrains the class of scientific concepts and theories which are accessible to us. The paper explores the possibility of doing science with artificial cognitive agents, in the framework of an interactivist-constructivist cognitive model of science. Intelligent robots, by virtue of having different sensorimotor capabilities, may overcome the fundamental limitations of human science and provide important technological innovations. Mathematics and nanophysics are prime candidates for being studied by artificial scientists
Intrinsic Motivation Systems for Autonomous Mental Development
Exploratory activities seem to be intrinsically rewarding
for children and crucial for their cognitive development.
Can a machine be endowed with such an intrinsic motivation
system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development.The complexity of the robot’s activities autonomously increases and complex developmental sequences self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations
which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learned. Finally, these various results are discussed in relation to more complex forms of behavioral organization and data coming from developmental psychology.
Key words: Active learning, autonomy, behavior, complexity,
curiosity, development, developmental trajectory, epigenetic
robotics, intrinsic motivation, learning, reinforcement learning,
values
Enactivism and Robotic Language Acquisition: A Report from the Frontier
In this article, I assess an existing language acquisition architecture, which was deployed in linguistically unconstrained human–robot interaction, together with experimental design decisions with regard to their enactivist credentials. Despite initial scepticism with respect to enactivism’s applicability to the social domain, the introduction of the notion of participatory sense-making in the more recent enactive literature extends the framework’s reach to encompass this domain. With some exceptions, both our architecture and form of experimentation appear to be largely compatible with enactivist tenets. I analyse the architecture and design decisions along the five enactivist core themes of autonomy, embodiment, emergence, sense-making, and experience, and discuss the role of affect due to its central role within our acquisition experiments. In conclusion, I join some enactivists in demanding that interaction is taken seriously as an irreducible and independent subject of scientific investigation, and go further by hypothesising its potential value to machine learning.Peer reviewedFinal Published versio
From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness (Part 3)
This third paper locates the synthetic neurorobotics research reviewed in the second paper in terms of themes introduced in the first paper. It begins with biological non-reductionism as understood by Searle. It emphasizes the role of synthetic neurorobotics studies in accessing the dynamic structure essential to consciousness with a focus on system criticality and self, develops a distinction between simulated and formal consciousness based on this emphasis, reviews Tani and colleagues' work in light of this distinction, and ends by forecasting the increasing importance of synthetic neurorobotics studies for cognitive science and philosophy of mind going forward, finally in regards to most- and myth-consciousness
Computational and Robotic Models of Early Language Development: A Review
We review computational and robotics models of early language learning and
development. We first explain why and how these models are used to understand
better how children learn language. We argue that they provide concrete
theories of language learning as a complex dynamic system, complementing
traditional methods in psychology and linguistics. We review different modeling
formalisms, grounded in techniques from machine learning and artificial
intelligence such as Bayesian and neural network approaches. We then discuss
their role in understanding several key mechanisms of language development:
cross-situational statistical learning, embodiment, situated social
interaction, intrinsically motivated learning, and cultural evolution. We
conclude by discussing future challenges for research, including modeling of
large-scale empirical data about language acquisition in real-world
environments.
Keywords: Early language learning, Computational and robotic models, machine
learning, development, embodiment, social interaction, intrinsic motivation,
self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J.
Horst and J. von Koss Torkildsen, Routledg
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