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| Brooks, R., L.A. Stein, Building Brains for Bodies Autonomous Robots 1994 (1)1:pp. 7-25 [pdf] |
| We describe a project to capitalize on newly available levels of computational resources in order to understand human cognition. We are building an integrated physical system including vision, sound input and output, and dextrous manipulation, all controlled by a continuously operating large scale parallel MIMD computer. The resulting system will learn to "think" by building on its bodily experiences to accomplish progressively more abstract tasks. Past experience suggests that in attempting to build such an integrated system we will have to fundamentally change the way artificial intelligence, cognitive science, linguistics, and philosophy think about the organization of intelligence. We expect to be able to better reconcile the theories that will be developed with current work in neuroscience. |
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| Lungarella, M., Metta, G., Pfeifer, R., Sandini, G. Developmental robotics: a survey Connection Science 2004 (0)0:1-40 [pdf] |
| Developmental robotics is an emerging field located at the intersection of robotics, cognitive science and developmental sciences. This paper elucidates the main reasons and key motivations behind the convergence of fields with seemingly disparate interests, and shows why developmental robotics might prove to be beneficial for all fields involved. The methodology advocated is synthetic and two-pronged: on the one hand, it employs robots to instantiate models originating from developmental sciences; on the other hand, it aims to develop better robotic systems by exploiting insights gained from studies on ontogenetic development. This paper gives a survey of the relevant research issues and points to some future research directions. |
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| Weng, J., Zhang, Y. Developmental robots: A new paradigm 2002 [pdf] |
| It has been proved to be extremely challenging for humans to program a robot to such a su±cient degree that it acts properly in a typical unknown human environment. This is especially true for a humanoid robot due to the very large number of redundant degrees of freedom and a large number of sensors that are required for a humanoid to work safely and e®ectively in the human environment. How can we address this fundamental problem? Motivated by human mental development from infancy to adulthood, we present a theory, an architecture, and some experimental results showing how to enable a robot to develop its mind automatically, through online, real time interactions with its environment. Humans mentally raise the robot through robot sitting and robot schools instead of task-specific robot programming. |
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| Pierce, D., Kuipers, B. Map learning with Uninterpreted Sensors and Effectors Artificial Intelligence 1997 (92):169-229 |
| This paper presents a set of methods by which a learning agent can learn a sequence of increasingly abstract and powerful interfaces to control a robot whose sensorimotor apparatus and environment are initially unknown. The result of the learning is a rich hierarchical model of the robot's world (its sensimotor apparatus and environment). The learning methods rely on generic properties of the robot's world such as almost-everywhere smooth effects of motor control signals on sensory features. At the lowest level of the hierarchy, the learning agent analyzes the effects of its motor control signals in order to define a new set of control signals, one of each of the robot's degrees of freedom. It uses a generate-and-test approach to define sensory features that capture important aspects of the environment. It uses linear regression to learn models that characterize context-dependent effects of the control laws for finding and following paths defined using constraints on the learned features. The agent abstracts these control laws, which interact with the continuous environment, to a finite set of actions that implement discrete state transitions. At this point, the agent has abstracted the robot's continuous world to a finite-state world and can use existing methods to learn its structure. The learning agent's methods are evaluated on several simulated robots with different sensorimotor systems and environments. |
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| Searle, J. Minds, Brains and programs Behavioral and Brain Sciences 1980 (3)7:585-642 [html] |
| What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI (artificial intelligence). According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states. In strong AI, because the programmed computer has cognitive states, the programs are not mere tools that enable us to test psychological explanations; rather, the programs are themselves the explanations. |
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| Churchland, P.S., Sejnowski, T.J. The Computational Brain 1992 [html] |
| This attractive and well-illustrated volume falls somewhere between a trade book and a textbook, with a style well suited for the Scientific American reader, as well as the active scientist, who may know something of either computer science or neuroscience but welcomes a crisp narrative that includes the necessary background from each discipline.... The reader will be well rewarded who seeks to understand, from well-chosen examples, how to merge the analysis of neuroscientific data with the developments of computational principles." -- Michael A. Arbib, Science The Computational Brain is the first unified and broadly accessible book to bring together computational concepts and behavioral data within a neurobiological framework. Churchland and Sejnowski address the foundational ideas of the emerging field of computational neuroscience, examine a diverse range of neural network models, and consider future directions of the field. |
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| Marr, D. Vision: a computational investigation into the human representation and processing of visual information 1982 |
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