|
 ABSTRACT
While classic AI systems still struggle to properly incorporate common-sense knowledge, Situated and Embodied Artificial Intelligence (SEAI) aims to build animats that acquire a common-sense understanding of the world via interactions between simulated brains, bodies and environments. Neuroscientists believe that much of this common sense involves predictive models for physical activities, but the transfer of sensorimotor skill knowledge to cognition is non-trivial, indicating that SEAI may meet a daunting challenge of its own. This paper considers the neurological basis for procedural common sense and the possibilities for its transfer to conscious reasoning. This helps assess the prospects for SEAI to eventually surpass classic AI in the quest for generally intelligence systems.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
| |
1
|
A. Barto, Adaptive critics and the basal ganglia, in Models of Information Processing in the Basal Ganglia, J. Houk, J. Davis, and D. Beiser, eds., Cambridge, MA, 1995, The MIT Press, pp. 215--232.
|
| |
2
|
M. Bear, B. Conners, and M. Paradiso, Neuroscience: Exploring the Brain, Lippincott Williams and Wilkins, Baltimore, MD, 2 ed., 2001.
|
| |
3
|
R. Beer, The dynamics of active categorical perception in an evolved model agent, Adaptive Behavior, 11 (2003), pp. 209--243.
|
| |
4
|
|
| |
5
|
|
| |
6
|
T. Deacon, The Symbolic Species: The Co-evolution of Language and the Brain, W.W. Norton and Company, New York, 1998.
|
| |
7
|
|
| |
8
|
G. Edelman and G. Tononi, A Universe of Consciousness, Basic Books, New York, NY, 2000.
|
| |
9
|
J. Houk, Information processing in modular circuits linking basal ganglia and cerebral cortex, in Models of Information Processing in the Basal Ganglia, J. Houk, J. Davis, and D. Beiser, eds., Cambridge, MA, 1995, The MIT Press, pp. 3--9.
|
| |
10
|
J. Houk, J. Adams, and A. Barto, A model of how the basal ganglia generate and use neural signals that predict reinforcement, in Models of Information Processing in the Basal Ganglia, J. Houk, J. Davis, and D. Beiser, eds., Cambridge, MA, 1995, The MIT Press, pp. 249--270.
|
| |
11
|
D.-A. Jirenhed, G. Hesslow, and T. Ziemke, Exploring internal simulation of perception in mobile robots, in Proceedings of the 4th European Workshop on Advanced Mobile Robots, Arras, Baerveldt, Balkenius, Burgard, and Siegwart, eds., 2001, pp. 107--113.
|
| |
12
|
E. Kandel, J. Schwartz, and T. Jessell, Principles of Neural Science, McGraw-Hill, New York, NY, 2000.
|
| |
13
|
G. Lakoff and R. Nunez, Where Mathematics Comes From, Basic Books, New York, 2000.
|
| |
14
|
R. R. Llinas, i of the vortex, The MIT Press, Cambridge, MA, 2001.
|
| |
15
|
J. Mcclelland, B. McNaughton, and R. O'Reilly, Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory, Tech. Rep. PDP.CNS.94.1, Carnegia Mellon University, Mar. 1994.
|
| |
16
|
E. Rolls and A. Treves, Neural Networks and Brain Function, Oxford University Press, New York, 1998.
|
| |
17
|
L. Squire and E. Kandel, Memory: From Mind to Molecules, Henry Holt and Company, New York, 1999.
|
| |
18
|
L. Squire and S. Zola, Structure and function of declarative and nondeclarative memory systems, Genetic Programming and Evolvable Machines, 93 (1996), pp. 13515 -- 13522.
|
| |
19
|
L. Steels, Intelligence with representation, Philosophical Transactions: Mathematical, Physical and Engineering Sciences, 361 (2003), pp. 2381--2395.
|
| |
20
|
D. Wolpert, R. C. Miall, and M. Kawato, Internal models in the cerebellum, Trends in Cognitive Sciences, 2 (1998), pp. 338--347.
|
|
Adobe Acrobat
QuickTime
Windows Media Player
Real Player
Pdf
I.2