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Erschienen in:
Generation of Search Behavior by a Modification of Q-MDP Value Method Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Bio-inspired Classification in the Architecture of Situated Agents

verfasst von : G. Gini, A. M. Franchi, F. Ferrini, F. Gallo, F. Mutti, R. Manzotti

Erschienen in: Intelligent Autonomous Systems 13

Verlag: Springer International Publishing

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Abstract

Cognitive development concerns the evolution of human mental capabilities through experience earned during life. Important features needed to accomplish this target are the self-generation of motivations and goals as well as the development of complex behaviors consistent with these goals. Our target is to build such a bio-inspired cognitive architecture for situated agents, capable of integrating new sensing data from any source. Based on neuroscience assessed concepts, as neural plasticity and neural coding, we show how a categorization module built on cascading classifiers is able to interpret different sensing data. Moreover, we see how to give a biological interpretation to our classification model using the winner-take-all paradigm.

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Vorheriges Kapitel Leveraging Area Bounds Information for Autonomous Multirobot Exploration
Nächstes Kapitel Adaptive Multiagent Path Planning with Dynamic Heuristic
Fußnoten
1
In questa parte ci sono due/tre frasi da rivedere; non ho capito bene le correzioni!
 
Literatur
1.
R. Manzotti, F. Mutti, S. Y. Lee and G. Gini, “A model of a middle level of cognition based on the interaction among the thalamus, amygdala, and the cortex.” IEEE International Conference on Systems, Man, and Cybernetics, pp. 1996–2001, November 2012.
2.
M. Lungarella, G. Metta, R. Pfeiffer and G. Sandini, “Developmental robotics: a survey,” Connection Science, vol. 4, no. 15, pp. 151–190, 2003.
3.
R. Manzotti and V. Tagliasco, “From “behaviour-based” robots to “motivations-based” robots”, Robotics and Autonomous Systems, vol. 2, no. 51, pp. 175–190, 2005.
4.
J. Sharma, A. Angelucci and M. Sur, “Induction of visual orientation modules,” Nature, vol. 404, pp. 841–847, 2000.
5.
S. M. Sherman and R. Guillery, Exploring the Thalamus, Elsevier, 2000.
6.
S. Duncan and L. F. Barret, “The role of the amygdala in visual awareness,” Trends in cognitive science, vol. 11, no. 5, pp. 190–192, 2008.
7.
F. Mussa-Ivaldi and E. Bizzi, “Motor learning through the combination of primitives,” Philosophical transactions of the Royal Society, vol. 355, no. 1404, pp. 1755–1769, 2000.
8.
R. Jackendoff, Consciousness and the computational mind, MIT Press, 1987.
9.
E. Rosh, “Principles of categorization,” Cognition and categorization, pp. 27–48, 1978.
10.
B. Olshausen A. and D. J. Field, “Sparse coding of sensory inputs,” Current Opinion in Neurobiology, vol. 14, pp. 481–487, 2004.
11.
P. Viola and M. Jones, “Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade,” Advances in Neural Information Processing System, vol. 14, pp. 1311–1318, 2001.
12.
T. D. Albright, E. R. Kandel and M. I. Posner, “Cognitive neuroscience,” Current Opinion in Neurobiology, vol. 10, pp. 612–624, 2000.
13.
P. Cisek and J. F. Kalaska, “Neural mechanisms for interacting with a world full of action choices,” Annual Review of Neuroscience, vol. 33, pp. 269–298, 2010.
14.
E. R. Kandel, J. H. Schwartz and T. M. Jessell, Principles of neural science, McGraw-Hill, 2000.
15.
F. Mutti and G. Gini, “Bio-inspired disparity estimation system from energy neurons,” in International Conference on Applied Bionics and Biomechanics ICABB-2010, Venice, 2010.
16.
F. Mutti, H. Marques and G. Gini, “A model of the visual dorsal pathway for computing coordinate transformations: an unsupervised approach,” in Advances in Intelligent Systems and Computing, Springer, 2013, pp. 239–246.
17.
E. Schneidman, W. Bialek and M. J. Berry, “Synergy, Redundancy, and Independence in population codes,” The Journal of Neuroscience, 2003.
18.
S. Denève, P. Latham and A. Pouget, “Efficient computation and cue,” Nature Neuroscience, vol. 4, no. 8, pp. 826–831, 2001.
19.
E. Salinas and L. Abbott, “Coordinate transformations in the visual system: how to generate gain fields and what to compute with them,” Progress in Brain Research, no. 130, pp. 175–190, 2001.
20.
M. Carandini and D. J. Heeger, “Normalization as a canonical neural computation,” Nature Reviews Neuroscience, no. 13, pp. 51–62, 2013.
21.
A. Hyvärinen and E. Oja, “Independent component analysis: Algorithms and applications,” Neural Networks, vol. 13, no. 4–5, p. 411–430, 2000.
22.
E. A. Murray and S. P. Wise, “Interactions between orbital prefrontal cortex and amygdala:advanced cognition, learned responses and instinctive behaviors,” Current opinion in Neurobiology, vol. 20, pp. 212–220, 2010.
23.
D. J. Freedman, M. Riesenhuber, T. Poggio and E. K. Miller, “Categorical Representation of Visual Stimuli in the Primate Prefrontal Cortex,” Science, vol. 291, no. 5502, pp. 312–316, 2001.
24.
D. M. Tax and R. P. Duin, “Combining One-Class Classifier,” in Multiple Classifier Systems, 2001, pp. 299–308.
25.
R. Rifkin and A. Klautau, “In difense of One-Vs-All Classification,” Journal of Machine Learning Research, vol. 5, pp. 101–141, 2004.
26.
C. D. Salzman and W. T. Newsome, “Neural mechanisms for forming a perceptual decision,” Science, vol. 5156, no. 264, pp. 231–237, 1994.
27.
T. Powell and G. Paynter, “Going Grey? Comparing the OCR Accuracy Levels of Bitonal and Greyscale Images,” D-Lib Magazine, vol. 15, no. 3–4, 2009.
28.
W. Chaney, Dynamic Mind, Houghton-Brace Publishing, 2007.
29.
J. M. Baker, L. Deng, J. Glass, S. Khudanpur, C.-H. Lee, N. Morgan and D. O’Shaughnessy, “Research Developments and Directions in Speech Recognition and Understanding,” Ieee Signal processing magazine, vol. 26, no. 4, pp. 78–85, 2009.
Metadaten
Titel
Bio-inspired Classification in the Architecture of Situated Agents
verfasst von
G. Gini
A. M. Franchi
F. Ferrini
F. Gallo
F. Mutti
R. Manzotti
Copyright-Jahr
2016
Buch
Intelligent Autonomous Systems 13

Print ISBN: 978-3-319-08337-7

Electronic ISBN: 978-3-319-08338-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-08338-4_43