Future Robots

Towards a robotic science of human beings

| Institute of Cognitive Sciences and Technologies, National Research Council, Rome
HardboundAvailable
ISBN 9789027204615 | EUR 105.00 | USD 158.00
 
e-Book
ISBN 9789027270085 | EUR 105.00 | USD 158.00
 
This book is for both robot builders and scientists who study human behaviour and human societies. Scientists do not only collect empirical data but they also formulate theories to explain the data. Theories of human behaviour and human societies are traditionally expressed in words but, today, with the advent of the computer they can also be expressed by constructing computer-based artefacts. If the artefacts do what human beings do, the theory/blueprint that has been used to construct the artefacts explains human behaviour and human societies. Since human beings are primarily bodies, the artefacts must be robots, and human robots must progressively reproduce all we know about human beings and their societies. And, although they are purely scientific tools, they can have one very important practical application: helping human beings to better understand the many difficult problems they face today and will face in the future - and, perhaps, to find solutions for these problems.
[Advances in Interaction Studies, 7]  2014.  xii, 489 pp.
Publishing status: Available
Table of Contents
Preface
xi–xii
1. Robots as theories of behaviour
1–32
2. Robots that have motivations and emotions
33–80
3. How robots acquire their behaviour
81–120
4. Robots that have language
121–158
5. Robots with a mental life
159–186
6. Social robots
187–220
7. Robotic families
221–258
8. Robots that learn from other robots and develop cultures and technologies
259–300
9. Robot that own things
301–338
10. Political robotics
339–360
11. Robotic economies
361–406
12. Individually different robots and robots with pathologies
407–426
13. Robots that have art, religion, philosophy, science, and history
427–450
14. Human robots are future robots
451–460
15. How human robots can be useful to human beings
461–478
References and additional readings
479–488
Index
489
“This is an inspirational book that ranges from simple evolutionary robotic simulations on navigation tasks to more challenging simulation experiments on social, political and economic issues. The book describes numerous examples from the wide and diverse work done by Parisi and his collaborators and former students at the renowned Artificial Life and Robotics group at the National Research Council in Rome. This volume sets the theoretical and technological bases for forthcoming research on future robots.”
“This is a deep, exciting, and thought-provoking exploration of our common computational future, performed by a leading scientific mind and world-class computational social science innovator.”
Cited by

Cited by other publications

Biscione, Valerio, Giancarlo Petrosino & Domenico Parisi
2015. External stores: Simulating the evolution of storing goods and its effects on human behaviour. Interaction Studies 16:1  pp. 118 ff. Crossref logo
Chanet, Corentin & David Eubelen
2019.  In Blended Cognition [Springer Series in Cognitive and Neural Systems, 12],  pp. 245 ff. Crossref logo
Damiano, Luisa & Paul Dumouchel
2018. Anthropomorphism in Human–Robot Co-evolution. Frontiers in Psychology 9 Crossref logo
Damiano, Luisa, Paul Dumouchel & Hagen Lehmann
2015. Towards Human–Robot Affective Co-evolution Overcoming Oppositions in Constructing Emotions and Empathy. International Journal of Social Robotics 7:1  pp. 7 ff. Crossref logo
Hakli, Raul & Pekka Mäkelä
2019. Moral Responsibility of Robots and Hybrid Agents. The Monist 102:2  pp. 259 ff. Crossref logo
Johnson, Deborah G. & Mario Verdicchio
2018. Why robots should not be treated like animals. Ethics and Information Technology 20:4  pp. 291 ff. Crossref logo
Lyons, Siobhan
2018.  In Death and the Machine,  pp. 1 ff. Crossref logo
Parisi, Domenico
2017.  In Robotics - Legal, Ethical and Socioeconomic Impacts, Crossref logo
Scorolli, Claudia
2019. Re-enacting the Bodily Self on Stage: Embodied Cognition Meets Psychoanalysis. Frontiers in Psychology 10 Crossref logo

This list is based on CrossRef data as of 25 june 2020. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.

References

References and additional readings

This is a list of references, grouped by topics, that provide more detailed information on some of the research described in the book. For some of the topics the list also includes a few additional references to relevant work by other authors.

Living in a “natural” environment

Parisi, D., Cecconi, F. & Nolfi, S.
(1990) Econets: Neural networks that learn in an environment. Network, 1, 149–168. Crossref link
Nolfi, S. & Parisi, D.
(1993) Self-selection of input stimuli for improving performance. In G. A. Bekey (Ed.), Neural networks and robotics (pp. 403–418). Berlin: Kluwer. Crossref link
Parisi, D.
(1994) Are neural networks necessarily passive receivers of input? In F. Masulli, P. G. Morasso & A. Schenone (Eds.), Neural networks in biomedicine (pp. 113–124). Singapore: World Scientific.
Parisi, D. & Cecconi, F.
(1995) Learning in the active mode. In F. Moràn, A. Moreno, J. J. Merelo & P. Chacòn (Eds.), Advances in artificial life. Third European Conference on Artificial Life (pp. 439–462). London: Springer.
Menczer, F. & Belew, R. K.
(1996) From complex environments to complex behaviours. Adaptive Behaviour, 4, 317–363. Crossref link
Parisi, D.
(1997) Active sampling in evolving neural networks. Human Development, 40, 320–324. Crossref link

* * *

Duchon, A. P., Kaelbling, L. P. & Warren, W. H.
(1998) Ecological robotics. Adaptive Behaviour, 6, 473–507. Crossref link
Arkin, R. C., Cervantes-Perez, F. & Weitzenfeld, A.
(1998) Ecological robotics: A schema-theoretic­ approach. In R. C. Bolles, H. Bunke & H. Noltemeier (Eds.), Intelligent robots: Sensing, modelling and planning (pp. 377–393). Singapore: World Scientific.
Ikegami, T.
(2009) Rehabilitating biology as a natural history. Adaptive Behaviour, 17, 325–328. Crossref link

The embodied and action-based nature of knowledge

Nolfi, S. & Parisi, D.
(1999) Exploiting the power of sensory-motor coordination. In D. Floreano, J-D. Nicoud, & F. Mondada (Eds.), Artificial Life 1 (pp. 173–182). London: Springer. Crossref link
Schlesinger, M., Parisi, D. & Langer, J.
(2000) Learning to reach by constraining the movement search space. Developmental Science, 3, 67–80. Crossref link
Borghi, A. M., Di Ferdinando, A., & Parisi, D.
(2002) The role of perception and action in object categorization. In J. A. Bullinaria & W. Lowe (Eds.), Connectionist models of cognition and perception (pp. 40–50). Singapore: World Scientific.
Di Ferdinando, A. & Parisi, D.
(2005) Internal representations of sensory input reflect the motor output with which organisms respond to sensory input. In A. Carsetti (Ed.), Seeing and thinking (pp. 58–63). Berlin: Kluwer.
Caligiore, D., Borghi, A. M., Parisi, D. & Baldassarre, G.
(2010) TRoPICALS: A computational embodied neuroscience model of experiments on compatibility effects. Psychological Review, 117, 1188–1228. Crossref link

* * *

Steels, L.
(1994) The artificial life roots of artificial intelligence. Artificial Life, 1, 75–110. Crossref link
Chiel, H. & Beer, R.
(1997) The brain has a body: Adaptive behaviours emerge from interactions of brain, body, and environment. Trends in Neurosciences, 20, 553–557. Crossref link
Metta, G. & Fitzpatrick, P.
(2003) Better vision through manipulation. Adaptive Behaviour, 11, 109–128. Crossref link
Chemero, A. & Turvey, M. T.
(2007) Gibsonian affordances for roboticists. Adaptive Behaviour, 15, 473–480. Crossref link
Pfeifer, R. & Bongard, J. C.
(2007) How the body shapes the way we think. Cambridge, MA: MIT Press.

Evolution and learning

Cecconi, F. & Parisi, D.
(1991) Evolving organisms that can reach for objects. In J. A. Meyer & S. W. Wilson (Eds.), From animals to animals 1 (pp. 391–399). Cambridge, MA: MIT Press.
Parisi, D., Nolfi, S. & Cecconi, F.
(1992) Learning, behaviour, and evolution. In F. Varela & 
P. Bourgine (Eds.), Toward a practice of autonomous systems (pp. 207–216). Cambridge, MA: MIT Press.
Nolfi, S., Elman, J. L. & Parisi, D.
(1994) Learning and evolution in neural networks. Adaptive Behaviour, 3, 5–28. Crossref link
Lund, H. H. & Parisi, D.
(1995) Pre-adaptation in populations of neural networks evolving in a changing environment. Artificial Life, 2, 179–197. Crossref link
Parisi, D. & Nolfi, S.
(1996) The influence of learning on evolution. In R. K. Belew & 
M. Mitchell­ (Eds.), Adaptive individuals in evolving populations (pp. 419–428). Readings, MA: Addison-Wesley.
Miglino, O., Nolfi, S. & Parisi, D.
(1996) Discontinuity in evolution: How different levels of organization imply pre-adaptation. In R. K. Belew & M. Mitchell (Eds.), Adaptive individuals in evolving populations (pp. 399–415). Readings, MA: Addison-Wesley.
Elman, J. L., Bates, E. A., Johnson, M. H., Karmiloff-Smith, A., Parisi, D. & Plunkett, K.
(1996) Rethinking innateness. A connectionist perspective on development. Cambridge, MA: MIT Press.
Nolfi, S. & Parisi, D.
(1997) Learning to adapt to changing environments in evolving neural networks. Adaptive Behaviour, 5, 75–98. Crossref link
Nolfi, S.
(2000) How learning and evolution interact: The case of a learning task which differs from the evolutionary task. Adaptive Behaviour, 7, 231–236. Crossref link
Nolfi, S. & Floreano, D.
(2000) Evolutionary robotics: The biology, intelligence, and technology of self-organizing machines. Cambridge, MA: MIT Press.
Calabretta, R., Nolfi, S., Parisi, D. & Wagner, G. P.
(2000) Duplication of modules facilitates the evolution of functional specialization. Artificial Life, 6, 69–84. Crossref link
Di Ferdinando, A., Calabretta, R., & Parisi, D.
(2001) Evolving modular architectures for neural networks. In R. French & J. Sougné (Eds.), Proceedings of the sixth neural computation and psychology workshop: Evolution, learning, and development (pp. 253–262). London: Springer.
Parisi, D.
(2003) Evolutionary psychology and Artificial Life  In S. J. Scher & F. Rauscher (Eds.), Evolutionary psychology: Alternative approaches (pp. 243–265). London: Springer. Crossref link
Calabretta, R. & Parisi, D.
(2005) Evolutionary connectionism and mind/brain modularity. In W. Callabaut & D. Rasskin-Gutman (Eds.), Modularity. Understanding the development and evolution of complex natural systems (pp. 309–330). Cambridge, MA: MIT Press.
Floreano, D., Dürr, P. & Mattiussi, C.
(2008) Neuroevolution: from architectures to learning. Evolutionary Intelligence, 1, 47–62. Crossref link

* * *

Ackley, D. H. & Littman, M. L.
(1992) Interactions between learning and evolution. In Langton, C., Farmer, J., Rasmussen, S. & Taylor, C. (Eds.), Artificial Life 2 (pp. 487–510). Redwood City, CA: Addison-Wesley.
Cliff, D., Husbands, P. & Harvey, I.
(1993) Explorations in evolutionary robotics. Adaptive Behaviour, 2, 73–110. Crossref link
Harvey, I., Di Paolo, E., Wood, R., Quinn, M. & Tuci, E.
(2004) Evolutionary robotics: A new scientific tool to study cognition. Artificial Life, 11, 79–98. Crossref link

Development

Cangelosi, A., Parisi, D. & Nolfi, S.
(1994) Cell division and migration in a ‘genotype’ for neural networks. Network, 5, 497–515. Crossref link
Nolfi, S., Miglino, O. & Parisi, D.
(1994) Phenotypic plasticity in evolving neural networks. In D. P. Gaussier & J-D. Nicoud (Eds.), From perception to action (146–157). Los Alamitos, CA: IEEE Computer Society Press.
Nolfi, S. & Parisi, D.
(1995) Evolving artificial neural networks that develop in time. In F. Moran, A. Moreno, J. J. Merelo, & P. Chacòn (Eds.), Advances in Artificial Life. Proceedings of the third European conference on Artificial Life (pp. 353–367). London: Springer.
Parisi, D.
(1996) Computational models of developmental mechanisms. In R. Gelman & T. K. Au (Eds.), Perceptual and cognitive development (pp. 373–412). San Diego, CA: Academic Press.
Parisi, D. & Nolfi, S.
(2001) Development in neural networks. In J. P. Mukesh, V. Honavar & 
K. Balakrishan (Eds.), Advances in evolutionary synthesis of neural networks (pp. 215–246). Cambridge, MA: MIT Press.
Schlesinger, M. & Parisi, D.
(2001) The agent-based approach: A new direction for computational models of development. Developmental Review, 21, 121–146. Crossref link
Parisi, D. & Schlesinger, M.
(2002) Artificial Life and Piaget. Cognitive Development, 17, 1301–1321. Crossref link
Cangelosi, A., Nolfi, S. & Parisi, D.
(2003) Artificial life models of neural development. In Kumar, S. & Bentley, P. J. (Eds.), On Growth, form, and computers (pp. 339–354). San Diego, CA: Academic Press.
Schlesinger, M.
(2003) A lesson from robotics: Modeling infants as autonomous agents. Adaptive Behavior, 11, 97–107. Crossref link
(2004) Evolving agents as a metaphor for the developing child. Developmental Science, 7, 158–164.
Schlesinger, M., & McMurray, B.
(2012) The past, present, and future of computational models of cognitive development. Cognitive Development, 27, 326–348. Crossref link
Cangelosi, A. & Schlesinger, M.
(2014) Developmental robotics: From babies to robots. ­Cambridge, MA: MIT Press.

Motivations and emotions

Cecconi, F. & Parisi, D.
(1992) Neural networks with motivational units. In From Animals to Animats 2 (pp. 167–181). Cambridge, MA: MIT Press.
Parisi, D.
(1996) Motivation in artificial organisms. In G. Tascini, F. Esposito, V. Roberto & 
P. Zingaretti (Eds.), Machine learning and perception (pp. 3–19). Singapore: World Scientific.
Mirolli, M. & Parisi, D.
(2003) Artificial organisms that sleep. In W. Banzhaf, T. Christaller, 
P. Dittrich, J. T. Kim, & J. Ziegler (Eds.), Proceedings of the seventh European conference on Artificial Life (pp. 377–386). London: Springer.
Parisi, D.
(2004) Internal robotics. Connection Science, 16, 325–338. Crossref link
Ruini, F., Petrosino, G., Saglimbeni, F., & Parisi, D.
(2010) The strategic level and the tactical level of behaviour. In Gray, J. & Nefti-Meziani, S. (Eds.), Advances in Cognitive Systems (pp.  271–299). Herts, UK: IET Publisher. Crossref link
Parisi, D. & Petrosino, G.
(2010) Robots that have emotions. Adaptive Behaviour, 18, 453–469. Crossref link
Saglimbeni, F., & Parisi, D.
(2011) Input from the external environment and input from within the body. In G. Kampis, I. Karsai, & E. Szathmáry (Eds.), Advances in Artificial Life. Darwin meets von Neumann (pp. 148–155). London: Springer. Crossref link
Parisi, D.
(2011) The other half of the embodied mind. Frontiers in Psychology, 69, 1–8.
Petrosino, G., Parisi, D. & Nolfi, S.
(2013) Selective attention enables action selection: Evidence from evolutionary robotics experiments. Adaptive Behaviour, 21, 356–370. Crossref link

* * *

Arbib, M. A. & Fellous, J. M.
(2004) Emotions: From brain to robot. Trends in Cognitive Sciences, 8, 554–561. Crossref link
Ziemke, T.
(2008) The role of emotions in biological and robotic autonomy. Biosystems, 91, 401–408. Crossref link
Cos, I., Canamero, L. & Hayes, G.
(2013) Learning affordances of consummatory behaviours: motivation-driven adaptation for motivated agents. Adapative Behaviour, 18, 285–314. Crossref link
Cos, I., Canamero, L., Hayes, G. & Gillies, A.
(2013) Hedonic value: enhancing adaptation for motivated agents. Adaptive Behaviour, 21, 465–483. Crossref link

Language

Parisi, D.
(1997) An artificial life approach to language. Brain and Language, 59, 121–146. Crossref link
Cangelosi, A. & Parisi, D.
(1998) The emergence of a ‘language’ in an evolving population of neural networks. Connection Science, 10, 83–97. Crossref link
Parisi, D. & Cangelosi, A.
(2002) A unified simulation scenario for language development, evolution, and historical change. In A. Cangelosi & D. Parisi (Eds.), Simulating the evolution of language (pp. 255–275). London: Springer. Crossref link
Cangelosi, A., & Parisi, D.
(2004) The processing of verbs and nouns in neural networks: 
Insights from synthetic brain imaging. Brain and Language, 2, 401–408. Crossref link
Mirolli, M. & Parisi, D.
(2004) Language, altruism, and docility: How cultural learning can favour language evolution. In J. B. Pollack, M. Bedau, P. Husbands, T. Ikegami & R. A. Watson (Eds.), Artificial Life 9 (pp. 182–187). Cambridge, MA: MIT Press.
(2005) How can we explain the emergence of a language that benefits the hearer but not the speaker? Connection Science, 17, 307–324. Crossref link
(2006) Talking to oneself as a selective pressure for the emergence of language. In A. Cangelosi, A. D. M. Smith & K. Smith (Eds.), Proceedings of the sixth international conference on the evolution of language (pp. 182–187). Singapore: World Scientific.
Mirolli, M., Cecconi, F., & Parisi, D.
(2007) A neural network model for explaining the asymmetries between linguistic production and linguistic comprehension. In S. Vosniadou, 
D. Kayser, & A. Protopapas (Eds.), Proceedings of the European Cognitive Science Conference 2007 (pp. 670–675). Hillsdale, NJ: Erlbaum.
Floreano, D., Mitri, S., Magnenat, S. & Keller, L.
(2007) Evolutionary conditions for the emergence of communication in robots. Current Biology, 17, 514–519. Crossref link
Mirolli, M. & Parisi, D.
(2008) How producer bias can favour the evolution of communication: an analysis of evolutionary dynamics. Adaptive Behaviour, 16, 27–52. Crossref link
Uno, R., Marocco, D., Nolfi, S. & Ikegami, T.
(2011) Emergence of proto-sentences in artificial communicating systems. IEEE Transactions on Autonomous Mental Development, 3, 146–153. Crossref link

* * *

Steels, L.
(2011) Modeling the cultural evolution of language. Physics of Life Reviews, 8, 339–356. Crossref link
Steels, L., & Loetzsch, M.
(2012) The Grounded Naming Game. In L. Steels (Ed.), Experiments in cultural language evolution. Amsterdam: John Benjamins. Crossref link
Yuruten, O., Sahin, E. & Kalkan, S..
(2013) The learning of adjectives and nouns from affordances and appearance features. Adaptive Behaviour, 21, 437–451. Crossref link

The influence of language on the representation of the world in the human mind

Cangelosi, A. & Harnad, S.
(2000) The adaptive advantage of symbolic theft over sensorimotor toil: grounding language in perceptual categories. Evolution of Communication, 4, 117–142. Crossref link
Cangelosi, A. & Parisi, D.
(2001) How nouns and verbs differentially affect the behavior of artificial organisms. In J. D. Moore & K. Stenning (Eds.), Proceedings of the 23rd annual conference of the Cognitive Science Society (pp. 170–175). Hillsdale, NJ: Erlbaum.
Mirolli, M. & Parisi, D.
(2005) Language as an aid to categorization: A neural network model of early language acquisition. In A. Cangelosi, G. Bugmann & R. Borisyuk (Eds.), Modelling language, cognition and action. Proceedings of the ninth neural computation and psychology workshop (pp. 97–106). Singapore: World Scientific.
(2009) Language as a cognitive tool. Minds and Machines, 19, 517–528. Crossref link
Massera, G., Tuci, E., Ferrauto, T. & Nolfi, S.
(2010) The facilitatory role of linguistic instructions on developing manipulation skills. IEEE Computational Intelligence Magazine, 5, 33–42. Crossref link
Mirolli, M. & Parisi, D.
(2011) Towards a Vygotskian cognitive robotics: The role of language as a cognitive tool. New Ideas in Psychology, 29, 298–311. Crossref link

Mental life

Cecconi, F. & Parisi, D.
(1990) Learning to predict the consequences of one’s own actions. In R. Eckmiller, G. Hartmann & G. Hauske (Eds.), Parallel processing in neural systems and computers (pp. 237–240). Amsterdam: Elsevier.
Caligiore, D., Tria, M., & Parisi, D.
(2006) Some adaptive advantages of the ability to make predictions. In From Animals to Animats 9 (pp. 17–28). London: Springer. Crossref link
Parisi, D.
(2007) Mental robotics. In Chella, A. & Manzotti, R. (Eds.) Artificial Consciousness (pp. 191–211). Exeter, UK: Imprint-Academic.

Sociality

Cecconi, F., Denaro, D., Parisi, D. & Piazzalunga, U.
(1994) Social aggregations in evolving neural networks. In C. Castelfranchi & E. Werner (Eds.), Artificial social systems (pp. 41–54). London: Springer. Crossref link
Baldassarre, G., Nolfi, S. & Parisi, D.
(2003) Evolving mobile robots able to display collective behavior. Artificial Life, 9, 255–267. Crossref link
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D.
(2004) Defining and identifying communities in networks. Proceedings of the National Academy of Science, 101, 2658–2663. Crossref link
Castellano, C., Cecconi, C., Loreto, V., Parisi, D. & Radicchi, F.
(2004) Self-contained algorithms to detect communities in networks. Eur. Phys. J. B., 38, 311–319. Crossref link
Parisi, D. & Nolfi, S.
(2006) Sociality in embodied neural agents. In R. Sun (Ed.), Cognition and multi-agent interaction: From cognitive modeling to social simulation (pp. 328–354). ­Cambridge: Cambridge University Press.
Baldassarre, G., Parisi, D. & Nolfi, S.
(2006) Distributed coordination of simulated robots based on self-organisation. Artificial Life, 12, 289–311. Crossref link
Mitri, S., Wischmann, S., Floreano, D. & Keller, L.
(2012) Using robots to understand social behavior. Biological Reviews, 88, 31–39. Crossref link
Nolfi, S.
(2012) Co-evolving predator and prey robots. Adaptive Behavior, 20, 10–15. Crossref link
Lettieri, N. & Parisi, D.
(2013) Neminem laedere: An evolutionary agent-based model of the interplay between punishment and damaging behaviour. Artificial Intelligence and Law, 21, 425–453. Crossref link

* * *

Bonabeau, E., Dorigo, M. & Theraulaz, G.
(Eds) (1999) Swarm intelligence: From natural to artificial systems. Oxford: Oxford University Press.
Fong, T., Noubakhsh, I. & Dautenhahn, K.
(2003) A review of socially interactive robots. Robotics and Autonomous Systems, 42, 143–166. Crossref link
Epstein, J. K. & Axtell, R.
(2006)  Generative social science . Social science from the bottom up. Princeton, NJ: Princeton University Press.

Families

Menczer, F. & Parisi, D.
(1992) A model for the emergence of sex in evolving networks: Adaptive advantage or random drift? In F. Varela & P. Bourgine (Eds.), Towards a practice of autonomous systems (pp. 337–345). Cambridge, MA: MIT Press.
Parisi, D., Cecconi, F. & Cerini, A.
(1995) Kin-directed altruism and attachment behaviour in an evolving population of neural networks. In N. Gilbert, & R. Conte (Eds.) Artificial societies: The computational simulation of social life (pp. 238–251). London: UCL Press.
Pedone, R. & Parisi, D.
(1997) In what kinds of social groups can “altruistic” behaviors evolve? In R. Conte, R. Hegselmann, & P. Terna (Eds.), Simulating social phenomena (pp. 195–201). London: Springer. Crossref link
Floreano, D., Mitri, S., Perez-Uribe, A. & Keller, L.
(2008) Evolution of altruistic robots. Computational Intelligence: Research Frontiers, 232–248,LNCS 5050.
Mitri, S., Floreano, D. & Keller, L.
(2011) Relatedness influences signal reliability in evolving robots. Proceedings of the Royal Society B, 278, 378–383. Crossref link
Da Rold, F., Petrosino, G., & Parisi D.
(2011) Male and female robots. Adaptive Behaviour, 19, 317–334. Crossref link

* * *

Todd, P. M. & Miller, G. F.
(1993) Parental guidance suggested: How parental imprinting evolves through sexual selection as an adaptive learning mechanism. Adaptive Behaviour, 2, 5–47. Crossref link

Culture

Cecconi, F., Menczer, F. & Belew, R.
(1995) Maturation and evolution of imitative learning in artificial organisms. Adaptive Behavior, 4, 29–50. Crossref link
Denaro, D. & Parisi, D.
(1996) Cultural evolution in a population of neural networks. In 
M. Marinaro & R. Tagliaferri (Eds.), Neural nets (pp. 100–111). London: Springer.
Parisi, D.
(1997) Cultural evolution in neural networks. IEEE Expert, 12, 9–11. Crossref link
Ugolini, M. & Parisi, D.
(1999) Simulating the evolution of artifacts. In D. Floreano, J.-D. Nicoud, & F. Mondada (Eds.), Advances in Artificial Life (pp. 489–498). London: Springer. Crossref link
Parisi, D., & Ugolini, M.
(2002) Living in enclaves. Complexity, 7, 21–27. Crossref link
Parisi, D., Cecconi, F., & Natale, F.
(2003) Cultural change in spatial environments: The role of cultural assimilation and internal changes in cultures. The Journal of Conflict Resolution, 47, 163–179. Crossref link
Acerbi, A. & Parisi, D.
(2006) Cultural transmission between and within generations. Journal of Artificial Societies and Social Systems, 9 (1).
Cecconi, F., Antinucci, F., Parisi, D., & Natale, F.
(2006) Simulating the expansion of farming and the differentiation of European languages. In B. Laks & D. Simeoni (Eds.), Origins and Evolution of Language (pp. 234–258). Oxford: Oxford University Press.
Acerbi, A., Ghirlanda, S. & Enquist, M.
(2014) Regulatory traits: Cultural influences on cultural evolution. In S. Cagnoni, M. Mirolli & M. Villani (Eds.), Evolution, complexity, and Artificial Life (pp. 135–148). London: Springer. Crossref link

* * *

Curran, D. & O’Riordan, C.
(2006) Increasing population diversity through cultural learning. Adaptive Behaviour, 14, 315–338. Crossref link
Nehaniv, C. L. & Dautenhahn, K.
(2007) Imitation and social learning in robots, humans, and animals. Cambridge: Cambridge University Press. Crossref link

Economic and political life

Parisi, D.
(1997) What to do with a surplus. In R. Conte, R. Hegselmann, & P. Terna (Eds.), Simulating social phenomena (pp. 133–151). London: Springer. Crossref link
Cecconi, F. & Parisi, D.
(1998) Individual versus social survival strategies. Journal of Artificial Societies and Social Simulation, 1(2).
Parisi, D.
(1998) A cellular automata model of the expansion of the Assyrian empire. In 
S. Bandini­, R. Serra & F. S. Liverani (Eds.), Cellular Automata (pp. 194–200). London: Springer.
Delre, S. A. & Parisi, D.
(2007) Information and cooperation in a simulated labour market: 
A computational model of the evolution of workers and firms. In M. Salzano & D. Colander (Eds.), Complexity hints for economic policy (pp. 181–200). London: Springer.
Gigliotta, O., Miglino, O., & Parisi, D.
(2007) Groups of agents with a leader. Journal of Artificial Societies and Social Simulation, 10, 1–10.
Cecconi, F., di Gennaro, F., Parisi, D. & Schiappelli, A.
(in press) Simulating the emergence of proto-urban centres in Ancient Southern Etruria. In J. A. Barcelò (Ed.), Mathematics and Archaeology. Enfield, NH: Science Publishers.
Subjects

Consciousness Research

Consciousness research
BIC Subject: UYQ – Artificial intelligence
BISAC Subject: COM004000 – COMPUTERS / Intelligence (AI) & Semantics
U.S. Library of Congress Control Number:  2014008326