Domenico Parisi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Domenico Parisi has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Stefano Nolfi
Institute of Cognitive Sciences and Technologies National Research Council (CNR-ISTC),Rome,Italy
2 shared publications - Angelo Cangelosi
The University of Manchester
1 shared publication - Gianluca Baldassarre
Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
1 shared publication - Massimiliano Ugolini
Department of Communication, University of Siena, Siena, Italy
1 shared publication
Latest Publications
- 1 July 2003
- Published by MIT Press in Artificial Life
- Vol. 9 (3) , 255-267
Evolving Mobile Robots Able to Display Collective Behaviors
We present a set of experiments in which simulated robots are evolved for the ability to aggregate and move together toward a light target. By developing and using quantitative indexes that capture the structural properties of the emerged formations, we show that evolved individuals display interesting behavioral patterns in which groups of robots act as a single unit. Moreover, evolved groups of robots with identical controllers display primitive forms of situated specialization and play different behavioral functions within the group according to the circumstances. Overall, the results presented in the article demonstrate that evolutionary techniques, by exploiting the self-organizing behavioral properties that emerge from the interactions between the robots and between the robots and the environment, are a powerful method for synthesizing collective behavior.
- 1 July 2003
- Published by MIT Press in Artificial Life
- Vol. 9 (3) , 255-267
Evolving Mobile Robots Able to Display Collective Behaviors
We present a set of experiments in which simulated robots are evolved for the ability to aggregate and move together toward a light target. By developing and using quantitative indexes that capture the structural properties of the emerged formations, we show that evolved individuals display interesting behavioral patterns in which groups of robots act as a single unit. Moreover, evolved groups of robots with identical controllers display primitive forms of situated specialization and play different behavioral functions within the group according to the circumstances. Overall, the results presented in the article demonstrate that evolutionary techniques, by exploiting the self-organizing behavioral properties that emerge from the interactions between the robots and between the robots and the environment, are a powerful method for synthesizing collective behavior.
- 1 September 2001
- Published by Wiley in Complexity
- Vol. 7 (1) , 21-27
Living in Enclaves
What are the consequences of living in small isolated communities vs living in larger environments as members of bigger communities? If the resources are scarce and highly spatially and temporally variable, living in isolated communities can lead to extinction because of temporary food scarcity and less efficient genetic selection. Artifacts that increase the energy extracted from natural resources can avoid extinction of local populations, but still choosing the artifacts to be reproduced from the restricted artifact pool of the local community rather than from a larger pool leads to less good artifacts and smaller global population size.
- 1 September 2001
- Published by Wiley in Complexity
- Vol. 7 (1) , 21-27
Living in Enclaves
What are the consequences of living in small isolated communities vs living in larger environments as members of bigger communities? If the resources are scarce and highly spatially and temporally variable, living in isolated communities can lead to extinction because of temporary food scarcity and less efficient genetic selection. Artifacts that increase the energy extracted from natural resources can avoid extinction of local populations, but still choosing the artifacts to be reproduced from the restricted artifact pool of the local community rather than from a larger pool leads to less good artifacts and smaller global population size.
- 1 November 1994
- Published by Taylor & Francis in Network: Computation in Neural Systems
- Vol. 5 (4) , 497-515
Cell division and migration in a ‘genotype’ for neural networks
Much research has recently been dedicated to applying genetic algorithms to populations of neural networks. However. While in real organisms the inherited genotype maps in complex ways into the resulting phenotype, in most of this research the development process that creates the individual phenotype is ignored. In this paper we present a model of neural development which includes cell division and cell migration in addition to axonal growth and branching. This reflects, in a very simplified way, what happens in the ontogeny of real organisms. The development process of our artificial organisms shows successive phases of functional differentiation and specialization. In addition, we find that mutations that affect different phases of development have very different evolutionary consequences. A single change in the early stages of cell division and migration can have huge effects on the phenotype, while changes in later stages usually have a less dramatic impact. Sometimes, changes that affect the first developmental stages may be retained, producing sudden changes in evolutionary history.
- 1 November 1994
- Published by Taylor & Francis in Network: Computation in Neural Systems
- Vol. 5 (4) , 497-515
Cell division and migration in a ‘genotype’ for neural networks
Much research has recently been dedicated to applying genetic algorithms to populations of neural networks. However. While in real organisms the inherited genotype maps in complex ways into the resulting phenotype, in most of this research the development process that creates the individual phenotype is ignored. In this paper we present a model of neural development which includes cell division and cell migration in addition to axonal growth and branching. This reflects, in a very simplified way, what happens in the ontogeny of real organisms. The development process of our artificial organisms shows successive phases of functional differentiation and specialization. In addition, we find that mutations that affect different phases of development have very different evolutionary consequences. A single change in the early stages of cell division and migration can have huge effects on the phenotype, while changes in later stages usually have a less dramatic impact. Sometimes, changes that affect the first developmental stages may be retained, producing sudden changes in evolutionary history.