2011
DOI: 10.1007/978-3-642-21283-3_19
|View full text |Cite
|
Sign up to set email alerts
|

Input from the External Environment and Input from within the Body

Abstract: Behaviour responds to both input from the external environment and input from within the organism's body. Input from the external environment has mainly the function to regulate the execution of the organism's activities while input from the body is used to decide which activity to execute. We evolve artificial organisms which to survive and reproduce have to both eat food and drink water in equivalent quantities and therefore at any given time they have to decide whether to look for food or water. We show tha… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
4
0

Year Published

2012
2012
2015
2015

Publication Types

Select...
3
3
1

Relationship

3
4

Authors

Journals

citations
Cited by 30 publications
(4 citation statements)
references
References 14 publications
0
4
0
Order By: Relevance
“…Cognitive robotics work has investigated the effects of environmental changes (e.g. Lowe 2007;Saglimbeni and Parisi 2009), synthetic hormone modulation of action selection and behaviour (e.g. Avila-Garcìa 2004; Avila-Garcìa and Cañamero 2005; Moioli, Vargas, Von Zuben and Husbands 2008) and other neuromodulatory effects (cf.…”
Section: Using Cognitive Robotics To Extend the Igt Benchmarkmentioning
confidence: 99%
“…Cognitive robotics work has investigated the effects of environmental changes (e.g. Lowe 2007;Saglimbeni and Parisi 2009), synthetic hormone modulation of action selection and behaviour (e.g. Avila-Garcìa 2004; Avila-Garcìa and Cañamero 2005; Moioli, Vargas, Von Zuben and Husbands 2008) and other neuromodulatory effects (cf.…”
Section: Using Cognitive Robotics To Extend the Igt Benchmarkmentioning
confidence: 99%
“…Moreover, these assumptions refer to ancestral environments, not present-day ecologies: while there are methods to acquire data on living conditions in ancestral times (e.g., through paleobiology and primate archeology; Haslam et al, 2009 ), they are bound to deliver incomplete information at best, in spite of substantial research efforts. Recent work has demonstrated the viability and fruitfulness of computational methods, e.g., experimental evolutionary robotics: the basic idea is to let populations of simulated robots evolve under specific ecological pressures, and then observe their behavior with the aim of drawing implications for the understanding of processes in natural organisms faced by similar, uncertainty-based tasks (Da Rold et al, 2011 ; Saglimbeni and Parisi, 2011 ). This approach allows to observe how several forms of risk introduced in the evolutionary environment affect choice behavior, both in ecology and in experimental settings.…”
Section: Risk Attitudes Environmental Uncertainty and Addictive Behamentioning
confidence: 99%
“…Thus, integrating evolutionary simulations with naturalistic studies has the potential for huge scientific payoffs. With respect to experimental evolutionary robotics (Da Rold et al, 2011 ; Saglimbeni and Parisi, 2011 ), advantages of this method include the following ones. First, full observability means that robots’ behavior can be observed in extreme detail both “in the wild” (i.e., in the ecological setting where robots evolve), and “in the lab” (i.e., under specific test conditions).…”
Section: Risk Attitudes Environmental Uncertainty and Addictive Behamentioning
confidence: 99%
“…The basic idea is to evolve populations of simulated robots under specific ecological pressures, 1 and then observe their behavior under laboratory conditions, with the aim of drawing interesting implications for our understanding of natural organisms faced by similar tasks. Let us label this methodology experimental evolutionary robotics, to stress the fact that these robots are studied not only in the ecology where they evolve, but also under artificial laboratory conditions (for early examples of this method, see Da Rold et al, 2011;Saglimbeni and Parisi, 2011;Paglieri et al, 2014; for a general discussion see Parisi, 2014). 2 1 This process of evolution is achieved through evolutionary algorithms: simply put, the idea is to apply to artificial organisms some key mechanisms of natural evolution (e.g., fitness, selection, random mutation) to generate and identify effective behavioral and/or morphological solutions to problems posed by a certain environment.…”
Section: Introductionmentioning
confidence: 99%