2016
DOI: 10.1098/rstb.2015.0447
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Abstract: One contribution of 13 to a theme issue 'The major synthetic evolutionary transitions'.Subject Areas: computational biology, evolution Keywords: language evolution, grammaticalization, emergence of grammar, fluid construction grammar, agent-based models, major transitions in evolution Author for correspondence: Luc Steels e-mail: steels@arti.vub.ac.be Agent-based models for the emergence and evolution of grammar Luc Steels ICREA, IBE-Universitat Pompeu Fabra and CSIC, 08003 Barcelona, Spain LS, 0000-0001-9134-… Show more

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Cited by 28 publications
(17 citation statements)
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“…A similarly strong focus on words referring to concrete physical objects, processes, actions, and object properties such as color or size is prevalent in language-oriented research in developmental robotics [8]- [25] and agent-based approaches in evolutionary linguistics [26]- [29]. There, symbol grounding [30], the linking of symbols with data or concepts derived from the robot's own embodiment, is employed for robots to "make sense" of these linguistic entities.…”
Section: Introductionmentioning
confidence: 99%
“…A similarly strong focus on words referring to concrete physical objects, processes, actions, and object properties such as color or size is prevalent in language-oriented research in developmental robotics [8]- [25] and agent-based approaches in evolutionary linguistics [26]- [29]. There, symbol grounding [30], the linking of symbols with data or concepts derived from the robot's own embodiment, is employed for robots to "make sense" of these linguistic entities.…”
Section: Introductionmentioning
confidence: 99%
“….) all the way to simulation studies (Baxter et al, 2009;Beuls & Steels, 2013;Blythe & Croft, 2012;Landsbergen et al, 2010;Lestrade, 2015;Nowak et al, 2001;Pijpops et al, 2015;Steels, 2016;Van Trijp, 2013), the main workhorse today is generalized linear (mixed)models, with time as an explanatory variable, often under multivariate control or in interaction with other covariates (see, a.o., De Cuypere, 2015;Fonteyn & Van de Pol, 2016;Gries & Hilpert, 2010;Petré & Van de Velde, 2018;Rosemeyer, 2016Rosemeyer, , 2014Wolk et al, 2013;Zimmermann, 2017). This may seem reasonable, as regressing competing variants or frequency metrics on time can accommodate the typical S-curve that is observed in many cases of diachronic change (Blythe & Croft, 2012;Denison, 2003;Feltgen et al, 2017;Kroch, 1989;Pintzuk, 2003;Weinreich et al, 1968, p. 113).…”
Section: Inferential Statistics With the Variable Timementioning
confidence: 99%
“…At any point, the extra steps required get to the new preys new location are given by the distance between the predator and prey over the predator speed, k. As such, the a + 1th iteration results in a step number of Hence, summing for all a ∈ ℕ , by the induction hypothesis the total number of steps required is the geometric series , and hence, the total number of extra steps required is as required in (11). ◻ Therefore, from (7), (8), and (11), probability of capture once the predator is at a scent marker is given by Using a Monté Carlo algorithm [9] , a pseudorandom number, p, is generated such that p ∈ successful hunt is observed. As with simulation 1, this was repeated 10 4 times.…”
Section: Proposition 1 the Total Number Of Extra Steps Required For Cmentioning
confidence: 99%
“…These techniques have then been used in areas such as population dynamics, for example 'Aphid Population Dynamics of Agricultural Landscapes: An Agent-Based Simulation Model' [7]. More recently, agent-based modelling has been used to describe the emergence of the evolution of grammar by Luc Steels [8].…”
Section: Introductionmentioning
confidence: 99%