Abstract
| Language | English |
|---|---|
| Pages | 131-168 |
| Number of pages | 38 |
| Journal | Biologically Inspired Cognitive Architectures |
| Volume | 16 |
| DOIs | |
| Publication status | Published - 1 Apr 2016 |
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Keywords
- Causal
- Dynamic
- Modeling
- State-determined system
- Temporal
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Dynamic Modeling Based on a Temporal-Causal Network Modeling Approach. / Treur, J.
In: Biologically Inspired Cognitive Architectures, Vol. 16, 01.04.2016, p. 131-168.Research output: Contribution to Journal › Article › Academic › peer-review
TY - JOUR
T1 - Dynamic Modeling Based on a Temporal-Causal Network Modeling Approach
AU - Treur, J.
PY - 2016/4/1
Y1 - 2016/4/1
N2 - This paper presents a dynamic modelling approach that enables to design complex high level conceptual representations of models in the form of causal-temporal networks, which can be automatically transformed into executable numerical model representations. Dedicated software is available to support designing models in a graphical manner, and automatically transforming them into an executable format and performing simulation experiments. The temporal-causal network modelling format used makes it easy to take into account theories and findings about complex brain processes known from Cognitive, Affective and Social Neuroscience, which, for example, often involve dynamics based on interrelating cycles. This enables to address complex phenomena such as the integration of emotions within all kinds of cognitive processes, and of internal simulation and mirroring of mental processes of others. In this paper also the applicability has been discussed in general terms, showing for example that every process that can be modelled by first-order differential equations, also can be modeled by the presented temporal-causal network modeling apporoach. A variety of example models that can be found in other papers illustrate the applicability of the approach in more detail.
AB - This paper presents a dynamic modelling approach that enables to design complex high level conceptual representations of models in the form of causal-temporal networks, which can be automatically transformed into executable numerical model representations. Dedicated software is available to support designing models in a graphical manner, and automatically transforming them into an executable format and performing simulation experiments. The temporal-causal network modelling format used makes it easy to take into account theories and findings about complex brain processes known from Cognitive, Affective and Social Neuroscience, which, for example, often involve dynamics based on interrelating cycles. This enables to address complex phenomena such as the integration of emotions within all kinds of cognitive processes, and of internal simulation and mirroring of mental processes of others. In this paper also the applicability has been discussed in general terms, showing for example that every process that can be modelled by first-order differential equations, also can be modeled by the presented temporal-causal network modeling apporoach. A variety of example models that can be found in other papers illustrate the applicability of the approach in more detail.
KW - Causal
KW - Dynamic
KW - Modeling
KW - State-determined system
KW - Temporal
U2 - 10.1016/j.bica.2016.02.002
DO - 10.1016/j.bica.2016.02.002
M3 - Article
VL - 16
SP - 131
EP - 168
JO - Biologically Inspired Cognitive Architectures
T2 - Biologically Inspired Cognitive Architectures
JF - Biologically Inspired Cognitive Architectures
SN - 2212-683X
ER -