Decision-making in the cognitive architecture SiMA

Wendt, Alexander; Gelbard, Friedrich; Fittner, Martin; Schaat, Samer; Jakubec, Matthias; Brandstätter, Christian; Kollmann, Stefan

doi:10.1109/taai.2015.7407100

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…This method of navigation will in the next step get integrated into the multi-agent simulation framework of the project SiMA, which is continuously improved and developed further, e.g. in [14], to fulfill further tasks.…”

Section: Discussionmentioning

confidence: 99%

Enabling Smart Navigation by Incorporating Human Inspired Evaluation of Route Sections

Brandstaetter¹

2018

JCP

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It is challenging for every child not becoming lost in the shopping center. Simulated agents have the same problem. So they need some localization. On the basis of the existing cognitive architecture in th project Simulation of Mental Apparatus and Applications (SiMA) at the Institute of Computer Technology (ICT) currently, we show, how cognitive architectures can get extended by location awareness and navigation in a human-like way. This paper deals with particular requirements for the used data structures to enable self-localization and pathfinding in a bionically inspired way. The typical data structure for navigation in the form of a connected graph gets modified and extended to be compatible with psychoanalytic concepts and still allow navigation. The functionality was implemented exemplary in a multiagent simulation.

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Section: Discussionmentioning

confidence: 99%

Enabling Smart Navigation by Incorporating Human Inspired Evaluation of Route Sections

Brandstaetter¹

2018

JCP

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“…Kollmann et al [70] proposes a multiagent approach to develop a distributed cognitive architecture based on the cognitive architecture SiMA (Simulation of the Mental Apparatus and Applications [134]) that includes emotions. The proposed architecture is called ECABA and provides a primary process that deals with reactive behavior and a secondary process that controls deliberative behavior to cope with long-term goals.…”

Section: Overview Of Normative Emotional Agentsmentioning

confidence: 99%

Normative Emotional Agents: A Viewpoint Paper

Argente¹,

Val²,

Perez-Garcia³

et al. 2022

IEEE Trans. Affective Comput.

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Human social relationships imply conforming to the norms, behaviors and cultural values of the society, but also socialization of emotions, to learn how to interpret and show them. In multiagent systems, much progress has been made in the analysis and interpretation of both emotions and norms. Nonetheless, the relationship between emotions and norms has hardly been considered and most normative agents do not consider emotions, or vice-versa. In this article, we provide an overview of relevant aspects within the area of normative agents and emotional agents. First we focus on the concept of norm, the different types of norms, its life cycle and a review of multiagent normative systems. Secondly, we present the most relevant theories of emotions, the life cycle of an agent's emotions, and how emotions have been included through computational models in multiagent systems. Next, we present an analysis of proposals that integrate emotions and norms in multiagent systems. From this analysis, four relationships are detected between norms and emotions, which we analyze in detail and discuss how these relationships have been tackled in the reviewed proposals. Finally, we present a proposal for an abstract architecture of a Normative Emotional Agent that covers these four norm-emotion relationships.

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“…Therefore, they don't fit as an overall solution into our holistic functional model of the human mind. But there are still other projects in the area of artificial general intelligence that should be shortly discussed here by picking LIDA (Learning Intelligent Distribution Agent) as a representative concerning the similarity to SiMA [8] and the learning functions.…”

Section: Background and Related Workmentioning

confidence: 99%

How Human Inspired Learning Enhances the Behavior of Autonomous Agents

Fittner¹

2018

JCP

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An autonomous agent must deal with unforeseen situations that can't be preprogrammed. Therefore, the agent has to make its own experiences, solutions and valuations to situations, actions and objects to be able to enhance previous actions and avoid repeating wrong actions. On the basis of the existing cognitive architecture Simulation of Mental Apparatus and Applications (SiMA) at the Institute of Computer Technology (ICT) an bionically inspired attempt of learning should be implemented in functional model of the human mind which is then used in a multi-agent simulation showing how bionically inspired cognitive architectures can get extended by learning. Due to the attempt in the project SiMA the learning function has to fit in the psychoanalytic model and therefore it has to be compatible with the way of learning that human beings do. This might also help to get a little bit closer to the understanding of how the human mind manipulates memory to show these until now unreached cognitive abilities.

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Decision-making in the cognitive architecture SiMA

Cited by 8 publications

References 5 publications

Enabling Smart Navigation by Incorporating Human Inspired Evaluation of Route Sections

Enabling Smart Navigation by Incorporating Human Inspired Evaluation of Route Sections

Normative Emotional Agents: A Viewpoint Paper

How Human Inspired Learning Enhances the Behavior of Autonomous Agents

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