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Related Experiment Video

Updated: Mar 7, 2026

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

Mario Becomes Cognitive.

Fabian Schrodt¹, Jan Kneissler¹, Stephan Ehrenfeld¹

¹Department of Computer Science and Department of Psychology, Eberhard Karls University of Tübingen.

Topics in Cognitive Science

|February 9, 2017

Summary

This summary is machine-generated.

The SEMLINCS cognitive architecture learns production rules from sensorimotor data, enabling embodied agents to exhibit versatile, goal-directed behavior in environments like Super Mario Bros. This approach models cognitive development without pre-programmed rules.

Keywords:

Artificial intelligence Cognitive architecture Cognitive science and games Event schemata Language grounding Learning Self-motivated behavior Symbol grounding

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Published on: July 31, 2007

Area of Science:

Cognitive Science
Artificial Intelligence
Computational Neuroscience

Background:

Cognitive architectures aim to model human cognition computationally.
Existing symbolic architectures often rely on pre-defined rules and symbols.
A recent proposal suggests a subsymbolic computational theory for unifying cognition.

Purpose of the Study:

Introduce the cognitive control architecture SEMLINCS.
Model embodied cognitive development where agents learn symbolic structures.
Enable agents to plan and control interactions autonomously.

Main Methods:

Implemented SEMLINCS to control a game agent (Mario) in a Super Mario Bros. clone.
Focused on autonomous learning of production rules from continuous sensorimotor experiences.
Evaluated behavioral versatility and rule acquisition.

Main Results:

Demonstrated successful learning of production rules and symbols from sensorimotor data.
Showcased autonomous, self-motivated behavioral control and planning.
Observed knowledge- and motivation-dependent behavioral individualization.

Conclusions:

SEMLINCS successfully models embodied cognitive development by learning rules from experience.
The architecture supports versatile, goal-directed behavior and planning.
Future enhancements are needed, but the focus on learning rules from sensorimotor data is promising.