Occupancy‐based utility pattern mining in dynamic environments of intelligent systems

The categorization of geometric objects in 3D and 2D contexts depends on invariant referents

10.31234/osf.io/cbhw2 ◽

2021 ◽

Author(s):

Simon Stephan ◽

Sarah Placì ◽

Michael R. Waldmann ◽

Giorgio Vallortigara

Keyword(s):

Cognitive Abilities ◽

Intelligent Systems ◽

Dynamic Environments ◽

Object Categorization ◽

Geometrical Configuration ◽

Geometrical Features ◽

Geometric Objects ◽

The World ◽

Treat All

The categorization of geometric objects is one of the most fundamental problems all intelligent systems have to deal with in dynamic environments in which objects' geometrical configuration constantly changes. Animals, including humans, do not treat all geometrical differences equally: they ignore some geometrical features when it comes to generalization but not others. So far, no theory has been presented that explains this cognitive phenomenon. We here propose and empirically test such a theory. The theory identifies and relies on the invariant referents existing in 3D (i.e., gravity) and 2D (e.g., any 2D frame) environments to predict the geometrical differences reasoners consider as important or irrelevant for object categorization. We test and confirm a novel central prediction of the theory, namely that human reasoners categorize objects differently in 3D and 2D environments. These findings cast new light on core cognitive abilities that minds use to make sense of the world.

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PLANNING AND REACTING ACROSS SUPERVENIENT LEVELS OF REPRESENTATION

International Journal of Cooperative Information Systems ◽

10.1142/s0218215792000118 ◽

1992 ◽

Vol 01 (03n04) ◽

pp. 411-449 ◽

Cited By ~ 2

Author(s):

LEE SPECTOR ◽

JAMES HENDLER

Keyword(s):

Control Systems ◽

Intelligent Systems ◽

Hierarchical Control ◽

Dynamic Environments ◽

Ai Planning ◽

Planning Systems ◽

Complex Dynamic ◽

Strong Constraint ◽

External Agents

For intelligent systems to interact with external agents and changing domains, they must be able to perceive and to affect their environments while computing long term projection (planning) of future states. This paper describes and demonstrates the supervenience architecture, a multilevel architecture for integrating planning and reacting in complex, dynamic environments. We briefly review the underlying concept of supervenience, a form of abstraction with affinities both to abstraction in AI planning systems, and to knowledge-partitioning schemes in hierarchical control systems. We show how this concept can be distilled into a strong constraint on the design of dynamic-world planning systems. We then describe the supervenience architecture and an implementation of the architecture called APE (for Abstraction-Partitioned Evaluator). The application of APE to the HomeBot domain is used to demonstrate the capabilities of the architecture.

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The categorization of geometric objects in 3D and 2D contexts depends on invariant referents

10.31234/osf.io/78syx ◽

2021 ◽

Author(s):

Sarah Placì ◽

Simon Stephan ◽

Michael R. Waldmann ◽

Giorgio Vallortigara

Keyword(s):

Cognitive Abilities ◽

Intelligent Systems ◽

Dynamic Environments ◽

Object Categorization ◽

Geometrical Configuration ◽

Geometrical Features ◽

Geometric Objects ◽

The World ◽

Treat All

The categorization of geometric objects is one of the most fundamental problems all intelligent systems have to deal with in dynamic environments in which objects’ geometrical configuration constantly changes. Animals, including humans, do not treat all geometrical differences equally: they ignore some geometrical features when it comes to generalization but not others. So far, no theory has been presented that explains this cognitive phenomenon. We here propose and empirically test such a theory. The theory identifies and relies on the invariant referents existing in 3D (i.e., gravity) and 2D (e.g., any 2D frame) environments to predict the geometrical differences reasoners consider as important or irrelevant for object categorization. We test and confirm a novel central prediction of the theory, namely that human reasoners categorize objects differently in 3D and 2D environments. These findings cast new light on core cognitive abilities that minds use to make sense of the world.

Download Full-text