AI - Symbol Grounding Problem.

 

The symbol grounding problem shows how entities might be linked to their physical counterparts.

To put it another way, the symbol grounding issue is concerned with how ideas inside an intelligent agent's representation might be linked to their referents in the real world.

Stevan Harnad (1990) likened the situation to a symbol/ symbol merry-go-round, in which one symbol is grounded by another meaningless symbol.

A cognitive model is a representation of the mind in symbols.

To construct artificial cognitive systems, several types of representations are required.

There are two types of semantics in a mental representation: 

1. lexical (or semantic system) and 
2. compositional (or connectionist system) semantics.

• The lexical semantic is the meaning of a single word, whereas the compositional semantic is the meaning of a set of words in relation to one another.

• The compositional semantic is studied in traditional cognitive research.

Cognitive architectures are hybrid systems that pick the matched knowledge piece using lexical and compositional representations of knowledge (or chunks) and pattern matching.

Architecture's sensory input is chunks. 

For all hybrid systems, tying pieces to the outside world has not been described.

As a result, the symbol grounding issue prevents artificial cognitive systems from reaching their full cognitive potential, particularly in terms of perception and motor abilities.

A new sort of memory, dubbed visual patterns, has been created to overcome the symbol grounding issue for cognitive models.

• Visual patterns are visual representations of items in the actual world that are linked to knowledge aspects (chunks).

• Cognitive models can communicate with one other via a more accurate representation of vision, assisting in the solution of the symbol grounding issue.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Cognitive Architectures.

References And Further Reading

Harnad, Stevan. 1990. “The Symbol Grounding Problem.” Physica D: Nonlinear Phenomena 42, no. 1–3 (June): 335–46.

Ritter, Frank E., Farnaz Tehranchi, and Jacob D. Oury. 2018. “ACT-R: A Cognitive Architecture for Modeling Cognition.” Wiley Interdisciplinary Reviews: Cognitive Science 10, no. 4, 1–19.

Tehranchi, Farnaz, and Frank E. Ritter. 2018. “Modeling Visual Search in Interactive Graphic Interfaces: Adding Visual Pattern Matching Algorithms to ACT-R.” In Proceedings of the 16th International Conference on Cognitive Modeling, 162–67. Madison: University of Wisconsin.

Artificial Intelligence - Cognitive Agent Interaction.

 

 A key assumption of cognitive science is the ambition to provide a unified theory of mind that reveals the qualitative form of human cognition and behavior.

Cognitive agents (or cognitive models) are cognitive theory applications.

These agents are computer simulations that can predict and explain human thought.

Cognitive agents must complete a real-world task that involves many different kinds of cognition, including interaction.

Artificial intelligence (AI) is a method of simulating human intelligence.

These simulations aren't necessarily fully interactive or comprehensive.

An interactive cognitive agent is designed to cover all of the actions that a user engages in when using a current display-oriented interface.

Interactive cognitive agents try to think and behave like people, making them ideal candidates for surrogate users.

Noninteractive cognitive agents just provide a trace of the mind's cognitive steps, but interactive cognitive agents give more thorough and precise simulations.

This is accomplished through interactive cognitive agents engaging directly with the screen-as-world.

Models may now communicate with uninstrumented interfaces on both the computer where the model is executing and on distant machines.

Improved interaction not only supports a wider variety of activity, but it also improves the model's accuracy and representation of human actions on tasks that require interaction.

A cognitive architecture, knowledge, and a perception-motor module are the three components of an interactive cognitive agent.

Cognitive architectures are infrastructures that give a set of fixed computational processes that represent the fixed cognitive mechanisms that create behavior for all activities.

They give a mechanism to combine and apply cognitive science theory as a fixed set.

A cognitive model is generated when knowledge is added to a cognitive architecture.

To interact with the environment, the perception-motor module regulates visual and motor output.

Cognitive agents that are interactive can view the screen, push keys, and move and click the mouse.

Through their comprehensive coverage of theory and capacity to produce actions, interactive cognitive agents contribute to cognitive research, human-computer interaction, automation (interface engineering), education, and assistive technology.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Cognitive Architectures.

Further Reading:

Newell, Allen. 1990. Unified Theories of Cognition. Cambridge, MA: Harvard University Press.

Ritter, Frank E., Farnaz Tehranchi, and Jacob D. Oury. 2018. “ACT-R: A Cognitive Architecture for Modeling Cognition.” Wiley Interdisciplinary Reviews: Cognitive Science 10, no. 4: 1–19.