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Schemas01:42

Schemas

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A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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Schemata01:17

Schemata

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A schema is a mental construct that organizes related concepts, allowing the brain to process information efficiently. Upon activation, schemata facilitate assumptions about people or objects.
Two types of schemata are:
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Levels of Organization01:09

Levels of Organization

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Biological organization is the classification of biological structures, ranging from atoms at the bottom of the hierarchy to the Earth's biosphere. Each level of the hierarchy represents an increase in complexity that builds upon the previous level.
Molecules Are Composed of Atoms, and Biomolecules Are Assembled from Molecules:
The most basic levels include atoms, molecules, and biomolecules. Atoms, the smallest unit of ordinary matter, are composed of a nucleus and electrons. Molecules...
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Radical Formation: Abstraction00:47

Radical Formation: Abstraction

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The electron of an atom can be abstracted from a compound by a relatively unstable radical to generate a new radical of relatively greater stability. For example, an initiator which forms radicals by homolysis can abstract a suitable species like a hydrogen atom or a halogen atom from a compound to generate a new radical. This ability of radicals to propagate by abstraction is a crucial feature of radical chain reactions.
Even though homolysis produces radicals, it is different from radical...
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Mnemonic Devices01:23

Mnemonic Devices

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Mnemonic devices are cognitive tools that facilitate memory retention by linking new information to familiar patterns or organizational strategies. These techniques are beneficial for remembering complex or lengthy sets of information by simplifying and structuring them in easily retrievable ways.
Acronyms
Acronyms are created by using the initial letters of a series of words to form a new word or phrase. This approach condenses complex information into a single, memorable entity. For example,...
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Piaget's Stage 3 of Cognitive Development01:17

Piaget's Stage 3 of Cognitive Development

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During Piaget's concrete operational stage, from ages 7 to 11, children exhibit a marked increase in logical thinking skills, specifically in relation to tangible, real-world events. This stage is characterized by the development of several essential cognitive concepts, including conservation, reversibility, and classification, all of which support the child's evolving capacity for structured thought.
Conservation and Constancy of Quantity
A significant cognitive milestone in the...
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Related Experiment Video

Updated: Mar 1, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Creating Objects and Object Categories for Studying Perception and Perceptual Learning

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Constructing Abstraction Hierarchies Using a Skill-Symbol Loop.

George Konidaris1

  • 1Departments of Computer Science and Electrical & Computer Engineering Duke University, Durham NC 27708.

IJCAI : Proceedings of the Conference
|June 6, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a framework for creating abstraction hierarchies, enabling agents to build complex models from simpler ones. This method facilitates faster planning by leveraging learned skills for efficient problem-solving.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Robotics

Background:

  • Markov decision processes (MDPs) are fundamental to reinforcement learning but can be computationally expensive for complex problems.
  • Developing effective representations is crucial for efficient decision-making in artificial agents.
  • Existing methods often struggle with scalability and the automatic generation of useful abstractions.

Purpose of the Study:

  • To present a novel framework for constructing abstraction hierarchies in artificial intelligence.
  • To enable agents to learn increasingly abstract representations of their environment and tasks.
  • To demonstrate the utility of skill-based representations for efficient planning.

Main Methods:

  • The proposed framework involves an iterative process of skill-construction and representation-construction phases.
  • Agents alternate between learning new skills and building more abstract Markov decision processes (MDPs) based on these skills.
  • The core idea is that an agent's skills define the appropriate abstract representation for a given problem.

Main Results:

  • The developed framework successfully constructs hierarchies of abstract MDPs.
  • The learned abstraction hierarchies significantly improve planning efficiency.
  • The framework was illustrated with a successful application in the Taxi domain, demonstrating practical utility.

Conclusions:

  • Abstraction hierarchies built using skill-based representations offer a powerful approach for enhancing AI planning.
  • This method provides a principled way to generate useful abstractions automatically.
  • The framework has the potential to improve the performance and scalability of intelligent agents in complex environments.