Reasoning Entity

A Reasoning Entity is a cognitive entity capable of performing reasoning tasks (applying structured processes to draw conclusions, make decisions, or solve problems).

• Context:
  • It can range form being an Living Reasoning Entity (octopus) to being a Mechanical Reasoning Entity (such as LLM-based reasoners).
  • It can range form being an Unconscious Reasoning Entity (octopus, LLM-based reasoners) to being a Conscious Reasoning Entity (such as conscious persons).
  • ...
• Example(s):
  • Living Reasoning Entity, such as:
    • Animal Reasoner: Some animals, like primates and octopi, exhibit problem-solving abilities and decision-making behaviors.
    • Human Reasoner: A person using logic, intuition, or experience to solve a problem or make a decision.
    • ...
  • Computational Reasoning System, such as:
    • Rule-Based Expert System: An AI system that applies pre-defined rules to generate specific recommendations or conclusions.
    • Formal Logic Reasoning Systems: Systems designed to perform precise logical inferences and prove theorems or verify software.
    • LLM-based Reasoners: Large language model-based systems that use probabilistic and pattern-based approaches to simulate reasoning.
  • ...
• Counter-Example(s):
  • Reactive System: Executes predefined actions without reasoning or learning.
  • Random Process: Generates outcomes without any logic, pattern, or reasoning mechanism.
  • Search Engine: Retrieves information based on keywords without performing logical inference or reasoning over the content.
  • Signal Processor: Processes input signals without drawing conclusions or making decisions.
• See: Reasoning, Computational Reasoning System, Human Cognition, Artificial Intelligence.

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References

2024

• Perplexity.ai
  • Human Cognition: Human cognition encompasses the mental processes involved in acquiring knowledge and understanding through thought, experience, and the senses. Some key aspects of human cognition related to reasoning include:
    • **Deductive Reasoning**: Drawing logical conclusions from given premises. Humans use deductive reasoning to apply general rules to specific situations.
    • **Inductive Reasoning**: Making broad generalizations from specific observations. This allows humans to form hypotheses and theories based on patterns.
    • **Abductive Reasoning**: Inferring the most likely explanation from an incomplete set of observations. Humans use this for diagnosis and problem-solving.
  • Artificial Intelligence Systems: AI systems, including large language models (LLMs), aim to replicate aspects of human-like reasoning:
    • **Rule-Based Systems**: Early AI used explicit logical rules for deductive reasoning.
    • **Machine Learning**: Modern AI uses inductive reasoning to learn patterns from data.
    • **Probabilistic Models**: Enable abductive reasoning by inferring likely explanations.
    • **LLM-Based Reasoners**: Large language models like GPT-3 can perform multiple reasoning types by leveraging their broad knowledge base[4].
  • Citations:

[1] https://en.wikipedia.org/wiki/Cognition
[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3385676/
[3] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8692281/
[4] https://www.psypost.org/fascinating-brain-imaging-research-sheds-light-on-a-fundamental-mechanism-of-human-cognition/
[5] https://www.nature.com/articles/s41593-018-0312-0
[6] https://www.lumosity.com/hcp/overview
[7] https://www.neurosciencephd.columbia.edu/content/human-cognition-behavior-and-neuroscience
[8] https://www.sciencedirect.com/topics/social-sciences/human-cognition

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