Wednesday, August 18, 2010

MicroPSI and Genifer and dANN

I've been investigating MicroPSI.  Here are some introductions:
Psi-Theory is about human action regulation, intention and behaviour. The theory describes a comprehensive model of the human brain, its cognitive processes, emotion and motivation. It is about the informational structure of an intelligent, motivated, emotional agent (called Psi) which is able to survive in arbitrary domains of reality. This agent is driven by several satisfiable needs: need for food, water, the avoidance of pain, certainty, competence and affiliation). The cognitive processes are modulated by emergent emotional states, memory, and sensory perception.

Source Code

I collected a package of some old Java MicroPSI source code from 2005, including the above mentioned documentation that I found helpful.

  • Five (5) basic needs that drive behaviour
  • Generate intentions to satisfy needs

Action Regulation
  • Intention is selected and executed
  • Action regulation is modulated by Emotions

  • Emotions emerge from the system through modulation of the cognitive processes
  • Agents do not “have” an emotion, but it thinks and acts emotionally

5 Basic Needs that Trigger Behavior
  • Preserve Existence
    • food, water, avoidance of pain
  • Preserve Species
    • sexuality and reproduction
  • Affiliate
    • need to belong to a group and engage in social interactions (“signals of legitimacy”)
  • Be Certain
    • predictability of events and consequences
  • Be Competent
    • capability of mastering problems and tasks, including satisfying one’s needs

PSI Quad Networks
Psi Neural Networks and Activation

PSI as part of an Open-Source AGI System

I was wondering how these systems might fit together in a modern implementation of the PSI Cognitive model:
  • PSI - action, motivation, intention, emotion
  • Genifer - logic nodes and logic processing, providing memory, descriptions of sensations, and invokeable actions, geniform natural language
  • dANN - graph framework, bayesian, markov, signal processing, genetic wavelets, procedural learning
  • EnCog - neural networks and learning
  • OpenRDF - read and write semantic web data, utilize publicly available OWL ontologies to structure knowledgebases
  • MOSES - procedure learning
  • DeSTIN - machine learning, pattern recognition, concept formation
  • WordNet, FrameNet, ConceptNet, ...
  • Stanford Parser | RelEx - natural language input
  • NLGen - natural language output
  • anything else?


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