The Novamente Cognition Engine: An Integrative, Experiential Learning Focused Approach to AGI
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Transcript of The Novamente Cognition Engine: An Integrative, Experiential Learning Focused Approach to AGI
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IRC Learning and the Novamente Cognition
Engine
Imitative-Reinforcement-Corrective Learning:A Robust Learning Methodology
for Virtual Pets and Avatarsimplemented using a limited version of the Novamente Cognition Engine
Ben GOERTZEL, Cassio PENNACHIN, Nil GEISSWEILLER, Moshe LOOKS, Andre SENNA, Welter SILVA, Ari HELJAKKA, Carlos LOPES
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The Novamente Cognition Engine: An Integrative, Experiential Learning Focused Approach to AGI
Knowledge representation:– Nodes and links (a weighted, labeled hypergraph)– Probabilistic weights, like an uncertain semantic network– Hebbian weights, like an attractor neural network
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Learning algorithms:– Automated program learning (for small, purpose-specific
programs meeting AI-determined specifications)• NCE uses MOSES a probabilistic improvement on genetic
programinng, described in Moshe Looks 2006 PhD thesis– Uncertain inference
• NCE uses Probabilistic Logic Networks, a novel fusion of probability theory and formal logic
– PLN book to be published by Springer in early 2008– Economic Attention Allocation
• Artificial economics used for assignment of credit and attention allocation
The Novamente Cognition Engine: An Integrative, Experiential Learning Focused Approach to AGI
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An Integrative, Experiential Learning Focused Approach to AGI(underlying both the Novamente and OpenCog initiatives)
Cognitive architecture:– Focused on interactive learning, e.g. virtual embodiment, NL
conversation, robotics– Largely inspired by human cognitive architecture
Teaching Methodology:– Embodied, experiential, socially interactive– Combining imitative and reinforcement learning
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Novamente Cognition Engine is one, well-fleshed-out, example of a concrete AGI design within this family of designs
OpenCog framework (OpenCog.org) incorporates Novamente’s knowledge representation and overall software framework, and will allow experimentation with multiple alternate learning algorithms within this same framework
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Why May This Approach Have a Prayer of Succeeding?
• It is based on a well-reasoned, comprehensive theory of mind, – covering both the concretely-implemented and
emergent aspects of mind– Oriented toward encouraging the emergence of a
self-system within the AI’s knowledge base, based on embodied social learning
– See The Hidden Pattern• The specific algorithms and data structures chosen to
implement this theory of mind are efficient, robust and scalable and, so is the software implementation
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Stages of Cognitive Development
No self yet
Emergence of phenomenal self
Objective detachment from phenomenal self
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Intelligence
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Intelligence
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Intelligence
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Animal-level AI’s killer app: Virtual Pets
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Virtual Worlds
Each month, 24% of the 34.3M US kids
and teens on the web are visiting a virtual world. By 2011 that number is expected to be 53%
For example, Webkinz grew from 800K users in Oct 2006 to more than 7M in Oct 2007
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Media for Virtual Pets
2.3B use mobile phones
1.2B use the Internet
465M joined Virtual Worlds
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Pets in Virtual Worlds
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Pets for PC Games
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Pets for Mobile Gaming
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Pets in World of Warcraft
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Current Virtual Pets: Cute but Dumb
Current virtual pets are rigidly programmed and lack emotional responsiveness, individual personality or ability to learn.
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Building a Better Pet Brain
Adding the ability to have the pets genuinely learn and respond to the environment will make them more real to the user, and increase the user/virtual pet bond. This supports trends toward personalization and community, enriching both.
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Novamente Pet Brain
Novamente pets respond to and interact with objects, creatures and avatars, and learn from experiences that will then influence future behavior.
For example, if there happens to be a cat around, there is a good chance the pet dog would chase it. However, if the cat scares him away, the dog might not be so eager to chase the cat next time.
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Training the Pet Brain
Novamente pets can be taught to do simple or complex tricks, from sitting to playing soccer or learning a dance -- by learning from a combination of encouragement, reinforcement and demonstration.
give “sit” command clap when pet sitsshow “sit” example
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IRC Learning
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Teaching with a Partner
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Current Pet Brain Architecture
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Next-Gen Pet Brain Architecture
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Next Step: Language Learning?
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Intelligence