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

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

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

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

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

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

Stages of Cognitive Development

No self yet

Emergence of phenomenal self

Objective detachment from phenomenal self

Intelligence

Intelligence

Intelligence

Animal-level AI’s killer app: Virtual Pets

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

Media for Virtual Pets

2.3B use mobile phones

1.2B use the Internet

465M joined Virtual Worlds

Pets in Virtual Worlds

Pets for PC Games

Pets for Mobile Gaming

Pets in World of Warcraft

Current Virtual Pets: Cute but Dumb

Current virtual pets are rigidly programmed and lack emotional responsiveness, individual personality or ability to learn.

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.

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.

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

IRC Learning

Teaching with a Partner

Current Pet Brain Architecture

Next-Gen Pet Brain Architecture

Next Step: Language Learning?

Intelligence