1. IRC Learning and the Novamente Cognition Engine Imitative-Reinforcement-Corrective Learning: A Robust Learning Methodology for Virtual Pets and Avatars implemented using a limited version of the Novamente Cognition Engine

2. 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

6. 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

7. 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

9. 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

10. 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

11. Stages of Cognitive Development No self yet Emergence of phenomenal self Objective detachment from phenomenal self

12. Intelligence

16. Animal-level AI’s killer app: Virtual Pets

17. 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

18. Media for Virtual Pets 2.3B use mobile phones 1.2B use the Internet 465M joined Virtual Worlds

19. Pets in Virtual Worlds

20. Pets for PC Games

22. Pets for Mobile Gaming

23. Pets in World of Warcraft

24. Current Virtual Pets: Cute but Dumb Current virtual pets are rigidly programmed and lack emotional responsiveness, individual personality or ability to learn.

25. 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.

26. 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.

27. 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

28. IRC Learning

29. Teaching with a Partner

30. Current Pet Brain Architecture

31. Next-Gen Pet Brain Architecture

32. Next Step: Language Learning?

33. Intelligence