Novamente:An Integrative Approach to Embodied Artificial General Intelligence

Ben Goertzel

Novamente LLC

Overview

Artificial General IntelligenceHistorical Approaches to AIThe Importance of EmbodimentStages of Cognitive DevelopmentThe AGI-SIM Simulation WorldThe Novamente AI ArchitectureLearning to Play Fetch and TagObject Permanence and Word-Object AssociationSteps Toward Human-Level AI

Artificial General Intelligence (AGI)

“The ability to achieve complex goals in complex environments using limited computational resources”

• Autonomy• Practical understanding of self and others• Understanding “what the problem is” as opposed to

just solving problems posed explicitly by programmers

Existing AI Paradigms

Paradigm Strengths Weaknesses

GOFAI Representation of abstract knowledge

Reasoning (short proofs)

Pattern recognition

Learning

Autonomy

Neural nets Pattern recognition

Learning

Associative memory

Perception/action/cognition integration

Representation of abstract knowledge

Abstract reasoning

Learning

Autonomy

Evolutionary Programming

Pattern recognition

Learning of complex procedures

Representation of abstract knowledge

Abstract reasoning

Autonomy

Probabilistic Reasoning

Representation of abstract uncertain knowledge

Reasoning (short proofs)

Hypothesis formation

Autonomy

Pattern recognition

Subsumption Robotics

Autonomy

Learning

Perception-action integration

Cognition

Representation of abstract knowledge

Embodiment (real or virtual) provides a would-be AGI with

Symbol grounding Most crucially: grounding of subtle words like prepositions

An effective medium for learning complex cognitive skills attention allocation procedure-learning inference control

A sense of self Critical for cognition as well as mental health

Empathy with humans

The Power of Embodiment

Stages of Cognitive Development

Infantile

• Instinct

• Imitation

• Word-Object Association

• Object Permanence

Pre-Operational

• Simple syntax

• Systematic word-object associations

• Single-parameter object classifications

Concrete Operational

• Conservation Laws

• Theory of Mind

• Complex object classifications

• Advanced syntax

Formal

• Abstract deductive reasoning

• Scientific testing of hypotheses

(a path toward embodied AGI, inspired by the classic work of Jean Piaget)

AGI-SIM: An Open-Source Simulation

Environment for AGI

AI systems can sense and act in real-time via embodiment in a 3D virtual worldUses CrystalSpace (open-source game engine) for visualization Provides AI systems with multisensory inputs visual inputs at varying levels of granularity:

pixels, polygons or objects hearing, touch, proprioception, …

Integration with natural language interface for fluid, situated communicationSuitable for teaching/learning based on a developmental-psychology-based methodologyCompatible with Novamente but usable by any AI system via a simple sockets-based protocol

AGI-SIM Status

Version 1 complete and usable Available on SourceForge

Work in progress: More complete set of wireframe models Multi-room simulated world Realistic physics

Via integration of Open Dynamics Engine Integration of NLP chat

AI systems may viably synthesize knowledge gained via various means

virtually embodied experience AGI-SIM

physically embodied experience robotics

explicit encoding of knowledge in natural language ingestion of databases quantitative and relational

Post-Embodied AI

The Novamente Project

Long-term goal: creating "artificial general intelligence" approaching and then exceeding the

human level to be approached via a series of incremental phases

Novamente AI Engine: an integrative AI architecture synthesizes ideas from cognitive and neural science with computer science

algorithms such as evolutionary programming and probabilistic inference uses a unique "weighted labeled hypergraph" knowledge representation Efficient, scalable C++/Linux implementation

Currently parts of the Novamente codebase are being used for commercial projects

natural language processing biological data analysis

The Novamente Project

Moving Toward General Intelligence

Novamente is being used to control an embodied virtual agent in a 3D simulation world called AGI-SIM.

A loosely-Piaget-inspired series of cognitive developmental stages is being used to guide the process of teaching Novamente to carry out a series of progressively more complex tasks in the AGI-SIM environment.

The same approach being used within AGI-SIM may be used in future to embody Novamente in physical robots, including humanoid robots or automated vehicles.

Key Conceptual Aspects of Novamente

Knowledge representation is transparent wherever possible

Procedural and declarative knowledge are easily interconvertible

Evolutionary learning is used to supply creativity for both procedural and declarative knowledge

Probabilistic logical inference is used for basic reinforcement learning as well as abstract thinking

Architecture supports interaction of multiple specialized agents acting on a common knowledge store, along with processes of truly general scope

Design covers all aspects of human and machine cognition

Key Novamente Design Aspects

Aspect Function

Node Denotes a percept, set, list or action

Link Denotes a logical, associative or action relationship

MindAgent A persistently active cognitive, perceptual or active process

Core The “Mind OS” that maintains the store of nodes/links and executes MindAgents and Tasks

Map A semantically significant pattern of activation of MindAgents, Nodes and Links

Unit A collection of nodes, links and MindAgents grouped together to carry out some particular function (like perception, language processing, or abstract reasoning)

NovamenteNode and Links

Node types: WordNode, NumberNode, CharacterNode,… ConceptNode, ProcedureNode, PredicateNode,…

Link types: SimilarityLink, InheritanceLink, ImplicationLink,

EquivalenceLink,… ProcedureApplicationLink, PredicateEvaluationLink,…

Nodes and links weighted with: Probabilistic truth values Attention values similar to neural network activations

Architecture of a Novamente Lobe

MindAgents are based on

•Probabilistic Term Logic

•BOA-based Evolutionary Procedure Learning

•Frequent Itemset Mining

•Stochastic Local Search

Example MindAgents:

•First-order probabilistic inference

•Schema Learning with BOAP

•Probabilistic Attention Allocation

•Procedure and Predicate Evaluation

•Sentence Parsing

•Word Sense Disambiguation

•Sentence Production

•…

Distributed Novamente Architecture

Novamente Architecture:High-Level View

Learning to Play Fetch

ifelse holding(ifelse facingteachersteprotate)(ifelse nearballpickup(ifelse facingballstep

rotate))

Example program for learning to fetch a ball and bring it to the teacher, learned using Novamente’s BOA procedure learning algorithm

Learned via giving Novamente partial reinforcement for bringing the ball near the teacher

Learning to Play Fetch

Interpretation of Example Program

If holding the ball and facing the teacher move forward (to give the ball to the teacher)

Otherwise, if holding the ball, rotate ( in order to face the teacher)

Otherwise, if near the ball, pick it up

Otherwise, if facing the ball, move forward (to get the ball)

Otherwise, rotate (in order to face the ball)

Learning to Play Tag

Agents controlled by BOA learn rules for playing “tag” via reinforcement learning in a tag tournamentOptimal rules learned depend on ratio between agents’ speed and step sizeAn example of “cooperative learning” among a community of agents

Step size significantly smaller than robot diameter

(ifelse it

opponentangle

- opponentangle 3.1416)

step

Step size equal to robot diameter

(ifelse it

opponentangle

- opponentangle 1.5708)

step

Learning to Play Tag

Not-IT’s

Great Escape

Next Steps

Fall 2005 Milestones:

Oct. -- Complete integration of Novamente reasoning/learning with AGI-SIM

Nov./Dec. -- Complete first two “infantile stage” tasks: Object permanence Word-object association

Object Permanence

Word-Object Association

Long-Term Novamente Project Goals

Phase One: Definition of design and implementation of initial versions of key components Goal: a teachable AI system capable of embodiment in a simulation world

 

Phase Two: Refinement of design and implementation in the course of teaching the AI system to control an agent in a simulation world, according to a loosely Piagetan learning plan

Goal: an “artificial child” with qualitatively complex though not humanlike English conversation ability, and the approximate problem-solving ability of an average ten-year old human child within the context of its simulation world

Optionally: Initiate parallel development in “real robotics” via partnership with a robotics research team

 

Phase Three: Instruction of “artificial child” in relevant topics, including ethics and science

 

Phase Four: Instruction of AI system in AI design and general computer science Goal: an ethical AI capable of modifying its own implementation with a goal of self-improvement

Credits

Novamente:Cassio PennachinMoshe LooksAri HeljakkaAndre SennaIzabela GoertzelWelter SilvaMichael RossHugo PintoRodrigo Barra

AGI-SIM:

Ari Heljakka

Welter Silva