Novamente: An Integrative Approach to Embodied Artificial General Intelligence Ben Goertzel Novamente LLC

Overview Artificial General Intelligence Historical Approaches to AI The Importance of Embodiment Stages of Cognitive Development The AGI-SIM Simulation World The Novamente AI Architecture Learning to Play Fetch and Tag Object Permanence and Word-Object Association Steps 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 ParadigmStrengthsWeaknesses 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 world Uses 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 communication Suitable for teaching/learning based on a developmental-psychology-based methodology Compatible 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 AspectFunction 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)

Novamente Node 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 facingteacher step rotate) (ifelse nearball pickup (ifelse facingball step 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 tournament Optimal rules learned depend on ratio between agents ’ speed and step size An example of “ cooperative learning ” among a community of agents Step size significantly smaller than robot diameter (ifelse it opponentangle - opponentangle ) step Step size equal to robot diameter (ifelse it opponentangle - opponentangle ) 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 Pennachin Moshe Looks Ari Heljakka Andre Senna Izabela Goertzel Welter Silva Michael Ross Hugo Pinto Rodrigo Barra AGI-SIM: Ari Heljakka Welter Silva