Selmer Bringsjord, Nick Cassimatis, Kostas Arkoudas, and Bettina Schimanski

Department of Cognitive ScienceDepartment of Computer Science

Rensselaer Polytechnic Institute (RPI)Troy NY 12180 USA

December 9 2004

Human-Level Machine Learning

RAIR Lab Sponsors

-Cracking Project;“Superteaching”

Slate (Intelligence Analysis)

test generation

synthetic characters/psychological time

Deontic/DoxasticReasoning

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hypothesis generation;AI in support of IA

advanced synthetic charactrs

“Poised-For” Learning

Overview• The Problem:

– Machine learning is dominated by forms of learning that are impoverished relative to the human case.

– Humans often learn by leveraging an ensemble of “pre-installed” heterogeneous reasoning mechanisms and vast amounts of prior knowledge.

• Solution/Goal:– Formalize human learning and rich cognitive mechanisms that

underlie and enable it.– Implement these formalizations to produce “human-level”

machine learning, and corresponding applications.

• Applications:– Software and robotic applications; in our case, specifically

• Homeland defense/intelligence analysis tools• Elder-care robots that are easy to use.

– Improve learning in humans:• Intelligent tutoring systems in math, logic, computer science• More precise understanding of learning disabilities for less

traumatic interventions

Machine Learning Today:Costly Trial and Error

• Traditional machine learning:– Learn only after many repetitions of trial and error– Stuck on function-based model– E.g., Language: WSJ Corpus, 1987-1989, with 39 million words– Explanation-Based Learning uses only primitive reasoning/knowledge

• Hurts with applications:– Trial and error not good in cases where errors kill

• Medical robotics– Thousands of learning trials can be expensive

• Acquainting a robot with a new hospital would take days• Teaching people new software makes them less productive in the short-term.

Machines train us now instead of us training them.– Learning trials often not available

• Homeland security: Not thousands of people in flight schools– Robots and software therefore limited to narrow tasks and inflexible– We are forced to assemble machine knowledge manually

• CYC has over a million facts and is not even remotely complete

Some Motivating Examples...

Millions of students are currently learning primarily by reading -- and ditto e.g. for adult researchers like us!

Example 1: Suppose You Were Tasked to Learn About Astronomy!

The scorpion lies between Libra and Sagittarius in the Milky Way. It is not hard to imagine this pattern of starts resembling a scorpion,with its claws and stinging tail. An arc of stars marks the curve of itsraised tail and the fiery red star Antares lies at is heart...

Example 2: Human One-shot Learning(a simple example)

USB

CONVERTORCUP

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Example 3: ??? Polyscheme-Powered NRL Bot? PERI Cracking Sub-Test on

Wais?? ???

The traditional machine learning approach...

Behavior of Micro-PERI

Implications of One-Shot Learning and Learning by Reading

• Learning by reading and one-shot learning examples require:– Rich set of representation and reasoning abilities early on

• Where was speaker looking when he said “USB Converter”.• Social reasoning to track where speaker was looking.• Spatial and temporal reasoning to infer what he was looking at.• Diagrammatic reasoning

– Existing machine learning algorithms have no notion of space, time or human attention.

– Statistical generalization just one of several learning strategies; also need:• Inference (deductive, abductive, inductive, ...) from single group of percepts• Analogy• Imitation• Instruction

– Learning much more socially and physically interactive.• Ask questions: Why? How? What if? Physically test their own hypotheses about

the world.• And, in learning by reading...

– the best learners are those who “pre-test” themselves, and hence acquire “poised-for” knowledge that marks true learning

To Solve the Problem:A New (5-step) Research Program

1 Without flinching, study the human case -- humans (including kids) who learn rapidly, including learning by reading

– Developmental psychology has shown that even infants and toddlers have rich notions of:• Time, place, causality, belief, desire, attention, number, etc., and of inference over these

concepts

2 Develop formal theories that show how to use these factors to make learning faster and more effective

3 Develop machine learning algorithms using these formalizations that learn by:– Explicit reading and instruction– Analogical reasoning– Deduction, Abduction, etc.– Imitation– Visual reasoning

4 Build applications from these algorithms that have broad impact– Elder care– Homeland security

5 Trace out the implications of these algorithms for better teaching/learning in the human sphere, particularly in mathematics/logic instruction

– address “Math Gap”– including intelligent tutoring systems and synthetic characters

Our Approach Forges a Bridge

SBE

Behavioral &Cognitive Sciences

CISE

Artificial Intelligence andCognitive Science

? ?

Our Approach Forges a Bridge

SBE

Behavioral &Cognitive Sciences

CISE

Artificial Intelligence andCognitive Science

The Right Time:Resurrection of Human-Level AI

• Recognition of need for human-level AI and integrated cognitive systems growing:– Dedicated issue of AAAI’s journal of record (AI Magazine) to be devoted to human-level

AI• Cassimatis editor, Bringsjord, Arkoudas, Schimanski contributors

– AAAI Fall Symposium on Integrated Cognition (Cassimatis led)

– “Grand Cognitive Challenges” under discussion @ DARPA• “Psychometric AI” a candidate

– Hundreds of studies in infant cognition give us a good idea of what the right substrate is.

• Integrated cognitive models exist and are advancing every day

• Computational infrastructure there:– Abundant computational power for multiple methods in one system

– Formal methods exploding with new power (e.g., Athena)

– Robot and machine vision infrastructure in place:• Object recognition

• Face recognition, eye-tracking

• Mobility and navigation

• Robot manipulation

So the time is ripe for human-level machine learning.

Applications

Some Applications• High-stakes applications where trial and error too dangerous.

– Homeland security.– Hazardous waste removal.

• Robots and software for less sophisticated or learning-challenged humans use them.– Disabled.– Elder care.

• Elder-care robots easier to use by the older set.• Emerging Robotics Technologies & Applications Conference Proceedings, March 9-10, 2004,

Cambridge, MA– Rodney Brooks mentioned Elderly Care as one of the current future trends in robotics:

» Currently: None» Future: Robotic Assistants in Millions of Households

• Less brittle, more general, easier-to-learn and use robots and software.• Better learning environments:

– Direct/instruct robots (PERI)– More accurate pinpoint causes of problem learning.

A catalyst grant for ...?

• Carry out proof-of-concept version of entire 5-step research agenda• Build team to implement this sequence

– part of team that would presumably power full SLC on Human-Level Machine Learning

• Build proof-of-concept– p-o-c would run all the way through our proposed 5-step R&D sequence,,

start to finish– application/implementation:

• homeland defense• Elder care robot• ITS for math/logic/comp sci

• Workshops/Symposia• Conference presentations• Publications• Web site from the very start• ...

END

Objection

• How is this an improvement over GOFAI? i.e., Why isn’t this the 1970s all over again?– Less knowledge of human learning then– Formal methods in their infancy

• Nothing like Athena (used to prove a good part of Unix sound)!• Like two-layer neural networks compared to bigger ones

– Formal infrastructure was fragmented. Not known how to combine logical and probabilistic knowledge?

– So researchers were either using no representation and reasoning substrate or they were using the wrong one.

– Integrated cognitive models for combining methods not developed, • Polyscheme, ACT-R, ...

– These techniques were not interactive.• No question asking• No tracking or reasoning about human intent• No experimentation

PERIPsychometric Experimental Robotic Intelligence

• Scorbot-ER IX • Sony B&W XC55 Video

Camera• Cognex MVS-8100M

Frame Grabber• Dragon Naturally

Speaking Software• NL (Carmel & RealPro?)• BH8-260 BarrettHand

Dexterous 3-Finger Grasper System

Our Assets• Background in intersection of reasoning and

formal methods, and learning– Bringsjord, Cassimatis, Arkoudas, and

Schimanski

• Prior R&D in logic-based machine learning.– Bringsjord, Arkoudas

• Background in child development.– Cassimatis

• Integrated cognitive models– All four

• Background in robotics– Cassimatis, Bringsjord, Schimanski

Prior Related Work on One-Shot Learning

• There isn’t anything that maches up perfectly.

• But, related, we have:

Prior Related Work on Learning by Reading

• Ask for pointers from Ken Forbus...

Impact on Machine Learning and AI

• More flexible and resourceful learning and reasoning algorithms

• Intellectually flexible robots (again, e.g., PERI)

• Quantum leap in machine learning• Learning in situations that were impossible

before• Integration of reasoning community back

into learning community• Impact back on education, including

machine-assisted education (e.g., intelligent tutoring systems & synthetic characters)

Impact on Study of Human Learning

• Existing empirical work hampered by vague theories that make results of simple experiments controversial. – Formal theory should help this

• Develop better understanding of which instruction or learning techniques are best in which circumstances.

• More specifically:– Will produce new pedagogy linking learning to

reasoning (mathematics/logic a beneficiary)– Will produce revolutionary advances in intelligent

tutoring systems, synthetic characters/simulation)