Pat LangleyPat LangleyComputational Learning LaboratoryComputational Learning Laboratory

Center for the Study of Language and InformationCenter for the Study of Language and InformationStanford University, Stanford, California USAStanford University, Stanford, California USA

http://cll.stanford.edu/http://cll.stanford.edu/

Intelligent Behavior inIntelligent Behavior inHumans and MachinesHumans and Machines

Thanks to Herbert Simon, Allen Newell, John Anderson, David Nicholas, John Laird, Thanks to Herbert Simon, Allen Newell, John Anderson, David Nicholas, John Laird, Randy Jones, and many others for discussions that led to the ideas presented in this talk. Randy Jones, and many others for discussions that led to the ideas presented in this talk.

Early AI was closely linked to the study of human cognition. Early AI was closely linked to the study of human cognition.

This alliance produced many ideas that have been crucial to the This alliance produced many ideas that have been crucial to the field’s long-term development. field’s long-term development.

Over the past 20 years, that connection has largely been broken, Over the past 20 years, that connection has largely been broken, which has hurt our ability to pursue two of AI's original goals: which has hurt our ability to pursue two of AI's original goals:

to understand the nature of the human mindto understand the nature of the human mind

to achieve artifacts that exhibit human-level intelligenceto achieve artifacts that exhibit human-level intelligence

Re-establishing the connection to psychology would help achieve Re-establishing the connection to psychology would help achieve these challenging objectives. these challenging objectives.

Basic ClaimsBasic Claims

Review of early AI accomplishments that benefited from Review of early AI accomplishments that benefited from connections to cognitive psychologyconnections to cognitive psychology

Examples of AI's current disconnection from psychology Examples of AI's current disconnection from psychology and some reasons behind this unfortunate developmentand some reasons behind this unfortunate development

Ways that AI can benefit from renewed links to psychologyWays that AI can benefit from renewed links to psychology

Research on cognitive architectures as a promising avenueResearch on cognitive architectures as a promising avenue

Steps we can take to encourage research along these linesSteps we can take to encourage research along these lines

Outline of the TalkOutline of the Talk

As AI emerged in the 1950s, one central insight was that As AI emerged in the 1950s, one central insight was that computers might reproduce the complex cognition of humans.computers might reproduce the complex cognition of humans.

Some took human intelligence as an inspiration without trying Some took human intelligence as an inspiration without trying to model the details. to model the details.

Others, like Herb Simon and Allen Newell, viewed themselves Others, like Herb Simon and Allen Newell, viewed themselves as psychologists aiming to explain human thought.as psychologists aiming to explain human thought.

This paradigm was pursued vigorously at Carnegie Tech, and it This paradigm was pursued vigorously at Carnegie Tech, and it was respected elsewhere. was respected elsewhere.

The approach was well represented in the early edited The approach was well represented in the early edited volume volume Computers and ThoughtComputers and Thought. .

Early Links Between AI and PsychologyEarly Links Between AI and Psychology

Early Research on Knowledge RepresentationEarly Research on Knowledge Representation

Much initial work on representation dealt with the structure Much initial work on representation dealt with the structure and organization of human knowledge: and organization of human knowledge:

Hovland and Hunt's (1960) CLSHovland and Hunt's (1960) CLS

Feigenbaum's (1963) EPAMFeigenbaum's (1963) EPAM

Quillian's (1968) semantic networksQuillian's (1968) semantic networks

Schank and Abelson's (1977) scriptsSchank and Abelson's (1977) scripts

Newell's (1973) production systemsNewell's (1973) production systems

Not all research was motivated by concerns withNot all research was motivated by concerns withpsychology, but it had a strong impact on the field. psychology, but it had a strong impact on the field.

Early Research on Problem SolvingEarly Research on Problem Solving

Studies of human problem solving also influenced early AI Studies of human problem solving also influenced early AI research: research:

Newell, Shaw, and Simon’s (1958) Logic TheoristNewell, Shaw, and Simon’s (1958) Logic Theorist

Newell, Shaw, and Simon’s (1961) General Problem SolverNewell, Shaw, and Simon’s (1961) General Problem Solver

DeGroot’s (1965) discovery of progressive deepeningDeGroot’s (1965) discovery of progressive deepening

VanLehn’s (1980) analysis of impasse-driven errorsVanLehn’s (1980) analysis of impasse-driven errors

Psychological studies led to key insights about both state-space Psychological studies led to key insights about both state-space and goal-directed heuristic search. and goal-directed heuristic search.

Initial Paper on the Logic TheoristInitial Paper on the Logic Theorist

Early Research on Knowledge-Based ReasoningEarly Research on Knowledge-Based Reasoning

The 1980s saw many developments in knowledge-based The 1980s saw many developments in knowledge-based reasoning that incorporated ideas from psychology: reasoning that incorporated ideas from psychology:

expert systems (e.g., Waterman, 1986)expert systems (e.g., Waterman, 1986)

qualitative physics (e.g., Kuipers, 1984; Forbus, 1984)qualitative physics (e.g., Kuipers, 1984; Forbus, 1984)

model-based reasoning (e.g., Gentner & Stevens, 1983)model-based reasoning (e.g., Gentner & Stevens, 1983)

analogical reasoning (e.g., Gentner & Forbus, 1991)analogical reasoning (e.g., Gentner & Forbus, 1991)

Research on natural language also borrowed many ideas from Research on natural language also borrowed many ideas from studies of structural linguistics. studies of structural linguistics.

Early Research on Learning and DiscoveryEarly Research on Learning and Discovery

Many AI systems also served as models of human learning andMany AI systems also served as models of human learning anddiscovery processes: discovery processes: categorization (Hovland & Hunt, 1960; Feigenbaum, 1963; categorization (Hovland & Hunt, 1960; Feigenbaum, 1963;

Fisher, 1987)Fisher, 1987) problem solving (Anzai & Simon, 1979; Anderson, 1981; problem solving (Anzai & Simon, 1979; Anderson, 1981;

Minton et al., 1989; Jones & VanLehn, 1994)Minton et al., 1989; Jones & VanLehn, 1994) natural language (Reeker, 1976; Anderson, 1977; Berwick, natural language (Reeker, 1976; Anderson, 1977; Berwick,

1979, Langley, 1983)1979, Langley, 1983) scientific discovery (Lenat, 1977; Langley, 1979)scientific discovery (Lenat, 1977; Langley, 1979)

This work reflected the diverse forms of knowledge supportedThis work reflected the diverse forms of knowledge supportedby human learning and discovery. by human learning and discovery.

The Unbalanced State of Modern AIThe Unbalanced State of Modern AI

Unfortunately, AI has moved away from modeling human Unfortunately, AI has moved away from modeling human cognition and become unfamiliar with results from psychology.cognition and become unfamiliar with results from psychology.

Despite the historical benefits, many AI researchers now believe Despite the historical benefits, many AI researchers now believe psychology has little to offer it.psychology has little to offer it.

Similarly, few psychologists believe that results from AI are Similarly, few psychologists believe that results from AI are relevant to modeling human behavior. relevant to modeling human behavior.

This shift has taken place in a number of research areas, and it This shift has taken place in a number of research areas, and it has occurred for a number of reasons. has occurred for a number of reasons.

Current Emphases in AI ResearchCurrent Emphases in AI Research

Knowledge representationKnowledge representation focus on restricted logics that guarantee efficient processingfocus on restricted logics that guarantee efficient processing less flexibility and power than observed in human reasoningless flexibility and power than observed in human reasoning

Problem solving and planningProblem solving and planning partial-order and, more recently, disjunctive plannerspartial-order and, more recently, disjunctive planners bear little resemblance to problem solving in humansbear little resemblance to problem solving in humans

Natural language processingNatural language processing statistical methods with few links to psycho/linguistics statistical methods with few links to psycho/linguistics focus on tasks like information retrieval and extractionfocus on tasks like information retrieval and extraction

Machine learningMachine learning statistical techniques that learn far more slowly than humansstatistical techniques that learn far more slowly than humans almost exclusive focus on classification and reactive controlalmost exclusive focus on classification and reactive control

Technological Reasons for the ShiftTechnological Reasons for the Shift

One reason revolves around faster computer processors and One reason revolves around faster computer processors and larger memories, which have made possible new methods for:larger memories, which have made possible new methods for:

playing games by carrying out far more search than humansplaying games by carrying out far more search than humans

finding complicated schedules that trade off many factorsfinding complicated schedules that trade off many factors

retrieving relevant items from large document repositoriesretrieving relevant items from large document repositories

inducing complex predictive models from large data setsinducing complex predictive models from large data sets

These are genuine scientific advances, but AI might fare even These are genuine scientific advances, but AI might fare even better by incorporating insights from human behavior. better by incorporating insights from human behavior.

Formalist Trends in Computer ScienceFormalist Trends in Computer Science

Another factor involves AI’s typical home in departments of Another factor involves AI’s typical home in departments of computer science: computer science:

which often grew out of mathematics departmentswhich often grew out of mathematics departments

where analytical tractability is a primary concernwhere analytical tractability is a primary concern

where guaranteed optimality trumps heuristic satisficingwhere guaranteed optimality trumps heuristic satisficing

even when this restricts work to narrow problem classeseven when this restricts work to narrow problem classes

Many AI faculty in such organizations view connections to Many AI faculty in such organizations view connections to psychology with intellectual suspicion. psychology with intellectual suspicion.

Commercial Success of AICommercial Success of AI

Another factor has been AI’s commercial success, which has: Another factor has been AI’s commercial success, which has:

led many academics to study narrowly defined tasksled many academics to study narrowly defined tasks

produced a bias toward near-term applicationsproduced a bias toward near-term applications

caused an explosion of work on “niche AI”caused an explosion of work on “niche AI”

Moreover, component algorithms are much easier to evaluate Moreover, component algorithms are much easier to evaluate experimentally, especially given available repositories. experimentally, especially given available repositories.

Such focused efforts are appropriate for corporate AI labs, but Such focused efforts are appropriate for corporate AI labs, but academic researchers should aim for higher goals. academic researchers should aim for higher goals.

Benefits: Understanding Human CognitionBenefits: Understanding Human Cognition

One reason for renewed interchange between the two fields is One reason for renewed interchange between the two fields is to understand the nature of human cognition: to understand the nature of human cognition:

because this would have important societal applications in because this would have important societal applications in education, interface design, and other areas; education, interface design, and other areas;

because human intelligence comprises an important set of because human intelligence comprises an important set of phenomena that demand scientific explanation. phenomena that demand scientific explanation.

This remains an open and challenging problem, and AI This remains an open and challenging problem, and AI systemssystems

remain the best way to tackle it.remain the best way to tackle it.

Benefits: Source of Challenging TasksBenefits: Source of Challenging Tasks

Another reason is that observations of human abilities serve as Another reason is that observations of human abilities serve as an important source of challenges, such as: an important source of challenges, such as:

understanding language at a deeper level than current systemsunderstanding language at a deeper level than current systems

interleaving planning with execution in pursuit of many goalsinterleaving planning with execution in pursuit of many goals

learning complex knowledge structures from few experienceslearning complex knowledge structures from few experiences

carrying out creative activities in art and sciencecarrying out creative activities in art and science

Most work in AI sets its sights too low by focusing on tasks that Most work in AI sets its sights too low by focusing on tasks that hardly involve intelligence. hardly involve intelligence.

Psychological studies reveal the impressive abilities of human Psychological studies reveal the impressive abilities of human cognition and pose new problems for AI research. cognition and pose new problems for AI research.

Benefits: Constraints on Intelligent ArtifactsBenefits: Constraints on Intelligent Artifacts

how the system can represent and organize knowledge; how the system can represent and organize knowledge;

how the system can use that knowledge in performance; how the system can use that knowledge in performance;

how the system can acquire knowledge from experience. how the system can acquire knowledge from experience.

To develop intelligent systems, we must constrain their design, To develop intelligent systems, we must constrain their design, and findings about human behavior can suggest: and findings about human behavior can suggest:

Some of the most interesting AI research uses psychological Some of the most interesting AI research uses psychological ideas as design heuristics, including abilities we do ideas as design heuristics, including abilities we do notnot need need (e.g., to carry out rapid and extensive search). (e.g., to carry out rapid and extensive search).

Humans remain our only example of general intelligent systems, Humans remain our only example of general intelligent systems, and insights about their operation deserve serious consideration. and insights about their operation deserve serious consideration.

AI and Cognitive SystemsAI and Cognitive Systems

move beyond isolated phenomena and capabilities to develop move beyond isolated phenomena and capabilities to develop complete models of intelligent behavior; complete models of intelligent behavior;

develop cognitive systems that make strong theoretical claims develop cognitive systems that make strong theoretical claims about the nature of the mind; about the nature of the mind;

view cognitive psychology and artificial intelligence as close view cognitive psychology and artificial intelligence as close allies with distinct but related goals. allies with distinct but related goals.

In 1973, Allen Newell argued “In 1973, Allen Newell argued “You can’t play twenty questions You can’t play twenty questions with nature and winwith nature and win”. Instead, he proposed that we: ”. Instead, he proposed that we:

Newell claimed that a successful framework should provide a Newell claimed that a successful framework should provide a unifiedunified theory of intelligent behavior. theory of intelligent behavior.

He associated these aims with the idea of a He associated these aims with the idea of a cognitive architecturecognitive architecture..

Assumptions of Cognitive ArchitecturesAssumptions of Cognitive Architectures

Most cognitive architectures incorporate a variety of assumptions Most cognitive architectures incorporate a variety of assumptions from psychological theories: from psychological theories:

These claims are shared by a variety of architectures, including These claims are shared by a variety of architectures, including ACT-R, Soar, Prodigy, and IACT-R, Soar, Prodigy, and ICARUSCARUS. .

1.1. Short-term memories are distinct from long-term stores Short-term memories are distinct from long-term stores

2.2. Memories contain modular elements cast as symbolic structuresMemories contain modular elements cast as symbolic structures

3.3. Long-term structures are accessed through pattern matchingLong-term structures are accessed through pattern matching

4.4. Cognition occurs in retrieval/selection/action cyclesCognition occurs in retrieval/selection/action cycles

5.5. Performance and learning compose elements in memoryPerformance and learning compose elements in memory

each element in a short-term memory is an active version of each element in a short-term memory is an active version of some structure in long-term memory; some structure in long-term memory;

many mental structures are relational in nature, in that they many mental structures are relational in nature, in that they describe connections or interactions among objects; describe connections or interactions among objects;

concepts and skills encode different aspects of knowledge concepts and skills encode different aspects of knowledge that are stored as distinct cognitive structures;that are stored as distinct cognitive structures;

long-term memories have hierarchical organizations that long-term memories have hierarchical organizations that define complex structures in terms of simpler ones. define complex structures in terms of simpler ones.

Ideas about RepresentationIdeas about Representation

Cognitive psychology makes important representational claims: Cognitive psychology makes important representational claims:

Many architectures adopt these assumptions about memory. Many architectures adopt these assumptions about memory.

In addition, a cognitive architecture makes commitments about:In addition, a cognitive architecture makes commitments about:

performance processesperformance processes for: for: retrieval, matching, and selectionretrieval, matching, and selection inference and problem solvinginference and problem solving perception and motor controlperception and motor control

learning processeslearning processes that: that: generate new long-term knowledge structuresgenerate new long-term knowledge structures refine and modulate existing structuresrefine and modulate existing structures

Architectural Commitment to ProcessesArchitectural Commitment to Processes

In most cognitive architectures, performance and learning are In most cognitive architectures, performance and learning are tightly intertwined, again reflecting influence from psychology. tightly intertwined, again reflecting influence from psychology.

humans often resort to problem solving and search to solve humans often resort to problem solving and search to solve novel, unfamiliar problems; novel, unfamiliar problems;

problem solving depends on mechanisms for retrieval and problem solving depends on mechanisms for retrieval and matching, which occur rapidly and unconsciously;matching, which occur rapidly and unconsciously;

people use heuristics to find satisfactory solutions, rather people use heuristics to find satisfactory solutions, rather than algorithms to find optimal ones; than algorithms to find optimal ones;

problem solving in novices requires more cognitive resources problem solving in novices requires more cognitive resources than experts’ use of automatized skills. than experts’ use of automatized skills.

Ideas about PerformanceIdeas about Performance

Many architectures embody these ideas about performance.Many architectures embody these ideas about performance.

Cognitive psychology makes clear claims about performance: Cognitive psychology makes clear claims about performance:

efforts to overcome impasses during problem solving can lead efforts to overcome impasses during problem solving can lead to new cognitive structures; to new cognitive structures;

learning can transform backward-chaining heuristic search learning can transform backward-chaining heuristic search into forward-chaining behavior; into forward-chaining behavior;

learning is incremental and interleaved with performance; learning is incremental and interleaved with performance; structural learning involves monotonic addition of symbolic structural learning involves monotonic addition of symbolic

elements to long-term memory; elements to long-term memory; transfer to new tasks depends on the amount of structure transfer to new tasks depends on the amount of structure

shared with previously mastered tasks. shared with previously mastered tasks.

Ideas about LearningIdeas about Learning

Cognitive psychology has also developed ideas about learning: Cognitive psychology has also developed ideas about learning:

Architectures often incorporate these ideas into their operation. Architectures often incorporate these ideas into their operation.

Cognitive architectures come with a programming language that:Cognitive architectures come with a programming language that:

includes a syntax linked to its representational assumptionsincludes a syntax linked to its representational assumptions inputs long-term knowledge and initial short-term elementsinputs long-term knowledge and initial short-term elements provides an interpreter that runs the specified programprovides an interpreter that runs the specified program incorporates tracing facilities to inspect system behaviorincorporates tracing facilities to inspect system behavior

Architectures as Programming LanguagesArchitectures as Programming Languages

Such programming languages ease construction and debugging Such programming languages ease construction and debugging of knowledge-based systems.of knowledge-based systems.

Thus, ideas from psychology can support efficient development Thus, ideas from psychology can support efficient development of software for intelligent systems. of software for intelligent systems.

Responses: Broader AI EducationResponses: Broader AI Education

Most current AI courses ignore the field’s history; we need a Most current AI courses ignore the field’s history; we need a broader curriculum that covers its connections to: broader curriculum that covers its connections to:

cognitive psychologycognitive psychology

structural linguisticsstructural linguistics

logical reasoninglogical reasoning

philosophy of mindphilosophy of mind

These areas are more important to AI’s original agenda than are These areas are more important to AI’s original agenda than are ones from mainstream computer science. ones from mainstream computer science.

For one example, see http://cll.stanford.edu/reason-learn/ , a For one example, see http://cll.stanford.edu/reason-learn/ , a course I have offered for the past three years. course I have offered for the past three years.

Responses: Funding InitiativesResponses: Funding Initiatives

makes contact with ideas from computational psychology makes contact with ideas from computational psychology

addresses the same range of tasks that humans can handleaddresses the same range of tasks that humans can handle

develops integrated cognitive systems that move beyond develops integrated cognitive systems that move beyond component algorithmscomponent algorithms

In recent years, DARPA and NSF have taken promising steps In recent years, DARPA and NSF have taken promising steps in this direction, with clear effects on the community. in this direction, with clear effects on the community.

However, we need more funding programs along these lines. However, we need more funding programs along these lines.

We also need funding to support additional AI research that: We also need funding to support additional AI research that:

Responses: Publication VenuesResponses: Publication Venues

AAAI’s new track for integrated intelligent systems AAAI’s new track for integrated intelligent systems

this year’s Spring Symposium on AI meets Cognitive Sciencethis year’s Spring Symposium on AI meets Cognitive Science

the special issue of the special issue of AI MagazineAI Magazine on human-level intelligence on human-level intelligence

We need more outlets of this sort, but recent events have been We need more outlets of this sort, but recent events have been moving the field in the right direction. moving the field in the right direction.

We also need places to present work in this paradigm, such as: We also need places to present work in this paradigm, such as:

Closing RemarksClosing Remarks

In summary, AI’s original vision was to understand the basis In summary, AI’s original vision was to understand the basis of intelligent behavior in humans and machines. of intelligent behavior in humans and machines.

Many early systems doubled as models of human cognition, Many early systems doubled as models of human cognition, while others made effective use of ideas from psychology. while others made effective use of ideas from psychology.

Recent years have seen far less research in this tradition, with Recent years have seen far less research in this tradition, with AI becoming a set of narrow, specialized subfields. AI becoming a set of narrow, specialized subfields.

Re-establishing contact with ideas from psychology, including Re-establishing contact with ideas from psychology, including work on cognitive architectures, can remedy this situation. work on cognitive architectures, can remedy this situation.

The next 50 years must see AI return to its psychological roots The next 50 years must see AI return to its psychological roots if it hopes to achieve human-level intelligence. if it hopes to achieve human-level intelligence.

Closing DedicationClosing Dedication

Allen Newell (1927 – 1992) Herbert Simon (1916 – 2001)Allen Newell (1927 – 1992) Herbert Simon (1916 – 2001)

I would like to dedicate this talk to two of AI’s founding fathers:I would like to dedicate this talk to two of AI’s founding fathers:

Both contributed to the field in many ways: posing new problems, Both contributed to the field in many ways: posing new problems, inventing methods, writing key papers, and training students.inventing methods, writing key papers, and training students.

They were both interdisciplinary researchers who contributed not They were both interdisciplinary researchers who contributed not only to AI but to other disciplines, including psychology.only to AI but to other disciplines, including psychology.

Allen Newell and Herb Simon were excellent role models who we Allen Newell and Herb Simon were excellent role models who we should all aim to emulate. should all aim to emulate.