Pat Langley Computational Learning Laboratory Center for the Study of Language and Information
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Pat Langley Pat Langley Computational Learning Laboratory Computational Learning Laboratory Center for the Study of Language and Information Center for the Study of Language and Information Stanford University, Stanford, California Stanford University, Stanford, California http://cll.stanford.edu/ http://cll.stanford.edu/ Architectures for Adaptive Architectures for Adaptive Interpretation Interpretation
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Architectures for Adaptive Interpretation. Pat Langley Computational Learning Laboratory Center for the Study of Language and Information Stanford University, Stanford, California http://cll.stanford.edu/. Characteristics of Cognitive Architectures. - PowerPoint PPT Presentation
Transcript of Pat Langley Computational Learning Laboratory Center for the Study of Language and Information
Pat LangleyPat LangleyComputational Learning LaboratoryComputational Learning Laboratory
Center for the Study of Language and InformationCenter for the Study of Language and InformationStanford University, Stanford, CaliforniaStanford University, Stanford, California
http://cll.stanford.edu/http://cll.stanford.edu/
Architectures for Adaptive InterpretationArchitectures for Adaptive Interpretation
As typically defined and utilized, a cognitive architecture:As typically defined and utilized, a cognitive architecture:
aims to demonstrate generality and flexibility, rather than aims to demonstrate generality and flexibility, rather than success on a single application domain;success on a single application domain;
specifies the infrastructure that holds constant over domains;specifies the infrastructure that holds constant over domains;
focuses on functional structures and processes, rather than on focuses on functional structures and processes, rather than on the knowledge or implementation levels;the knowledge or implementation levels;
commits to representations and organizations of knowledge;commits to representations and organizations of knowledge;
comes with a programming language for encoding knowledge comes with a programming language for encoding knowledge and constructing intelligent systems.and constructing intelligent systems.
Characteristics of Cognitive ArchitecturesCharacteristics of Cognitive Architectures
These design principles apply not only to architectures focused on These design principles apply not only to architectures focused on action, but also to ones focused on explanation and understanding.action, but also to ones focused on explanation and understanding.
Most previous architectures, like Soar, ACT-R, Prodigy, and 3T Most previous architectures, like Soar, ACT-R, Prodigy, and 3T were designed for action. As a result:were designed for action. As a result:
Short-term beliefs focus on goals and plans;Short-term beliefs focus on goals and plans; Long-term knowledge focuses on skills and procedures;Long-term knowledge focuses on skills and procedures; Inference is implemented with one-way production rules.Inference is implemented with one-way production rules.
To support flexible understanding and explanation, we need an To support flexible understanding and explanation, we need an alternative class of architectures in which:alternative class of architectures in which:
Beliefs focus on inferences made from knowledge and facts;Beliefs focus on inferences made from knowledge and facts; An episodic belief memory replaces short-term memory; An episodic belief memory replaces short-term memory; Knowledge exists to generate accurate and useful inferences;Knowledge exists to generate accurate and useful inferences; Inference involves flexible abduction rather than deduction. Inference involves flexible abduction rather than deduction.
Architectures for Action and InterpretationArchitectures for Action and Interpretation
We need architectures of this sort for robust learning by reading. We need architectures of this sort for robust learning by reading.
Interpretive Architecture: Structures and ProcessesInterpretive Architecture: Structures and Processes
Old GeneralOld GeneralKnowledgeKnowledge
LearningLearningand Revisionand Revision
FixedFixedParserParser
Inference andInference andInterpretationInterpretation
New GeneralNew GeneralKnowledgeKnowledge
New SpecificNew SpecificBeliefsBeliefs
Old SpecificOld SpecificBeliefsBeliefs
StatementsStatementsand Questionsand Questions
FixedFixedGeneratorGenerator
Answers andAnswers andSummariesSummaries
For each input I, For each input I, 1. Use the fixed parser to generate new beliefs. 1. Use the fixed parser to generate new beliefs. 2. Update the old beliefs by incorporating the new beliefs. 2. Update the old beliefs by incorporating the new beliefs. 3. Use the interpreter to infer additional new beliefs. 3. Use the interpreter to infer additional new beliefs. 4. If not yet done, go to step 3; else go to step 5. 4. If not yet done, go to step 3; else go to step 5. 5. Use the learner to add or revise knowledge structures. 5. Use the learner to add or revise knowledge structures. 6. If not yet done, do to step 5; else go to step 7. 6. If not yet done, do to step 5; else go to step 7. 7. Use the fixed generator to produce output. 7. Use the fixed generator to produce output.
Basic Interpretive CycleBasic Interpretive Cycle
Inference is driven by new facts or questions, but guided by old Inference is driven by new facts or questions, but guided by old beliefs and knowledge; learning is driven by new beliefs but beliefs and knowledge; learning is driven by new beliefs but biased by old knowledge and beliefs. biased by old knowledge and beliefs.
This leaves open key questions about the eagerness/laziness of This leaves open key questions about the eagerness/laziness of inference, the size of input/output units, and the degree to which inference, the size of input/output units, and the degree to which learning and inference are interleaved.learning and inference are interleaved.
