1. Pat Langley Institute for the Study of Learning and Expertise Palo Alto, California and Center for the Study of Language and Information Stanford University, Stanford, California http://cll.stanford.edu/~langleylangley@csli.stanford.edu Machine Learning for Cognitive Systems The views contained in these slides are the authors and do not represent official policies, either Expressed or implied, of the Defense Advanced Research Projects Agency or the DoD.

2. Expanding our Computational Horizons Expanding our Computational Horizons these successes are prime examples of niche AI, which these successes are prime examples of niche AI, which develops techniques that are increasingly powerful develops techniques that are increasingly powerful but that apply to an ever narrower classes of problems. but that apply to an ever narrower classes of problems. The field of machine learning has many success stories, but: supports the construction of general intelligent systems; supports the construction of general intelligent systems; aspires to the same learning abilities as appear in humans. aspires to the same learning abilities as appear in humans. Instead, we need a new vision for machine learning technology that: This would produce a broader research agenda that would take the field into unexplored regions. niche AI cognitive systems generality power

3. Challenge 1: Rapid Learning Challenge 1: Rapid Learning methods for learning classifiers from thousands of cases; methods for learning classifiers from thousands of cases; methods that converge on optimal controllers in the limit. methods that converge on optimal controllers in the limit. Current learning research focuses on asymptotic behavior: learn reasonable behavior from relatively few cases; learn reasonable behavior from relatively few cases; take advantage of knowledge to speed the learning process. take advantage of knowledge to speed the learning process. In contrast, humans are typically able to: We need more work on learning from few cases in the presence of background knowledge. experience performance

4. Challenge 2: Cumulative Learning Challenge 2: Cumulative Learning take no advantage of what has been learned before; take no advantage of what has been learned before; provide no benefits for what is learned afterwards. provide no benefits for what is learned afterwards. Current learning research focuses on isolated induction tasks that: incremental acquisition of knowledge over time that incremental acquisition of knowledge over time that builds on knowledge acquired during earlier episodes. builds on knowledge acquired during earlier episodes. In contrast, much human learning involves: We need much more research on such cumulative learning. initial knowledgeextended knowledge

5. Challenge 3: Varied Learning Challenge 3: Varied Learning Current learning research emphasizes tasks like classification and reactive control, whereas humans learn: grammars for understanding natural language; grammars for understanding natural language; heuristics for reasoning and problem solving; heuristics for reasoning and problem solving; scripts and procedures for routine behavior; scripts and procedures for routine behavior; cognitive maps for localization and navigation; cognitive maps for localization and navigation; models that explain the behavior of artifacts. models that explain the behavior of artifacts. We need more work on learning such varied knowledge structures. current focus of machine learning human learning abilities

6. Challenge 4: Compositional Learning Challenge 4: Compositional Learning involve one-step decisions for classification or regression; involve one-step decisions for classification or regression; utilize simple reactive control for acting in the world. utilize simple reactive control for acting in the world. Current learning research focuses on performance tasks that: the acquisition of modular knowledge elements that the acquisition of modular knowledge elements that can be composed dynamically by multi-step reasoning. can be composed dynamically by multi-step reasoning. But many other varieties of learning instead involve: We should give more attention to learning such compositional knowledge. knowledge reasoning

7. Challenge 5: Evaluating Embedded Learning Challenge 5: Evaluating Embedded Learning favor work on minor refinements of existing component algorithms; favor work on minor refinements of existing component algorithms; encourage mindless bake offs that provide little understanding. encourage mindless bake offs that provide little understanding. Current evaluation emphasizes static data sets for isolated tasks that: a set of challenging environments that exercise learning and reasoning, a set of challenging environments that exercise learning and reasoning, that include performance tasks of graded complexity and difficulty, and that include performance tasks of graded complexity and difficulty, and that have real-world relevance but allow systematic experimental control. that have real-world relevance but allow systematic experimental control. To support the evaluation of embedded learning systems, we need: battle management in-city driving air reconnaissance

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