old.ipto-2004.ppt
- 1. Machine Learning for Cognitive Systems 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/~langley langley@csli.stanford.edu The views contained in these slides are the author’s 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 The field of machine learning has many success stories, but: these successes are prime examples of niche AI, which develops techniques that are increasingly powerful but that apply to an ever narrower classes of problems. Instead, we need a new vision for machine learning technology that: supports the construction of general intelligent systems; aspires to the same learning abilities as appear in humans. This would produce a broader research agenda that would take the field into unexplored regions. power niche AI cognitive systems generality
- 3. Challenge 1: Rapid Learning Current learning research focuses on asymptotic behavior: methods for learning classifiers from thousands of cases; methods that converge on optimal controllers in the limit. In contrast, humans are typically able to: learn reasonable behavior from relatively few cases; take advantage of knowledge to speed the learning process. We need more work on learning from few cases in the presence of background knowledge. performance experience
- 4. Challenge 2: Cumulative Learning Current learning research focuses on isolated induction tasks that: take no advantage of what has been learned before; provide no benefits for what is learned afterwards. In contrast, much human learning involves: incremental acquisition of knowledge over time that builds on knowledge acquired during earlier episodes. We need much more research on such cumulative learning. initial knowledge extended knowledge
- 5. Challenge 3: Varied Learning Current learning research emphasizes tasks like classification and reactive control, whereas humans learn: grammars for understanding natural language; heuristics for reasoning and problem solving; scripts and procedures for routine behavior; cognitive maps for localization and navigation; models that explain the behavior of artifacts. We need more work on learning such varied knowledge structures. human learning abilities current focus of machine learning
- 6. Challenge 4: Compositional Learning Current learning research focuses on performance tasks that: involve one-step decisions for classification or regression; utilize simple reactive control for acting in the world. But many other varieties of learning instead involve: the acquisition of modular knowledge elements that can be composed dynamically by multi-step reasoning. We should give more attention to learning such compositional knowledge. knowledge reasoning knowledge reasoning
- 7. Challenge 5: Evaluating Embedded Learning Current evaluation emphasizes static data sets for isolated tasks that: favor work on minor refinements of existing component algorithms; encourage mindless “bake offs” that provide little understanding. To support the evaluation of embedded learning systems, we need: a set of challenging environments that exercise learning and reasoning, that include performance tasks of graded complexity and difficulty, and that have real-world relevance but allow systematic experimental control. battle management in-city driving air reconnaissance