Transcript Computational Discovery of Communicable Knowledge

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
[email protected]
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.
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.
power
This would produce a broader research agenda that would take the field
into unexplored regions.
niche AI
cognitive
systems
generality
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.
performance
We need more work on learning from few cases in the presence of
background knowledge.
experience
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
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
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
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
End of Presentation