Transcript Computational Discovery of Communicable Knowledge

Machine Learning for
Cognitive Networks
Pat Langley
Computational Learning Laboratory
Center for the Study of Language and Information
Stanford University, Stanford, California
http://cll.stanford.edu/~langley/
Thanks to Chris Ramming and Tom Dietterich for discussions that led to many of these ideas.
Definition of a Machine Learning System
a software
artifact
that improves
task performance
based on partial
task experience
by acquiring
knowledge
Elements of a Machine Learning System
performance
element
environment
knowledge
learning
algorithm
Five Representational Paradigms
decision
trees
logical
rules
probabilistic
formalisms
neural
networks
case
libraries
Three Formulations of Learning Problems
A more basic decision than choice of representational framework
is whether one formulates the problem as:
 Learning for classification and regression ;
 Learning for action and planning ; or
 Learning for interpretation and understanding .
These paradigms differ in their performance task, i.e., the manner
in which the learned knowledge is utilized.
Learning for Classification and Regression
Learned knowledge can be used to classify a new instance or to
predict the value for one of its numeric attributes, as in:
 Supervised learning – from labeled training cases
 Unsupervised learning – from unlabeled training cases
 Semi-supervised learning – from partly labeled cases
These are most basic, best-studied induction tasks, which has led
to development of robust algorithms for them.
Such methods have been used in many successful applications, and
they form the backbone of commercial data-mining systems.
Learning for Action and Planning
Learned knowledge can be used to decide which action to execute
or which choice to make during problem solving, as in:
 Adaptive interfaces – learn from interaction with user
 Behavioral cloning – learn from behavioral traces
 Empirical optimization – from varying control parameters
 Reinforcement learning – from delayed reward signals
 Learning from problem solving – from the results of search
Progress on these formulations is at different stages, with some
used in commerce and others needing more basic research.
Learning for Understanding
Learned knowledge can be used to interpret, understand, or explain
situations or events, as in:
 Structured induction –from trainer-explained instances
 Constructive induction – from self-explained training cases
 Generative induction – learn structures needed for explanation
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 Parameter estimation – from training cases given structures
 Theory revision – revise structures based on training cases
Research in these frameworks is less mature than others, but holds
great potential for combining learning with reasoning.
Comments about Problem Formulations
With respect to the Knowledge Plane, it is important to realize that
one can view a given task in different ways.
For example, one can formulate diagnostic problems as either:
 Supervised learning from labeled examples of network faults
 Unsupervised learning from anomalous network behaviors
 Behavioral cloning from traces of network manager’s responses
 Reinforcement learning from experience with sensing actions
 Constructive induction from explanations of network faults
We need measures of progress that focus on networking rather than
to specific problem formulations.
Challenges in Experimental Evaluation
To evaluate learning methods for the Knowledge Plane, we need:
 Dependent measures – related to network management tasks
 Independent variables
 Amount of experience – to determine rate of learning
 Complexity of task and data – to determine robustness
 System modules and knowledge – to infer sources of power
 Data sets and test beds – to support the experimental process
The goal of experimentation is to promote scientific understanding,
not to show that one method is better than another.