Computational%20Discovery%20of%20Communicable%20Knowledge

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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.
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Definition of a Machine Learning System
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Elements of a Machine Learning System
performance element
environment
knowledge
learning algorithm
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Five Representational Paradigms
decision trees
neural networks
logical rules
probabilistic formalisms
case libraries
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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.
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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.
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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.
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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
• 
  
• 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.
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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
  managers 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.
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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.