Intelligent Systems CSCI 6501 Dr' D' Riordan

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Intelligent SystemsCSCI 6501Dr. D. Riordan

• Pradeep Monga (B00342080)
• Satwant Sandhu (B00201045)

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• Literature Survey
• Structure for Credit-Apportionment Problem in
  Rule Based Systems

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• Overview
• In this Project we are concerned with
  Credit-Apportionment
• Problem in Rule Based Systems.
• We will use Bucket Brigade Algorithm in designing
  an expert
• system called GAMBLE (Genetic Algorithm
  Based Machine
• Learning Expert).
• The various research papers that we have gone
  through are part of
  IEEE digital library research papers
  along with some other papers
• available on internet accessible to all.
  
• The first Document that we referred was regarding
  the explanation of Credit-Apportionment in
  rule based systems. The paper is available in
  IEEE digital libraries IEEE Transactions on
  Systems, Man and Cybernetics. Vol 19. No 3
  May/June 1989 This paper talks about a framework
  of Credit-Apportionment and then defines what
  actually Credit-Apportionment is.
• The next reference paper is the research paper on
  Genetic Algorithm Based Machine Learning Expert
  system Indian Institute of Technology, Roorkee
  India 2001

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• The Credit-Apportionment process provides a
  formal basis for the problem analysis and
  algorithm design. It includes
• (1) System Environment sub - model which provides
  integrated view about the payoff to, as well as
  the external and internal aspects of rule based
  system,
• (2) Principles of Usefulness, which define the
  usefulness of rule actions
• (3) Definitions of the Credit-Apportionment
  problem which guides the algorithm synthesis.

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• System Environment sub model
• The environment is modeled as a state space
  consisting of a set of states and a state
  transition function. The state transition
  function specifies the next state as a function
  or a random variable of the current state and the
  output of the rule based system. The internal
  model or mental model of a rule based system is
  the model for the systems knowledge about its
  external world. The mental model is represented
  as a state space. The states in the space are
  sets of messages in the working memory. The
  environment is linked to the mental model with
  the help of payoff. Payoff is the one way bridge
  sending feedback information from the environment
  to mental model. Hence a rule based system is
  able to be aware of the semantic aspects of the
  activities through payoff.

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• As a semantic activity, usefulness of actions
  conducted by rules is determined by the
  environment. One way in which the environment
  determines the usefulness is expressed, by this
  model as a set of principles of usefulness. Based
  on the system environment sub model and
  principles of usefulness, the Credit-Apportionment
  problem can be formulated as-
• Local level problem
• Estimation of the inherent usefulness
  values in a particular context.
• Global level problem
• As approximation to the inherent usefulness
  functions( ) from the payoffs.

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• A new Credit-Apportionment Algorithm
• As an implementation example of the model,
  a new Credit Apportionment algorithm The context
  array Bucket Brigade Algorithm is used.
• Overcomes the information loss suffered by those
  algorithms using scalar valued strengths and
  hence to improve the estimation and approximation
  on rule inherent usefulness.
• Uses a set of domain-independent or
  domain-dependent context variables to partition
  the contexts into context chunks.
• Algorithm employs array valued strengths to
  estimate the inherent usefulness of rule actions
  under different context chunks.
• Using a set of context variables to partition the
  contexts into chunks, and by processing
  Credit-Apportionment on the context chunk level,
  array valued strength is inevitably able to
  provide a better approximation of the inherent
  usefulness function than a scalar valued strength
  can.

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• GAMBLE
• This talks about an expert system that is used
  for students who are seeking admission into an
  engineering institute for obtaining a degree in
  bachelors of engineering. After clearing the
  entrance examination the student is advised by
  this system as to which branch would be most
  suited for him. An algorithm is used for branch
  selection.
• All the available seats in every branch are
  calculated and list of available branches is
  formed. Then a branch aptitude total for the
  listed branches is calculated for the listed
  branches. The various parameters like logic,
  imagination, aesthetic sense, concentration
  level, etc are listed along with their weights.
  These are variable parameters that would be
  automatically updated with experience by the
  system. Then there is an idea about the Genetics
  based Machine learning where discussion about
  classifier systems is given along with its major
  3 components.
• Rule and message system
• Apportionment of Credit system
• Genetic algorithm
• The other part also talks about the
  Credit-Apportionment algorithm using the Bucket
  Brigade algorithm, this part of the paper is much
  more deeply related to our project proposal.
  Apportionment of Credit via competition and rule
  discovery using genetic algorithms form a
  reasonable basis for constructing a machine
  learning system atop the computationally
  convenient and complete framework of classifiers.

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Variable weights for parameters for different
branches
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Sample weights of parameters in Computer Science
Students performance distribution