D2.1 Requirements and Specification - CORBYS

D2.1 Requirements and Specification
Problem
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Retention
Solution
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Retrieval
Figure 1: CBR Process
Case Based Repository
1. The problem is entered into the system and analysed.
2. The case which closest matches the details of the problem is selected.
3. This case is modified to better fit the problem using predefined rules. This becomes the solution
which is presented.
4. The solution is analysed for its effect and is stored for future use along with an indication of its
successfulness. This allows the system to learn.
The system should be able to learn not only from its successful solutions, but also from its failed solutions
(success driven and failure driven learning). Successful solutions can be stored so that the solution can be
reused without regenerating it. Solutions which failed to solve the problem can be used to generate better
solutions for use at a later stage. CBR can be used to identify situations presented as cases from a repository
of known situations; however, the mapping of a situation as a case and subsequent measurement of similarity
between two cases poses a problem. Furthermore, the existence of an associated solution becomes irrelevant
as soon as the situation is matched to a template or a case from the repository.
11.3.4 Bayesian Networks
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Selection
Bayesian networks are probabilistic graphical models that represent a set of random variables and their
conditional dependencies via directed acyclic graphs. Bayesian networks are commonly used for probabilistic
reasoning in the context of situation assessment (Das et al. 2002; Bladon et al. 2002; Higgins 2005). Bayesian
networks are based on Bayes theorem which computes posterior or inverse probability for a proposition i.e.,
given the prior or unconditional probabilities of A and B, and knowing the conditional probability of B given
A, what is the conditional probability of A given B? Bayesian nodes in the networks represent propositions,
random variables, unknown parameters or hypotheses. Nodes are connected by edges, that represent
conditional dependency, and unconnected nodes therefore represent variables that are conditionally
independent. Each node has an associated probability function that gives the variable probability represented
by the node, upon receiving a set of values as input. Priori probabilities and conditional probabilities have to
be specified for each node in the network. Inverse probabilities for each node can then be computed using
Bayes rule as new input is received into the network.
In the context of situation assessment, Das et al. (2002) lists two important points that must be observed when
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Application of
rules to modify
selected cases
Figure 27: Case-Based Reasoning process
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Presentation