Dimensions of Machine Learning in Diagnosis
{ Version: Wed Aug 20 17:42:38 EDT 1997
}
- What can trigger learning?
- Failure; Success; Differences between expected and real values
- Need to improve abilities
- What are the elements supporting learning?
- Critiques, praise, estimates, evaluations and advice (internal or external)
- Sequences of diagnosis decisions
- Diagnosis histories, e.g traces of information flow, knowledge exchange, negotiation
- Analyses of failures and conflicting elements (goals, decisions)
- Feedback after completing the diagnosis task
- What might be learned?
- Constraints relating parameters or other elements of the diagnosis
- Dependencies between diagnosis parameters
- Support in favor of or against a decision
- Diagnosis rules, methods and plans
- Analogical associations
- Preferences
- Preconditions and postconditions for rules, actions and tasks
- Consequences of diagnosis decisions
- Types of failures and conflicts
- Heuristics for failure recovery and conflict resolution
- Successful diagnoses and diagnostic processes
- Availability of knowledge for learning
- Direct communication (with the user or another diagnosis system)
- Indirect communication (e.g., between diagnosis systems via a blackboard)
- Record of the state of the diagnosis
- Repositories of diagnosis and interaction histories
- Methods of learning
- Explanation-based learning
- Induction
- Knowledge compilation
- Case-based and analogical learning
- Reinforcement learning
- Genetic algorithms
- Neural networks
- Local vs. Global Learning
- Learning by a diagnosis program
- Learning by a group of diagnosis programs
- Consequences of learning
- Diagnosis improvement
- Improvement of the diagnostic process
Adapted from Grecu & Brown,
Dimensions of Learning in Agent-based Design,
AID'96, June 1996.
Based on http://cs.wpi.edu/~dcb/AID/taxonomy.html