GENERIC TASKS
|
The need for GT evolves from the fact that the level of abstraction of much work in KBS (e.g rules, frames, logic) is too low
to provide a rich vocabulary for knowledge and control. Also, they do not distinguish between different
types of knowledge based reasoning. For example, the task of designing a car would require
a significantly different reasoning strategy than diagnosing a malfunction in a car.
The conventional approaches apply the same strategy for two problems. Thus, these
strategies are low level w.r.t modeling the needed task-level behavior.
Generic tasks (GTs) are one example of higher level methodology of KBS.
The Generic task approach proposes that knowledge systems should be built of building
blocks, each of which is appropriate for a basic type of problem solving.
This is based on the proposition that a problem may occur in a number of domains and that
demonstrate similarities in the methods that are domain independent. Thus, to solve a
problem, a knowledge engineer needs only choose a GT that is best suited for performing
a particular function, or can use different GTs for performing the same function. or can
use a combination of them. So, GT facilitates knowledge acquisition because once the KE
selects the GT that he will use, his orientation while collecting knowledge will be close
to that of the GT (GT.proposes a methodology that helps in analysis, design, and construction
of a practical knowledge system). Some GTs have already been defined and developed such as
hierarchical classification(HC), structure matchers (Hypothesis matching), routine design (RD)
, Knowledge directed Information passing(IDB), and Abductive hypothesis assembly.
|
|
| Specifications of Generic Tasks |
Each generic task uses forms of knowledge and control strategies that are characteristic
to it, and are in general conceptually closer to domain knowledge. Each generic task is
characterized by information about the following:
- The kind of information it takes as input for the task and the information produced as a result of performing the task.
- The method used to represent and organize knowledge within the generic task. In particular, the specification of concept types that are involved in the generic task. Also, the specification of how knowledge will be organized in terms of concepts.
- The process (algorithm, problem solving, control, strategy) that the task uses. In other words, the specification of the inference strategy which can be applied to the knowledge in order to accomplish the function of the generic task.
|
| Generic Task Examples |
Hierarchical Classification:
Input: Given a situation description in terms of features.
Output: Classify it as specifically as possible, in a classification hierarchy.
Example use: Medical diagnosis can be often viewed partly as a classification problem.
GT Hypothesis matching
Input: Given a concept (a hypothesis) and a set of data (features) that describe the problem state.
Output: Decide how well the concept matches the situation. The task is a form of recognition.
Inference and control: At each level, a degree of confidence in the presence of a feature is computed from the features that constitute evidence for it, and this is performed recursively until a degree of confidence for the concept is computed. The basic theory is that recognition of a complex concept is performed by hierarchically computing intermediate abstractions from raw data.
Example use: Recognition can be performed by means of this strategy. e.g, the concept may be a disease and the data may be the patient data relevant to the disease, and we wish to know what the likelihood of the disease is.
GT Knowledge directed information passing
Input: Given attributes of some data entities.
Output: Determine the attributes of other data of interest, that are not directly known, but can be inferred from the available data.
Example use: A diagnostic system may use a knowledge-directed database of this type for converting sensor or chart values into data of direct relevance to diagnosis.
GT Synthesis by plan selection and refinement
Function: Designing an object by hierarchical planning.
Input: Given a specification of the objet to be designed.
Output: Generate design of an object meeting the specifications.
Example system: The task performed by R1
GT Abductive hypothesis assembly
Input: Given a situation description and a set of hypotheses each explaining some aspects of the situation and each with some plausibility value.
Output: Construct a composite hypothesis that is the best explanation of the situation.
Inference and control: Assembly and criticism alternate. At each stage during assembly the problem solving is driven by an attempt to explain the most significant datum remaining unexplained.
Example use: This Task is a diagnostic subtask in diagnostic reasoning as well as in theory formation in science.
Material obtained from several articles by Chandrasekaran.
|
|