12-10-2013_09-24-50 / [Simon_Kendal,_Malcolm_Creen]_Introduction_to_Know(BookFi.org)

Knowledge sources

Blackboard

FIGURE 6.2. Blackboard system architecture.

human experts from different specialisms do) so that they could solve problems in a combination of these areas? This is the idea behind blackboard architectures.

In the HEARSAY project an early blackboard system—a computer was connected to a library catalogue in order to be able to answer spoken enquires about the library’s stock such as:

What has John Smith written since 1974?

Which of his reports are on computing?

List these reports.

It is not easy to program a machine to understand such spoken queries because language is very complex and might be considered to consist of the following ‘layers’:

Phonetic—turning sound waves into word components, e.g. boy, s.

Morphemic—connecting word components together to form other words, e.g. boy + s = boys.

Syntactic—ordering the words according to the grammar of a particular language. For example, ‘Fast ran the athlete.’ is an inappropriate ordering of ‘The athlete ran fast’.

Semantic—the meaning from the words, e.g. ‘Blue dreams eat noisily.’ has words in the right order but the sentence is meaningless.

Textual—structure: connecting sentences to make paragraphs and whole texts.

It would be extremely difficult to write a single ES that coped with all of these problems. The designers of HEARSAY in fact wrote five separate ESs—one for each separate ‘layer’ of language and got them to cooperate via a blackboard.

The actual input to the system is the spectrogram showing the variation of energy in different frequency bands of the spoken input as it varies over time. HEARSAY replaces the speech stream at the lowest level of its blackboard (multi-level database, indexed by time flowing from left to right) with a set of time-located hypotheses as to what phonemes might be present, with confidence levels associated with the evidence for such phonemes in the spectrogram. This relation between fragments of

Feedback 3

You should have been able to suggest that managing such a process would require:

a protocol for scheduling which expert can contribute if two attempt to do so at the same time

an agreement about terminology, language, modelling frameworks, etc.

HEARSAY also provides scheduling whereby the activity of processes and their interaction through the blackboard database is controlled. Each process (KS) is viewed as an agent that embodies some area of knowledge, and can take action based on that knowledge. Some KSs are grouped as computational entities called modules in the final version of the HEARSAY-II system. The KSs within a module share working storage and computational routines which are common to the procedural computations of the grouped KSs. HEARSAY is based on the ‘hypothesize-and-test’ paradigm which views solution-finding as an iterative process, with each iteration involving the creation of a hypothesis about some aspect of the problem and a test of the plausibility of the hypothesis. Each step rests on a priori knowledge of the problem, as well as on previously generated hypotheses. The process terminates when the best consistent hypothesis is generated satisfying the requirements of an overall solution.

Though the KSs cooperate via the blackboard in an iterative formation of hypotheses, no KS ‘knows’ what or how many other KSs exist. This ignorance is maintained to achieve a completely modular KS structure that enhances the ability to test various representations of a KS as well as possible interactions of different combinations of KSs.

At any one time the blackboard contains all current hypotheses. Each hypothesis has an associated set of attributes, some optional, others required. Several of the required attributes are:

the name of the hypothesis and its level

an estimate of its time interval relative to the time span of the entire utterance

information about its structural relationships with other hypotheses

validity ratings.

The task of the system is therefore essentially a search problem where the search space is the set of all possible networks of hypotheses that sufficiently span the time interval of the utterance, connecting hypotheses directly derived from the acoustic input to those that describe the semantic content of the utterance. No KS can single-handedly generate an entire network to provide the element of the search space. Instead, the KSs cooperate to provide hypotheses for the network that provides an acceptable interpretation of the acoustic data. Each KS may read data, add, delete, or modify hypotheses, and attribute values of hypotheses on the

showing the general layout of the gardens, one area which will display the house structure, one area that will display the design for the kitchen and dining room, etc.

Each KS communicates and writes results, or draws their designs on the blackboard. Initially, the blackboard will be empty although all of the KSs will be allowed access to it. Initial requirements of the house and garden (including the dimensions and positioning of windows, etc.) will be added to the blackboard. When this is complete, the KBS for the garden design, and the KBS to design the internal structure, will both be able to start their work.

Thus one large KBs is segmented into several smaller KS’s.

Advantages and Limitations of Blackboard Architectures

As indicated earlier, when you wish to link several sources of expertise, in problems that naturally have ‘layers’, then the blackboard architecture is a good approach.

The overall problem is broken down into specific tasks, aiding maintenance, improving efficiency and allowing each KS to use whatever reasoning mechanism is most appropriate to it. Thus, the most appropriate knowledge representation scheme may be used by each KS and overall a range of schemes may be used. This aids flexibility.

Summary

In this section you have been introduced to the nature and potential application of blackboard systems and have seen how a real system was used.

SECTION 3: PROBLEM-SOLVING METHODS

Introduction

This section explains the use of general Problem Solving Methods (PSMs) and some of the generic issues affecting the use of all PSMs.

Objectives

By the end of the section you will be able to:

evaluate the use of control and domain knowledge within ESs

discuss the advantages and limitations of using PSMs.

Problem-Solving Methods

Problem-solving methods, in common usage, specify the sequence of tasks that are needed to solve a problem, and the knowledge required by the method.

Knowledge acquisition was traditionally seen as an exercise in acquiring domain knowledge from the expert. How that was processed was the responsibility of the inference engine.

In practice, for efficiency reasons the knowledge was structured so that the fundamental questions were asked first, and the inference proceeded to a solution following the same reasoning process as the expert used. This reasoning process is called control knowledge and is quite different from subject (or domain) knowledge.

More recently knowledge acquisition has come to be viewed as a modelling exercise where the reasoning processes, the domain knowledge and even the behaviour of the organisation can all be modelled separately. By modelling the problemsolving behaviour of experts, we can develop PSMs for generic tasks such as design, diagnosis and scheduling.

PSMs and Tasks

Among the requirements of an intelligent system is a certain degree of robustness: the system should be able to support some changes to its task specification. One way of providing such an ability is to allow the user to modify the system in order to control how tasks are performed and to specify new tasks within the general capabilities of the system itself. Those modifications are modifications of

the system knowledge base that concern a certain subset of the knowledge: the control or problem-solving knowledge.

One way of achieving such robustness is to couple an ES with a knowledgeacquisition tool where some domain-independent theory of the problem-solving process is explicitly represented.

Activity 4

This activity helps you recognise the advantages of separating domainindependent PSMs from domain knowledge.

Look at the following problem that was presented to students in an examination: Air (Cp = 0.24 Btu/lb◦F) enters a preheater at 80◦F and leaves at 300◦F; flue gases (Cp = 0.24 Btu/lb◦F) enter at 600◦F. A total of 180,000 lb of air per hour are heated by 188,000 lb of gas per hour. The coefficient of heat transfer, U, is 125 Btu per hour per sq ft per deg ◦F. Calculate the surface area of the tubes if the heater is arranged in parallel flow.

If we suggested that the ‘problem solving method’ involved:

stating the problem

listing symbols and their meaning

listing assumptions

identifying equations

calculating

stating the answer.

What advantages might there be in separating PSMs from the knowledge that might need to be applied?

Feedback 4

You should have been able to recognise that:

The PSM is open to inspection, and can be improved without necessarily affecting the domain knowledge.

By separating the PSM from the domain knowledge we enable reuse of either.

Two or more PSMs for solving the same generic task can be compared, and we can use whichever is more suited to a particular task.

Examples of PSMs

Problem-solving methods generally fall into two categories:

Classification—where solutions are selected from a pre-enumerated set

Construction—where solutions are created during problem solving.

Actual examples of PSMs include:

propose and revise (P&R), e.g. for an elevator configuration

acquire and present, e.g. for a report

extrapolate from a similar case, e.g. for sizing requirements for a computer system.

We will look at the first of these in some detail.

Propose and Revise

Propose and revise is a PSM to accomplish a configuration task. Propose and revise works by proposing a value for each parameter of a system at a time and checking to see whether each parameter satisfies all constraints on it.

In particular, within constructive problems, a configuration design task can be solved by the P&R problem solving method (Marcus and McDermott, 1989). This method belongs to a family of similar methods for configuration problems (propose critique-and-modify, propose-and-exchange, etc.) and simulates how a human being proposes the components of a design in a step-by-step manner meanwhile analysing whether it violates some constraints. If so, a remedy is applied based on heuristic knowledge in order to modify the original proposal. This process is repeated until, if possible, a satisfactory configuration is found.

The abstract structure of this method assumes the existence of three kinds of knowledge to be represented with three knowledge bases:

derivative knowledge

compatibility knowledge

remedy knowledge.

Activity 5

This activity will help you understand the nature of the P&R PSM.

Below are descriptions of the three kinds of knowledge mentioned above. Indicate on the left-hand which kind of knowledge the description relates to. Hint words in the descriptions have been removed to make this more challenging.

This knowledge is used to verify a proposed design and includes a set of criteria

This is knowledge to propose a design that includes specific criteria to deduce a complete design from initial specifications. This knowledge can be formulated