Then, the machine sorts the chunks by passing them through a magnetic field which deflects the high-charge, low-weight ones more than it does the low-charge, high charges ones. This is called a mass spectrogram. By knowing the masses, it is possible to guess how the atoms of a single molecule of the unknown substance are put together. The generator consists of a structure enumerator and a synthesizer, which produce a synthetic mass spectrogram by simulating the action of a real mass spectrometer on each enumerated structure.

Author:Gronris Gabar
Language:English (Spanish)
Published (Last):7 August 2012
PDF File Size:3.70 Mb
ePub File Size:17.55 Mb
Price:Free* [*Free Regsitration Required]

Does the patient have a clinically significant allergic reaction to any antimicrobial agent? How much does the patient weigh? My first recommendation is as follows: to cover for items 1, 2, 3, 5 and 6, give gentamycin using a dose of mg 1. Modify dose in renal failure. Also, to cover for item 4, give clindamycin using a dose of mg 8. If diarrhea or other gastrointestinal symptoms develop, check for pseudomembranous colitis.

It has the following organisational features: i. Reasoning: Backward chaining, and forward reasoning uses certainty factors to reason with uncertain information. MYCIN uses backward chaining to discover what organisms where present. Then it uses forward chaining to reason from the organism to a treatment regime. Heuristics: When the general category of infection has been established, MYCIN examines each candidate diagnosis in a depth-first manner. Heuristics are used to limit the search, including checking all premises of a possible rule to see if anyone of these is known to be false.

Explanations are generated by tracing back through the rules which have been fired. Example 2. It uses a structure called an inference network to represent the data base. Duda , then of SRI International. Its rules look like: Magnetite or pyrite in disseminated or veinlet from is present.

Then: 2, — 4 there is favourable mineralisation and texture for the prophylactic stage. The rules contain two confidence estimates. Number 2, in the rule shown above indicates that the presence of the evidence is mildly encouraging. The second confidence estimate measures the extent to which the evidence is necessary to the validity of the conclusion or in other words, the extent to which the lack of the evidence indicates that the conclusion is not valid.

Here number — 4, indicates the absence of the evidence is strongly discouraging for the conclusion. The prospect- scale models describe major ore-deposits such as: I.

Massive Sulfide deposit, Kuroko Type. Mississippi-Valley Lead-Zince. Western States Sandstone Uranium. The system does not only understand the rules in its knowledge base but can explain the steps used in reaching its conclusions. The knowledge acquisition system KAS has been developed for easy editing and expansion of the inference network structure in which the knowledge base is stored. It predicted a molybdenum deposit near Mt. Knowledge Representation: Production rules and semantic network.

Bayesian reasoning is used to deal with uncertainty. Heuristics: Depth-first search is focused using the probabilities of each hypothesis. Explanations are generated by tracking back through the rules which have been fired.

Example 3. Dendral: Dendral is one of the great classic application programs. To see what it does, let us suppose that an organic chemist wants to know the chemical nature of some substance newly created in the test tube. The first step is to determine the number of atoms of various kinds in one molecule of the stuff. This step determines the chemical formula, such as C8H16O.

The notation indicates that each molecule has eight atoms of carbon, 16 of hydrogen, and one of oxygen. The spectrogram machine bombards a sample with high energy electrons, causing the molecules to break up into charged chunks of various sizes. Then, the machine sorts the chunks by passing them through a magnetic field which deflects the high-charge, low-weight ones more than it does the low-charge, high-weight ones.

The deflected chunks are collected, forming a spectrogram. Example 4. The generator consists of a structure enumerator and a synthesiser, which produces a synthetic mass spectrogram by simulating the action of a real mass spectrometer on each enumerated structure. The structure enumerator ensures that the overall generator is on complete and non-redundant because the structure enumerator uses complete and non-redundant structure-enumeration procedure.

The overall generator is also informed, because the structure enumerator uses the chemical formula and knowledge about necessary and forbidden substructures.

The tester compares the real mass spectrogram with those produced by the generator. The possible structures are those whose synthetic spectrograms match the real one adequately.

The structure judged correct is the one whose synthetic spectrogram most closely matches the real one. However, it has been successful enough in this capacity to discover results which have been published as original research.

Dendrals performance rivalled then of human chemists expert at the task, and the program was used in industry and universities. Reasoning: Forward chaining data-driven. Heuristics: DENDRAL uses a variation of depth first search called generate and test, where all hypotheses are generated and then tested against the available evidence.

Heuristic knowledge from the users chemists is also used to constrain the search. The user can supply information and the system can request information as required. Example 5. When a company buys a big computer main frame it buys a central processor, memory, terminals, disk drives, tape drives, various peripheral controllers and other paraphernalia. All these components in number must be arranged sensibly along input-output buses. Moreover all the electronic modules must be placed on the proper kind of cabinet in a suitable slot of suitable back plane.

A completed system containing some production rules was delivered to DEC for acceptance testing in October, By , DEC personnel had become sufficiently familiar with the operation of XCON to assume full responsibility for its maintenance and development, and they have extended it to contain over 4, rules.

Such editors had been responsible for two operations: checking for completeness of orders, and laying out the physical placement and wiring connections between component modules in the chassis. These tasks are fairly tedious; and each of the components is specified by properties such as voltage, frequency, how many devices it supports, and how many ports it has.

In addition to this information, the editors had information indicating which components can or must be associated with other components. An example of one of the rules from XCON is shown: IF the current sub-task is assigning devices to unibus-modules, and there is an unassigned dual port disk drive, and the type of controller it requires is known, and there are two such controllers, neither of which has any devices assigned to it, and the number of devices which these controllers can support is known.

THEN assign the disk drive to each controller and note that each controller supports one device. XCON passed an extensive series of performance tests which were also used to upgrade its performance.

A set of ten configurations was submitted to the program and the results were evaluated by a team of twelve people consisting of technical editors, technicians, and engineers. Errors detected were corrected and the cycle repeated until fifty configurations had been produced.

This initial testing detected only twelve errors of seven different types. By XCON represented an investment of over 50 person-years of human effort. The experience of building XCON yielded a number of lessons which builders of other expert systems have found instructive include: a. This effort has required about four person-years per year.

The developers of XCON found that they did not have to complete the system before putting it to use. This illustrates an important characteristics of expert systems-their incremental growth. No matter how much knowledge was added to XCON, it still continued to make occasional mistakes. The developers of XCON found that building an expert system is an unending process. It has become an indispensable and effective business tool.

The savings produced by XCON has convinced DEC management that artificial intelligence, particularly its application to expert systems, has a bright future. The organizational features of XCON are: i. Knowledge Representation: Production rules about 10, Contain no numeric measures of uncertainty as this is dealing with design aspect.

Since it is possible to specify rules exactly no uncertainty is present. Heuristics: The main configuration task is split into- subtasks which are always examined in a predetermined order. Constraint satisfaction is used to inform the search for a solution to a subtask. The benchmark expert systems have proven the feasibility of using knowledge-based systems for solving problems traditionally requiring human experts. These examples of expert systems illustrate how the different techniques can be combined to produce a useful solution and how different problems require different solutions.


The History of Artificial Intelligence

G Veera Raghavaiah. It is one of the early example of a successful AI program. It reasons backward from its goal of determining the cause of a patient illness. It uses its production rule4s to reason backward from goals to clinical observations. To solve the top-level diagnostic goal, it looks for rules whose right sides suggest diseases. It then uses the left sides of those rules the preconditions to set up sub goals whose success would enable the rules to be invoked.


An analysis on the dendral expert system

Heuristic Dendral[ edit ] Heuristic Dendral is a program that uses mass spectra or other experimental data together with knowledge base of chemistry, to produce a set of possible chemical structures that may be responsible for producing the data. For example, the compound water H2O , has a molecular weight of 18 since hydrogen has a mass of 1. Heuristic Dendral would use this input mass and the knowledge of atomic mass numbers and valence rules, to determine the possible combinations of atomic constituents whose mass would add up to Thus, a program that is able to reduce this number of candidate solutions through the process of hypothesis formation is essential. New graph-theoretic algorithms were invented by Lederberg, Harold Brown, and others that generate all graphs with a specified set of nodes and connection-types chemical atoms and bonds -- with or without cycles. Moreover, the team was able to prove mathematically that the generator is complete, in that it produces all graphs with the specified nodes and edges, and that it is non-redundant, in that the output contains no equivalent graphs e.

Related Articles