INFERENCE MENGGUNAKAN FORWARD CHAINING PADA FOOD AFFORDABILITY

In this paper, it is explained that the forward and backward chaining can be naturally integrated on an extended Rete network via the generation of hypotheses from forward chaining inference. We introduce the concept of the hypothesis objects and augment the production rule format by adding the hypothesis condition elements in the LHS part of a rule, which control the forward and backward chaining invocation of rules. A rule can be used in forward directions, backward directions, or mixed directions. To be applied to the backward chaining, a modified version of Rete has been formulated by adding the hypothesis-and-nodes and backward direction edges to the node network. We then develop a backward chaining algorithm which takes advantage of already existing partial match results obtained by the previous forward chaining in the ERMI. The whole mixed inference process has the potential advantages over purely forward and backward chaining strategy.

In his book Mental Spaces, Fauconnier develops a powerful theory of human knowledge representation and linguistic processing that handles a variety of problems in linguistics and the philosophy of language in a simple, uniform, and intuitively plausible way. However, he has little to say about the structure or general role of mental spaces in cognition. The present paper proposes that mental spaces are a means of organizing knowledge in support of a general inference method, simulative reasoning, found in various guises both in logic and in Artificial Intelligence. The structuring required to fulfill this role allows us to make a wide variety of predictions which seem to be borne out by evidence from natural language. In attributing a specific function to mental spaces, this paper suggests that the theory of mental spaces defines a potentially significant paradigm for knowledge representation in Artificial Intelligence.

Whole task training, forward chaining, and backward chaining techniques were compared in teaching two different 120-step sequences of behavior to college students. In Exp. 1 participants learned a sequence that began with easy movements, progressed through more difficult movements, and ended with easy movements. Whole task training resulted in more errors than either forward or backward chaining. Differences were found in the location of errors. Both whole task training and forward chaining resulted in fewer errors at the beginning of the sequence and backward chaining resulted in fewer errors at the end of the sequence. Because the results of Exp. 1 may have been affected by the increased difficulty of the middle of the sequence, Exp. 2 was undertaken using a sequence of behavior where all segments were of equal difficulty. Whole task training and forward chaining resulted in fewer errors than backward chaining. The location of errors was similar to that found in Exp. 1.

A doctor or medical scholar needs a reference book to learn how to diagnose tropical diseases. This reference book is sometimes a hassle if you have to carry it everywhere. This reference book is also impractical if you have to search it first to find the symptoms and diseases you want to study. So that we need a system to make it easier for doctors and medical scholars to study the science of diagnosis and look for symptoms and diseases. Expert systems are knowledge-based programs that provide expert quality solutions to problems in a specific domain. This expert system is used in the fields of medicine, agriculture, business, and others. Expert systems in the field of medicine make it easy to identify diseases suffered by patients through the symptoms present in the patient. This expert system helps doctors make diagnoses to convince doctors about the results of the diagnosis. The expert system in this study uses a combined method. The combined method is forward chaining and backward chaining. The forward chaining method is used to determine specific symptoms that appear, while the backward chaining method is used to trace general symptoms that arise from specific symptoms that have been previously selected. The result of combining these methods provides a diagnostic percentage of 100%. Meanwhile, the user's assessment of the system gives a good response.

This paper reviews motivations for introducing fuzzy sets and fuzzy logic to knowledge representation in artificial intelligence. First we consider some areas of successful application of conventional approaches to system analysis. We then discuss limitations of these approaches and the reasons behind these limitations.We introduce different levels of representation for complex systems and discuss issues of granularity and fuzziness in connection with these representation levels. We make a distinction between decomposable and integrated complex systems and discuss the relevance of this distinction for knowledge representation and reasoning. We also distinguish fuzzy relations between quantities of different granularity within one domain from fuzzy relations between two different domains and discuss the need of considering both in artificial intelligence.We distinguish methods for describing natural, artificial, and abstract systems and contrast the modeling of system function with the modeling of system behavior in connection with the representation of fuzziness. The paper briefly discusses recent criticism of the fuzzy system approach and concludes with a prospect on soft computing in AI.

The popularity of Olympic-style weightlifting in fitness routines is growing, but participating in these exercises with improper technique places lifters at increased risk for injury. Fitness training professionals have developed multiple teaching strategies, but have not subjected these strategies to systematic evaluation, particularly with novice lifters. Two strategies recommended by professional training organizations are akin to forward and backward chaining, which have been shown effective at teaching other novel, complex behaviors. The present study compared these forward- and backward-chaining-like strategies to teach novice lifters "the clean" and "the snatch," two Olympic weightlifting movements frequently incorporated into high-intensity training programs. Participants performed lifts taught with forward chaining more accurately than lifts taught with backward chaining.

Currently, technology capable of absorbing human thought processes and patterns is known as artificial intelligence. An expert system is a component of artificial intelligence that contains knowledge and experience acquired from an expert in a particular field. The aim of this study is to develop a web-based expert system designed to detect digestive disorders in humans. Its purpose is to provide patients with information about their health status, correlated with their presenting symptoms, with a certain degree of accuracy. Through the use of this expert system, patients have the ability to identify potential digestive disorders based on individual symptoms or combinations of symptoms they may be experiencing. Additionally, it facilitates early detection of digestive disorders without the need to delay treatment until doctors or specialists are available. The method used is Forward Chaining, which involves collecting symptom data to determine the appropriate type of disease. Forward Chaining is a reasoning technique used in expert systems and rule-based systems. It begins with known events or conditions and then attempts to match these events with the IF (combination) clauses from the IF-THEN rules that are available.

There are two trends in the software initiatives of the U. S. Department of Defense which will have direct impact on future software development and which suggest a focus for current basic research in software. These trends are 1) the move toward smart weapons, suggesting the use of Expert Systems to control these weapons, and 2) the directive that all software for embedded computer systems be written in Ada. The research focus suggested by these developments is an analysis of the Ada programming language and its associated programming support environment (APSE) to determine how to design and construct an Expert System for natural implementation in Ada.It is well known that Ada can be used as a language for Expert Systems. There now exist several Expert System Shells written in Ada and more are being developed every day. The issue still open to research is how to do this optimally, making the best use of the expressive power of the language. An absurd example will make this clearer. It is also known that it is possible to write an Expert System in COBOL. Doing such would be a very tedious and error prone task, because the expressive power of the COBOL language is focused on commercial applications.Another possibility for writing an Expert System in Ada would be to take an existing Expert System in LISP and translate the code, line for line into Ada. Admittedly this is a plausible task; this author has done some preliminary investigation in this area. However, it is arguable that such an approach will not result in efficient Ada code, for constructs which are natural to LISP might not translate into constructs natural to Ada.While it is difficult to give a precise definition of an Expert System, it is easy to list characteristics which are usually associated with such a system. This paper presents the results of some preliminary research on the implementation of these features by means of some of the structures in Ada, such as Generic Program Units, Packages and Data Encapsulation, which are typical of the language's rich expressive power and which must form the basis of any natural implementation in Ada of an Expert System.One of the features expected in an Expert System is forward and backward chaining, probably implemented with a version of the Rete algorithm. For this reason, this research included writing several small symbolic pattern matching procedures and testing their performance.This research also investigated the use of Ada packages with encapsulated data types to implement a blackboard structure. It was found that a global data structure and appropriate operators could be implemented. As a side result of this work, the frame construct with attached operators and inheritance mechanisms was implemented.The research reported here also addressed the issue of Object Oriented Programming which has become quite popular in LISP programming. Here the match to Ada is quite natural for the latter language itself is also designed to support the Object Oriented Approach. Several LISP textbook examples were translated to the corresponding Ada structure and manipulated with some test programs.Several directions for future research have been generated as a result of this preliminary work. One open question is how to implement concepts originally developed for a language with late binding, such as LISP, in a language with strong typing such as Ada. Another issue concerns the translation of LISP code into Ada code. Early work suggests that it will be more profitable to view the LISP as a high level specification language for an Ada system rather than a program to be translated with literal fidelity. These and several other projects are being pursued and will be reported out in future papers.

An expert system is a system that adopts the expertise of an expert in a particular field into the system. Rabbits are one of the animals that are close to humans and have high body adaptability. However, rabbits are not free from the threat of disease. Treatment requires more costs so we need a way to find out the disease and the solution so that we can take the necessary actions. To overcome this, an expertise media was created that can be accessed by rabbit owners online using the Forward Chaining and Certainty Factor methods. The expert system must be able to work under uncertainty, so the Forward Chaining (FC) and Certainty Factor (CF) methods were added to overcome this problem. Forward Chaining is a decision making method that is commonly used in expert systems and the Certainty factor method is a method to prove the uncertainty of an expert's thinking. The system diagnosis results display possible diseases suffered by rabbits and provide treatment solutions for diseases with the largest CF values. With the calculation results, the level of confidence information based on the table is Definitely Yes. The accuracy of the system output is proven from the results of system validity with experts.

Expert system is made to transfer the expert knowledge into the system, so it hopefully can give solution to problem.“Expert system for diagnosis of infection TBC lung disease” is an alternative for the various expert system that can be used to overcome the problem. The aim of designing expert system is for diagnosis of infection TBC lung disease, should that users can diagnose towards this illnes based on symptoms put into this system. Development of expert system uses knowledge base stored in database using Paradox, rule based with forward chaining method.The result of consultation with the system indicate that this system can determine disease and suggestion based on symptoms inputed by user. Keyword : Expert System, Forward Chaining, Knowledge Base

Plant disease is one of the reasons that cause the destruction of plant. It affects plant productivity and quality. Most of the farmers made mistake in cope with this problem because of the lack of knowledge. Expert system is a solution that has been widely used for identifying disease. This paper presents an Android-based expert system to help identifying tomato diseases. Data used in this expert system consist of 16 data of tomato diseases, 53 data of symptoms, and 20 variety of rules. This paper implements forward chaining and certainty factor method. Forward chaining is used as a reasoning method to get the result of disease identification. Certainty factor is used as a calculation method to obtain accuracy degree of identification results. Testing has been done through two stages, internal and external. The result from internal testing shows that tomato expert system works properly and fit perfectly in various android devices. External testing is done by giving questionnaire to 44 respondents. The result of questionnaires shows that tomato expert system is categorized as “good” by them.

Three studies are presented comparing forward and backward chaining techniques. In the Experiment 1, an 8-step Lego® play construct was taught. In one condition, a construct was taught using forward chaining techniques and in the other condition a construct was taught using backward chaining techniques in an alternating treatments design. In the Experiment 2, a 12-step Lego® construct was taught using the same procedures outlined above. Finally, in Experiment 3, a pre-vocational task, building chess pieces, was taught. Efficiency of both chaining procedures was measured by number of trials to acquisition and number of error rates across conditions and participants. Results show that both chaining procedures were equally effective, all participants acquired the behavior chains with both chaining procedures and there was little difference in acquisition time and error rate between the two procedures.

This set of LOTUS 1-2-3 templates is a tutorial on basic expert system concepts, including explanations and demonstrations of backward chaining, forward chaining, stacks, depth-first and breadth-first searching, knowledge representation, demons and inheritance, and uncertainty. IF/THEN .. illustrates the ubiquitous LOTUS 1-2-3 as a programming language and transcends the requirement of knowing a specialized artificial intelligence (AI) programming language like LISP or Prolog to understand expert systems.

This paper introduces first an expert system shell based on stratified forward chaining. The stratified forward chaining was proposed as a generalization for the inheritance networks with exception since it allows more than one antecedent in the premises part of a rule. Expert systems built with this tool are rule based and the user interface is web based. The practical use of this chaining is presented here through the three chaining methods: forward chaining, backward chaining and mixed chaining. Our current aim is to make use of these inference mechanisms to help the user in his interaction with administration to identify in an efficient way the relevant information he needs. This is done through a question-response dialog to determine the user profile. The paper considers also in a second part a special kind of inference called deontic inference. The rationale behind this kind of inference is the possible presence of deontic rules which may impose a change in the base of current facts to make it comply with the obligations they prescribe.

Hydroponics systems require tremendous attention both in nutrition, plant growth, the risk of pest and disease attacks. Thus, this hydroponic farming system requires high experience and expertise that is the main obstacle for business actors who want to start a vegetable cultivation business with a hydroponic system, especially for business actors who do not have a background in agriculture. The hydroponic cultivation expert system in this study aims to detect plant diseases and pests and provide solutions to these diseases. In addition, this expert system application also aims to monitor plant needs, so this application can help farmers grow up hydroponic vegetable cultivation businesses. The combination of the forward and backward chaining methods is applied to detect plant diseases and pests. Whereas the forward chaining method use to monitor plant needs. The combination of the forward and backward chaining methods in identifying plant diseases and pests can make it easier for users to determine the suffered by plants and provide information about these diseases. The application of the forward chaining method in monitoring plant needs can identify the level of fulfilment of entails of each component of plant needs according to the type of plant and the day after planting. The application of this hydroponic expert system can overcome the limited number of hydroponic experts in helping farmers identify plant diseases and pests and monitor plant needs.