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Integration of Forward and Backward Inferences Using Extended Rete Networks
Published in Takushi Tanaka, Setsuo Ohsuga, Moonis Ali, Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 2022
Most current knowledge engineering tools have been designed to provide the structures for representing domain knowledge by using rule structure (IF-THEN), and an inference mechanism for controlling the use of domain knowledge. In production rule systems, two very important reasoning control strategies are forward (data-directed) chaining and backward (goal-directed) chaining [HWB83]. Although forward and backward chainings are useful inference schemes, forward chaining approaches sometimes have the disadvantage of generating many facts not directly related to the problem under consideration, while backward chaining approaches have the disadvantage of perhaps becoming fixed on an initial set of goals and having difficulty shifting focus when the data available do not support them. An interesting possibility is to inference in both directions simultaneously or opportunistically. We call this inference mechanism as mixed inference. To remedy control limitations within the basic architecture, several works have been tried. Some knowledge engineering tools (ART, TIRS, ESE, KEE, etc.) can process both forward and backward chaining [JC94]. However, their chaining schemes are not be naturally integrated tightly into a single uniform control framework.
Rule-Based Systems
Published in Adrian A. Hopgood, Intelligent Systems for Engineers and Scientists, 2021
Backward chaining is an inference strategy that assumes the existence of a goal that needs to be established or refuted. In the boiler-control example, our goal might be to establish whether it is appropriate to replace the outlet pipe, and we may not be interested in any other deductions that the system is capable of making. Backward chaining provides the means for achieving this objective. Initially, only those rules that can lead directly to the fulfillment of the goal are selected for examination. In our case, the only rule that can achieve the goal is rule r2_3, since it is the only rule whose conclusion is report('** outlet pipe needs replacing! **' ). The condition part of rule r2_3 is examined but, since there is no information about a steam outlet blockage in the fact base, rule r2_3 cannot be fired yet. A new goal is then produced, namely, steam_outlet is blocked, corresponding to the condition part of rule r2_3. Two rules, r2_4 and r2_6, are capable of fulfilling this goal and are therefore antecedents of rule r2_3. What happens next depends on whether a depth-first or breadth-first search strategy is used. These two methods for exploring a search tree were introduced in Chapter 1, but now the nodes of the search tree are rules.
C
Published in Philip A. Laplante, Comprehensive Dictionary of Electrical Engineering, 2018
chain reaction a process in which high-energy neutrons emitted from fissile radioactive material are directed into more fissile material such that more neutrons are emitted. The process creates heat which is used to power thermal power plants. chaining when the output stream of one arithmetic pipeline is fed directly into another arithmetic pipeline; used in vector computers to improve their performance. chaining of fuzzy rules a reasoning strategy which searches the knowledge base and chain from rule to rule to form inferences and draw conclusions. In forward chaining, a chain of datadriven rules are evaluated for which the conditional parts are satisfied to arrive at the conclusion. Backward chaining is goal-driven in which subgoals are established, where necessary, through which a chain of rules are selected, eventually satisfying the goal.
Prognostic modelling for industrial asset health management
Published in Safety and Reliability, 2022
Neda Gorjian Jolfaei, Raufdeen Rameezdeen, Nima Gorjian, Bo Jin, Christopher W. K. Chow
Expert Systems are the most popular knowledge-based techniques that require less training time than machine learning techniques. An ES is an intelligent computer program that simulates the knowledge and inference procedures of human experts to solve complex problems (Bollinger & Smith, 2001). It works well when the problem to be solved is complicated and when judgement and experience are helpful tools in finding the solution. An ES has two essential parts: the knowledge base and inference engine. Acquired knowledge is put into the rule base or knowledge base in an ES. Rules and facts make up the knowledge base. Rules in ESs are defined using IF-THEN syntax, where IF is an antecedent that is compared to inputs and THEN is a consequent, which is the output (Sharma et al., 2008). The inference is the derivation of new facts from known facts. There are two inference approaches that are commonly used as problem-solving strategies in ESs, forward and backward chainings. Forward chaining is reasoning from facts to conclusions resulting from those facts. Backward chaining involves reasoning in reverse from a hypothesis to facts that support a hypothesis. An inference engine is the processor of facts. The inference engine within an ES is the portion of the computer program that processes knowledge.
Backward chaining inference as a database stored procedure – the experiments on real-world knowledge bases
Published in Journal of Information and Telecommunication, 2018
Tomasz Xie¸ski, Roman Simiński
Backward chaining inference can be considered as a bottom-up procedure which starts with a main goal and queries the fact base about information which may satisfy the conditions contained in the rules. We basically go through the rules in the knowledge base looking for conclusions which match the query and if we find them, we can create new queries (adding new facts if necessary). Its complexity can be linear or less (taking into account the size of the knowledge base), depending on the implementation. The main idea and general description of a classical backward chaining inference algorithm have been repeatedly published, for example, in Grzymala-Busse (2012) and Ligeza (2006). But it is hard to find a detailed, step-by-step algorithm and for this reason we present a more detailed description in the form of a block diagram and pseudo-code.