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Forward Chaining
Forward chaining (or forward reasoning) is one of the two main methods of reasoning when using an inference engine and can be described logically as repeated application of ''modus ponens''. Forward chaining is a popular implementation strategy for expert systems, business and production rule systems. The opposite of forward chaining is backward chaining. Forward chaining starts with the available data and uses inference rules to extract more data (from an end user, for example) until a goal is reached. An inference engine using forward chaining searches the inference rules until it finds one where the antecedent (If clause) is known to be true. When such a rule is found, the engine can conclude, or infer, the consequent (Then clause), resulting in the addition of new information to its data. Inference engines will iterate through this process until a goal is reached. Example Suppose that the goal is to conclude the color of a pet named Fritz, given that he croaks an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Automated Reasoning
In computer science, in particular in knowledge representation and reasoning and metalogic, the area of automated reasoning is dedicated to understanding different aspects of reasoning. The study of automated reasoning helps produce computer programs that allow computers to reason completely, or nearly completely, automatically. Although automated reasoning is considered a sub-field of artificial intelligence, it also has connections with theoretical computer science and philosophy. The most developed subareas of automated reasoning are automated theorem proving (and the less automated but more pragmatic subfield of interactive theorem proving) and automated proof checking (viewed as guaranteed correct reasoning under fixed assumptions). Extensive work has also been done in reasoning by analogy using induction and abduction. Other important topics include reasoning under uncertainty and non-monotonic reasoning. An important part of the uncertainty field is that of argumentation ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information
Information is an abstract concept that refers to that which has the power to inform. At the most fundamental level information pertains to the interpretation of that which may be sensed. Any natural process that is not completely random, and any observable pattern in any medium can be said to convey some amount of information. Whereas digital signals and other data use discrete signs to convey information, other phenomena and artifacts such as analog signals, poems, pictures, music or other sounds, and currents convey information in a more continuous form. Information is not knowledge itself, but the meaning that may be derived from a representation through interpretation. Information is often processed iteratively: Data available at one step are processed into information to be interpreted and processed at the next step. For example, in written text each symbol or letter conveys information relevant to the word it is part of, each word conveys information ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Logic
Logic is the study of correct reasoning. It includes both formal and informal logic. Formal logic is the science of deductively valid inferences or of logical truths. It is a formal science investigating how conclusions follow from premises in a topic-neutral way. When used as a countable noun, the term "a logic" refers to a logical formal system that articulates a proof system. Formal logic contrasts with informal logic, which is associated with informal fallacies, critical thinking, and argumentation theory. While there is no general agreement on how formal and informal logic are to be distinguished, one prominent approach associates their difference with whether the studied arguments are expressed in formal or informal languages. Logic plays a central role in multiple fields, such as philosophy, mathematics, computer science, and linguistics. Logic studies arguments, which consist of a set of premises together with a conclusion. Premises and conclusions are usu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Rete Algorithm
The Rete algorithm ( , , rarely , ) is a pattern matching algorithm for implementing rule-based systems. The algorithm was developed to efficiently apply many rules or patterns to many objects, or facts, in a knowledge base. It is used to determine which of the system's rules should fire based on its data store, its facts. The Rete algorithm was designed by Charles L. Forgy of Carnegie Mellon University, first published in a working paper in 1974, and later elaborated in his 1979 Ph.D. thesis and a 1982 paper. Overview A naive implementation of an expert system might check each rule against known facts in a knowledge base, firing that rule if necessary, then moving on to the next rule (and looping back to the first rule when finished). For even moderate sized rules and facts knowledge-bases, this naive approach performs far too slowly. The Rete algorithm provides the basis for a more efficient implementation. A Rete-based expert system builds a network of nodes, where ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Opportunistic Reasoning
Opportunistic reasoning is a method of selecting a suitable logical inference strategy within artificial intelligence applications. Specific reasoning methods may be used to draw conclusions from a set of given facts in a knowledge base, e.g. forward chaining versus backward chaining. However, in opportunistic reasoning, pieces of knowledge may be applied either forward or backward, at the "most opportune time". An opportunistic reasoning system may combine elements of both forward and backward reasoning. It is useful when the number of possible inferences is very large and the reasoning system must be responsive to new data that may become known. Opportunistic reasoning has been used in applications such as blackboard system A blackboard system is an artificial intelligence approach based on the blackboard architectural model, where a common knowledge base, the "blackboard", is iteratively updated by a diverse group of specialist knowledge sources, starting with a pro ...s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Constraint Handling Rules
Constraint Handling Rules (CHR) is a declarative, rule-based programming language, introduced in 1991 by Thom Frühwirth at the time with European Computer-Industry Research Centre (ECRC) in Munich, Germany.Thom Frühwirth. ''Theory and Practice of Constraint Handling Rules''. Special Issue on Constraint Logic Programming (P. Stuckey and K. Marriott, Eds.), Journal of Logic Programming, Vol 37(1-3), October 1998. Originally intended for constraint programming, CHR finds applications in grammar induction, type systems, abductive reasoning, multi-agent systems, natural language processing, compilation, scheduling, spatial-temporal reasoning, testing, and verification. A CHR program, sometimes called a ''constraint handler'', is a set of rules that maintain a ''constraint store'', a multi-set of logical formulas. Execution of rules may add or remove formulas from the store, thus changing the state of the program. The order in which rules "fire" on a given constraint store is no ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Backward Chaining
Backward chaining (or backward reasoning) is an inference method described colloquially as working backward from the goal. It is used in automated theorem provers, inference engines, proof assistants, and other artificial intelligence applications. In game theory, researchers apply it to (simpler) subgames to find a solution to the game, in a process called ''backward induction''. In chess, it is called retrograde analysis, and it is used to generate table bases for chess endgames for computer chess. Backward chaining is implemented in logic programming by SLD resolution. Both rules are based on the modus ponens inference rule. It is one of the two most commonly used methods of reasoning with inference rules and logical implications – the other is forward chaining. Backward chaining systems usually employ a depth-first search strategy, e.g. Prolog. How it works Backward chaining starts with a list of goals (or a hypothesis) and works backwards from the consequent to the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
CLIPS
CLIPS is a public domain software tool for building expert systems. The name is an acronym for "C Language Integrated Production System." The syntax and name were inspired by Charles Forgy's OPS5. The first versions of CLIPS were developed starting in 1985 at NASA-Johnson Space Center (as an alternative for existing system ART*Inference) until the mid-1990s when the development group's responsibilities ceased to focus on expert system technology. The original name of the project was "NASA's AI Language" (NAIL). CLIPS was probably the most widely used expert system tool as of 2005. CLIPS itself is written in C, extensions can be written in C, and CLIPS can be called from C. Its syntax resembles that of the programming language Lisp. CLIPS incorporates a complete object-oriented language for writing expert systems. COOL combines the programming paradigms of procedural, object oriented and logical (theorem proving) languages. Facts and rules CLIPS uses forward chaining. Like ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Goal-oriented
Goal orientation, or achievement orientation, is an "individual disposition towards developing or validating one's ability in achievement settings". In general, an individual can be said to be ''mastery'' or ''performance'' oriented, based on whether one's goal is to develop one's ability or to demonstrate one's ability, respectively. A ''mastery'' orientation is also sometimes referred to as a ''learning'' orientation. Goal orientation refers to how an individual interprets and reacts to tasks, resulting in different patterns of cognition, affect and behavior. Developed within a social-cognitive framework, the orientation goal theory proposes that students' motivation and achievement-related behaviors can be understood by considering the reasons or purposes they adopt while engaged in academic work. The focus is on how students think about themselves, their tasks, and their performance. Goal orientations have been shown to be associated with individuals' academic achievement, adjus ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data-driven Science
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured and unstructured data, and apply knowledge from data across a broad range of application domains. Data science is related to data mining, machine learning, big data, computational statistics and analytics. Data science is a "concept to unify statistics, data analysis, informatics, and their related methods" in order to "understand and analyse actual phenomena" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational, and now data-driven) and asserted that "everything about science ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Rule Base
In computer science, a rule-based system is used to store and manipulate knowledge to interpret information in a useful way. It is often used in artificial intelligence applications and research. Normally, the term ''rule-based system'' is applied to systems involving human-crafted or curated rule sets. Rule-based systems constructed using automatic rule inference, such as rule-based machine learning, are normally excluded from this system type. Applications A classic example of a rule-based system is the domain-specific expert system that uses rules to make deductions or choices. For example, an expert system might help a doctor choose the correct diagnosis based on a cluster of symptoms, or select tactical moves to play a game. Rule-based systems can be used to perform lexical analysis to compile or interpret computer programs, or in natural language processing. Rule-based programming attempts to derive execution instructions from a starting set of data and rules. This is ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Iteration
Iteration is the repetition of a process in order to generate a (possibly unbounded) sequence of outcomes. Each repetition of the process is a single iteration, and the outcome of each iteration is then the starting point of the next iteration. In mathematics and computer science, iteration (along with the related technique of recursion) is a standard element of algorithms. Mathematics In mathematics, iteration may refer to the process of iterating a function, i.e. applying a function repeatedly, using the output from one iteration as the input to the next. Iteration of apparently simple functions can produce complex behaviors and difficult problems – for examples, see the Collatz conjecture and juggler sequences. Another use of iteration in mathematics is in iterative methods which are used to produce approximate numerical solutions to certain mathematical problems. Newton's method is an example of an iterative method. Manual calculation of a number's square root is a c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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