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What is an Equation - TI Education
What is an Equation - TI Education

approximate reasoning using anytime algorithms
approximate reasoning using anytime algorithms

Artificial Intelligence Question Bank 2014
Artificial Intelligence Question Bank 2014

... With the help of a diagram, explain the structure of RBS. What is conflict resolution? Explain the various strategies used for selecting a rule. Explain the working mechanism of forward chaining/backward chaining. Backward chaining is a depth first search strategy-Justify State merits and demerits o ...
LNCS 3242 - A Hybrid GRASP – Evolutionary Algorithm Approach to
LNCS 3242 - A Hybrid GRASP – Evolutionary Algorithm Approach to

... Ruler problem. In their representation, each chromosome is composed by a permutation of n − 1 integers that represents the sequence of the n − 1 lengths of its segments. In order to assure the uniqueness of each segment length in all the individuals of the population, certain precautions were taken: ...
artificial intelligence - ABIT Group of Institutions
artificial intelligence - ABIT Group of Institutions

... weak AI The principle behind Weak AI is simply the fact that machines can be made to act as if they are intelligent. For example, when a human player plays chess against a computer, the human player may feel as if the computer is actually making impressive moves. But the chess application is not thi ...
11 2 Solving Multi Step Equations
11 2 Solving Multi Step Equations

tablefinal
tablefinal

... optimization is very important and concerning act of obtaining the best result under given situations. This is the reason behind why optimization has been a popular research topic for decades. Optimization originated in the 1940s, when the British military faced the problem of allocating limited res ...
approximate reasoning using anytime algorithms
approximate reasoning using anytime algorithms

Linear Equations: Slope and Equations of Lines - UH
Linear Equations: Slope and Equations of Lines - UH

... that A, B, and C are integers whenever possible. In this course, you will be given problems where it is always possible to change the equation so that A, B, and C are integers. There are cases where it is not possible to change the coefficients to integers, such as the equation ...
Grammatical Evolution Hyper-heuristic for Combinatorial
Grammatical Evolution Hyper-heuristic for Combinatorial

IOSR Journal of Computer Engineering (IOSR-JCE)
IOSR Journal of Computer Engineering (IOSR-JCE)

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PDF Document

Final Course Review
Final Course Review

... about something which we can call a concept. This can be a situation, an object, a phenomenon, a relation. 2. Frames contain smaller pieces of knowledge: components, attributes, actions which can be (or must be) taken when conditions for taking an action occur. 3. Frames contain slots which are plac ...
Original Article A shifted hyperbolic augmented Lagrangian
Original Article A shifted hyperbolic augmented Lagrangian

... Metaheuristics are approximate methods or heuristics that are designed to search for good solutions, known as near-optimal solutions, with less computational effort and time than the more classical algorithms. While heuristics are tailored to solve a specific problem, metaheuristics are general-purp ...
What Is Approximate Reasoning?
What Is Approximate Reasoning?

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PDF

A Low-Cost Approximate Minimal Hitting Set Algorithm
A Low-Cost Approximate Minimal Hitting Set Algorithm

Combining Linear Programming and Satisfiability Solving for
Combining Linear Programming and Satisfiability Solving for

... are boolean-valued; typeface are real. must be solved to solve the entire LCNF problem1 . The key to the encoding is the simple but expressive concept of triggers — each propositional variable may trigger a constraint; this constraint is then enforced whenever the variable’s truth assignment is true ...
Lecture Notes on the Lambda Calculus
Lecture Notes on the Lambda Calculus

... information than classical ones, and in particular, they allow one to compute solutions to problems (as opposed to merely knowing the existence of a solution). The resulting algorithms can be useful in computational mathematics, for instance in ...
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PDF

... M if for all -.T0X2Y7Z9/CD-E [F@A@";>=@?\AH , M returns a satisfying assignment or concludes unsatisfiability of CJIK-.#L . ...
Heuristics for Planning with SAT
Heuristics for Planning with SAT

... they are usually evaluated on. In contrast, our heuristic relies on a very natural and simple principle that represents common intuitions about planning. We view the new heuristic as a step toward developing better SAT-based techniques for planning and other related problems such as model-checking a ...
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11-3

6.034 Artificial Intelligence by T. Lozano
6.034 Artificial Intelligence by T. Lozano

... which we will discuss later. Note that because we are not dealing with consistency constraints (all negative literals) we will not be able to deal with negative facts either. ...
Using Anytime Algorithms in Intelligent Systems
Using Anytime Algorithms in Intelligent Systems

... point of time. These data form the quality map of the algorithm. Figure 2 shows the quality map of the randomized tour-improvement algorithm. It summarizes the results of many activations of the algorithm with randomly generated input instances (including 50 cities). Each point (t, q) represents an ...
Algorithm Selection for Combinatorial Search Problems: A Survey
Algorithm Selection for Combinatorial Search Problems: A Survey

... clearly superior to previous approaches. In the majority of cases however, a new approach will improve over the current state of the art for only some problem instances. This may be because it employs a heuristic that fails for instances of a certain type or because it makes other assumptions about ...
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Unification (computer science)

Unification, in computer science and logic, is an algorithmic process of solving equations between symbolic expressions.Depending on which expressions (also called terms) are allowed to occur in an equation set (also called unification problem), and which expressions are considered equal, several frameworks of unification are distinguished. If higher-order variables, that is, variables representing functions, are allowed in an expression, the process is called higher-order unification, otherwise first-order unification. If a solution is required to make both sides of each equation literally equal, the process is called syntactical unification, otherwise semantical, or equational unification, or E-unification, or unification modulo theory.A solution of a unification problem is denoted as a substitution, that is, a mapping assigning a symbolic value to each variable of the problem's expressions. A unification algorithm should compute for a given problem a complete, and minimal substitution set, that is, a set covering all its solutions, and containing no redundant members. Depending on the framework, a complete and minimal substitution set may have at most one, at most finitely many, or possibly infinitely many members, or may not exist at all. In some frameworks it is generally impossible to decide whether any solution exists. For first-order syntactical unification, Martelli and Montanari gave an algorithm that reports unsolvability or computes a complete and minimal singleton substitution set containing the so-called most general unifier.For example, using x,y,z as variables, the singleton equation set { cons(x,cons(x,nil)) = cons(2,y) } is a syntactic first-order unification problem that has the substitution { x ↦ 2, y ↦ cons(2,nil) } as its only solution.The syntactic first-order unification problem { y = cons(2,y) } has no solution over the set of finite terms; however, it has the single solution { y ↦ cons(2,cons(2,cons(2,...))) } over the set of infinite trees.The semantic first-order unification problem { a⋅x = x⋅a } has each substitution of the form { x ↦ a⋅...⋅a } as a solution in a semigroup, i.e. if (⋅) is considered associative; the same problem, viewed in an abelian group, where (⋅) is considered also commutative, has any substitution at all as a solution.The singleton set { a = y(x) } is a syntactic second-order unification problem, since y is a function variable.One solution is { x ↦ a, y ↦ (identity function) }; another one is { y ↦ (constant function mapping each value to a), x ↦ (any value) }.The first formal investigation of unification can be attributed to John Alan Robinson, who used first-order syntactical unification as a basic building block of his resolution procedure for first-order logic, a great step forward in automated reasoning technology, as it eliminated one source of combinatorial explosion: searching for instantiation of terms. Today, automated reasoning is still the main application area of unification.Syntactical first-order unification is used in logic programming and programming language type system implementation, especially in Hindley–Milner based type inference algorithms.Semantic unification is used in SMT solvers and term rewriting algorithms.Higher-order unification is used in proof assistants, for example Isabelle and Twelf, and restricted forms of higher-order unification (higher-order pattern unification) are used in some programming language implementations, such as lambdaProlog, as higher-order patterns are expressive, yet their associated unification procedure retains theoretical properties closer to first-order unification.
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