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Intelligence by Design: Principles of Modularity and Coordination for
Intelligence by Design: Principles of Modularity and Coordination for

... All intelligence relies on search — for example, the search for an intelligent agent’s next action. Search is only likely to succeed in resource-bounded agents if they have already been biased towards finding the right answer. In artificial agents, the primary source of bias is engineering. This dis ...
Intelligence by Design - Department of Computer Science
Intelligence by Design - Department of Computer Science

... All intelligence relies on search — for example, the search for an intelligent agent’s next action. Search is only likely to succeed in resource-bounded agents if they have already been biased towards finding the right answer. In artificial agents, the primary source of bias is engineering. This dis ...
questions and answers: reasoning and querying in description logic
questions and answers: reasoning and querying in description logic

Differential Equations - The University of Texas at Dallas
Differential Equations - The University of Texas at Dallas

Solving Systems of Linear Equations
Solving Systems of Linear Equations

What is a heuristic? - University of Alberta
What is a heuristic? - University of Alberta



Solving Systems of Linear Equations
Solving Systems of Linear Equations

... Solve for x in one of the equations. Substitute the expression for x into the other equation to find y. Then substitute the value of y into one of the original equations to find x. Method 2 Solve for y first. Solve for y in one of the equations. Substitute the expression for y into the other equatio ...
Amoeba-Based Emergent Computing: Combinatorial Optimization
Amoeba-Based Emergent Computing: Combinatorial Optimization

Dynamic domain splitting for numeric CSPs
Dynamic domain splitting for numeric CSPs

... Identifying the splitting constraint to reconsider upon a failure can be done in a way similar as mac-cbj identifies the point where to backtrack [22]. 2B-consistency filtering algorithm works by shrinking the domains, which are intervals: it does not remove values one by one but it removes a subint ...
logic, programming and prolog (2ed)
logic, programming and prolog (2ed)

... predicate logic including notions like language, interpretation, model, logical consequence, logical inference, soundness and completeness. The final section introduces the concept of substitution which is needed in subsequent chapters. Chapter 2 introduces the restricted language of definite progra ...
Homework Solution 5 (p. 331, 2, 4, 6, 7,10,13,17,19, 21, 23) 2. (a
Homework Solution 5 (p. 331, 2, 4, 6, 7,10,13,17,19, 21, 23) 2. (a

metaheuristic approaches for the berth allocation problem
metaheuristic approaches for the berth allocation problem

Chapter 5
Chapter 5

The Exploration of Greedy Hill-climbing Search in Markov
The Exploration of Greedy Hill-climbing Search in Markov

Schematic Invariants by Reduction to Ground Invariants
Schematic Invariants by Reduction to Ground Invariants

Artificial Intelligence UNIT I Page 1 of 116 CSE– Dhaanish Ahmed
Artificial Intelligence UNIT I Page 1 of 116 CSE– Dhaanish Ahmed

... “It is not my aim to surprise or shock you-but the simplest way I can summarize is to say that there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until-in a visible future-the range of problems they can ...
CS 345 - Programming Languages
CS 345 - Programming Languages

... member(X, [X | _]). member(X, [_ | Y]) :- member(X, Y). The test for membership succeeds if either: • X is the head of the list [X | _] • X is not the head of the list [_ | Y] , but X is a member of the remaining list Y ...
LAO*: A heuristic search algorithm that finds solutions with loops
LAO*: A heuristic search algorithm that finds solutions with loops

... addition, the evaluation function is updated for a subset of states that includes the current state. At the beginning of each trial, the current state is set to the start state. A trial ends when the goal state is reached, or after a specified number of steps. An important feature of trial-based RTD ...
Systems of Linear Equations - Kirkwood Community College
Systems of Linear Equations - Kirkwood Community College

LNCS 3258 - Full Dynamic Substitutability by SAT Encoding
LNCS 3258 - Full Dynamic Substitutability by SAT Encoding

... 13, 15, 34] so local forms such as neighbourhood interchangeability are much more commonly used: Definition. A value a for variable v is neighbourhood interchangeable with value b if and only if for every constraint on v, the values compatible with v = a are exactly those compatible with v = b [15]. ...
Termination of Higher-order Rewrite Systems
Termination of Higher-order Rewrite Systems

... replacing a part of an expression by another expression, according to the rules. The resulting expression may be rewritten again and again, giving rise to a reduction sequence. Such a sequence can be seen as a computation. Certain expressions are considered as results, or normal forms. A computation ...
Automated Theorem Proving in a First
Automated Theorem Proving in a First

... Despite the complexity of automated reasoning in first-order logic, several methods were found to be efficient for finding unsatisfiability for non-trivial problems. Modern state-or-the-art automated theorem provers are based on superposition calculus [32] and its refinements. Finding satisfiability ...
Pathfinding Algorithms in Multi
Pathfinding Algorithms in Multi

1 2 3 4 5 ... 33 >

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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