RIGID E-UNIFICATION
... solvable by computing these equivalence classes. Shostak proved that for computing the equivalence classes of all terms in ThE s t i , no terms that are not in ThE s t i have to be considered: If s can be derived from t using the equalities in E, then this can be done without using an intermediate t ...
... solvable by computing these equivalence classes. Shostak proved that for computing the equivalence classes of all terms in ThE s t i , no terms that are not in ThE s t i have to be considered: If s can be derived from t using the equalities in E, then this can be done without using an intermediate t ...
Qualitative and Quantitative Solution Diversity in Heuristic
... Pervasive science fiction, futurology, and the computer science research community have taught us to expect our world to be permeated by an emerging artificially intelligent population, which, according to our own (diverse) dispositions, needs, and fears, we may envision pragmatically as aids to a c ...
... Pervasive science fiction, futurology, and the computer science research community have taught us to expect our world to be permeated by an emerging artificially intelligent population, which, according to our own (diverse) dispositions, needs, and fears, we may envision pragmatically as aids to a c ...
Enforcement in Abstract Argumentation via Boolean Optimization
... the complexity class NP utilize iterative approaches, where e.g. SAT solvers are used as practical NP-oracles by calling them several times, refining the solution each time [32, 33, 56]. The computational problems where we perform acceptance queries on a given AF are in this work regarded as static, ...
... the complexity class NP utilize iterative approaches, where e.g. SAT solvers are used as practical NP-oracles by calling them several times, refining the solution each time [32, 33, 56]. The computational problems where we perform acceptance queries on a given AF are in this work regarded as static, ...
Automatic planning of manipulator transfer movements
... to bring estimates on the accuracy of part positions within specified bounds. A central technical issue in this approach is deriving the accuracy estimates from geometric relationships and local accuracy information. RAPT has focused on the specification of manipulator programs by specifying the des ...
... to bring estimates on the accuracy of part positions within specified bounds. A central technical issue in this approach is deriving the accuracy estimates from geometric relationships and local accuracy information. RAPT has focused on the specification of manipulator programs by specifying the des ...
Numerical solution of saddle point problems
... In the vast majority of cases, linear systems of saddle point type have real coefficients, and in this paper we restrict ourselves to the real case. Complex coefficient matrices, however, do arise in some cases; see, e.g., Bobrovnikova and Vavasis (2000), Mahawar and Sarin (2003) and Strang (1986, page ...
... In the vast majority of cases, linear systems of saddle point type have real coefficients, and in this paper we restrict ourselves to the real case. Complex coefficient matrices, however, do arise in some cases; see, e.g., Bobrovnikova and Vavasis (2000), Mahawar and Sarin (2003) and Strang (1986, page ...
A Partial Taxonomy of Substitutability and Interchangeability
... [Freuder, 1991]. At the end of the process, the leaves of the discrimination tree are annotated with the equivalence NI values for the variable. The complexity of this process is O(n2 d2 ), where n is the number of variables and d is the maximum domain size. Alternatively, one can build a refutatio ...
... [Freuder, 1991]. At the end of the process, the leaves of the discrimination tree are annotated with the equivalence NI values for the variable. The complexity of this process is O(n2 d2 ), where n is the number of variables and d is the maximum domain size. Alternatively, one can build a refutatio ...
Cooperative Heuristic Search with Software Agents - Aalto
... • Path diversity: A simple search space exploration visualization, together with execution data, suggests that the observed performance gain in favor of A! is due to the more focused nature of the search effort. • Heuristic impact: A better search heuristic improves A! performance relatively more th ...
... • Path diversity: A simple search space exploration visualization, together with execution data, suggests that the observed performance gain in favor of A! is due to the more focused nature of the search effort. • Heuristic impact: A better search heuristic improves A! performance relatively more th ...
as a PDF
... Some other approaches include genetic fuzzy neural networks and genetic fuzzy clustering, among others ...
... Some other approaches include genetic fuzzy neural networks and genetic fuzzy clustering, among others ...
Analysis and Numerics of the Chemical Master Equation
... is dimensionally exponential. For example, the state space of a system with 10 different types of particles, where each particle can be alive or dead, is the size of 210 . The state space for biological systems with more than 10 varieties of species becomes too large to compute. As larger dimensiona ...
... is dimensionally exponential. For example, the state space of a system with 10 different types of particles, where each particle can be alive or dead, is the size of 210 . The state space for biological systems with more than 10 varieties of species becomes too large to compute. As larger dimensiona ...
Survey of Applications Integrating Constraint Satisfaction and Case
... capturing the rules from which the system can reason. Rule acquisition can be a time consuming and unreliable process. CBR makes it unnecessary to formulate experiences into rules. Some problem domains in particular naturally provide cases as part of the standard problem-solving process. Other domai ...
... capturing the rules from which the system can reason. Rule acquisition can be a time consuming and unreliable process. CBR makes it unnecessary to formulate experiences into rules. Some problem domains in particular naturally provide cases as part of the standard problem-solving process. Other domai ...
Genetic algorithm
In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.