Conventional models based on crisp regions can not deal with the Direction Relations between Uncertain Regions (DRUR). Using broad boundary to represent the uncertain boundary, a novel approach is proposed based on mo...
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Conventional models based on crisp regions can not deal with the Direction Relations between Uncertain Regions (DRUR). Using broad boundary to represent the uncertain boundary, a novel approach is proposed based on model SK for modeling DRUR in this paper. DRUR are described as the combinations of basic cardinal direction relations, then we study the composition of DRUR, and a method is put forward for calculating this composition.
The major problem in developing a useful formalism for reasoning about spatial information is the trade off between expressive power and computational tractability. Based on Bennett's modal representation of conve...
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The major problem in developing a useful formalism for reasoning about spatial information is the trade off between expressive power and computational tractability. Based on Bennett's modal representation of convex-hull and the RCC62 model which describes the topological relation between simple concave regions, the modal representation of RCC62 is presented in this paper, which has a theoretical advantage over 1st-order representations.
In Deep Web, there are various methods of studying extraction of data records. After carefully reviewing and analyzing those methods, we find that the problem of dealing with either zero or fewer query results and nes...
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In recent years, many scientists have done some work in monitoring the fog, and achieved fruitful results. Now we want to conduct more in-depth study. In this paper, we utilize MODTRAN to simulate the relationship bet...
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The satisfiability(SAT) problem is a core problem of artificial intelligence. Research findings in SAT are widely used in many areas. The main methods solving SAT problem are resolution principle, tableau calculus and...
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ISBN:
(纸本)9781424458219;9781424458240
The satisfiability(SAT) problem is a core problem of artificial intelligence. Research findings in SAT are widely used in many areas. The main methods solving SAT problem are resolution principle, tableau calculus and extension rule. Besides methods mentioned above, we find that the SAT problem can be solved with hitting set algorithms. If a set of clause is satisfiable, there must be a hitting set of the clause set which containing no complementary pairs of literals. Algorithm NEYVHS-tree is an efficient hitting set algorithm proposed by Ouyang. RNHST proposed in this paper is a revised algorithm in respect of ***-tree. It judges the satisfiability of a clause set by confirming the existence of the set's hitting set without complementary pairs of literals. The test result shows that RNHST is an efficient algorithm.
For the optimization problem about triangulation of Bayesian networks, a novel genetic algorithm, DHGA, is proposed in this paper. DHGA employs a heuristic-based mutation operation. Moreover, it uses population divers...
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ISBN:
(纸本)9781424476718
For the optimization problem about triangulation of Bayesian networks, a novel genetic algorithm, DHGA, is proposed in this paper. DHGA employs a heuristic-based mutation operation. Moreover, it uses population diversity to identify stagnation and convergence as well as to guide the search procedure. Experiments on representative benchmarks show that DHGA posses better performance and robustness than other swarm intelligence methods.
We have studied the AC-4 algorithm and then present key value ordering heuristic forming the new solving algorithm BT-KVV, which is based on the AC-4 algorithm. This algorithm takes full advantage of the state inf...
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ISBN:
(纸本)9781424479573
We have studied the AC-4 algorithm and then present key value ordering heuristic forming the new solving algorithm BT-KVV, which is based on the AC-4 algorithm. This algorithm takes full advantage of the state information of the data structure used in the AC-4 algorithm after the process of arc consistency. The algorithm sorts the values of the variables' domain according to the key importance of the values. So this order forces the solving algorithm to give priority to extend the key values of variables. In this way, the efficiency of the solving algorithm can be improved a lot. The result of our experiments shows that our algorithm has much more advantage over other solving algorithms.
Essential graph is a graphical representation for Markov equivalence classes of Bayesian networks. Learning essential graph can avoid some problems in traditional Bayesian networks learning algorithms: (1) the number ...
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Essential graph is a graphical representation for Markov equivalence classes of Bayesian networks. Learning essential graph can avoid some problems in traditional Bayesian networks learning algorithms: (1) the number of illegal structures is exponential, which infect the efficiency of structure learning;(2) comparing the structures in same equivalent class slow down the speed of convergence;(3) if the prior distribution for each structure is equal, the more structures contain in the equivalent class the higher prior probability of the class has. This paper employs two competitive bio-inspired algorithms, immune algorithm and co-evolutionary algorithm, for learning Essential graph. The algorithm combines dependency analysis and search-scoring approach together. Experiments show that the searching space was decreased, compare with prior works, the convergence speed and the efficiency was improved.
We researched one search algorithm based on direction in the unstructured P2P network, analyzed the limited insufficiencies of this algorithm to the search speed, we proposed the improved direction search algorithm ba...
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Vehicular ad hoc network (VANET) is a kind of self-organizing ad hoc network, which is specifically designed for communication among vehicles. In VANET, a source vehicle must rely on intermediate vehicles to forward i...
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