Artificial Intelligence with Uncertainty by Deyi Li, Yi Du

By Deyi Li, Yi Du

Via mathematical theories, types, and experimental computations, this ebook explores the uncertainties of data and intelligence that take place in the course of the cognitive techniques of people. It describes the cloud version, its uncertainties of randomness and fuzziness, and the correlation among them. The publication additionally facilities on different actual equipment for facts mining, reminiscent of the knowledge box and data discovery nation area. moreover, it offers an inverted pendulum instance to debate reasoning and regulate with doubtful wisdom in addition to offers a cognitive physics version to imagine human pondering with hierarchy.

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2. Obtain B from the resolution of ¬A ∨ B and A. 3. Obtain C from the resolution of B and ¬B ∨ C. 4. C and ¬C are contradictory, from which a permanently false clause (null clause) can be obtained by resolution. 5. So S is permanently false. This completes the proof. The resolution principle can be expanded properly. On one side, with the synergetic replacing method, resolution principle can be used to describe the predicate of the predicate (so called second-order predicate). It comes into being the secondorder predicate calculus.

4. 5. , forming symbol structures in the symbol system by finding out relations among symbols 6. , carrying on with its action process based on existing symbols A symbol system capable of completing the whole process is an integrated physical symbol system (PSS). fm Page 24 Friday, August 17, 2007 4:29 PM 24 Artificial Intelligence with Uncertainty so do up-to-date computers. Therefore, both the human and the computer are complete physical symbol systems. The physical symbol system hypothesis states that any system that exhibits intelligence is certain to carry out the above-mentioned six functions and that any system with the six functions is capable of exhibiting intelligence.

If the target function in the optimization problem is converted into the energy function of the network, and if we let the variables correspond to the state of the network, then the Hopfield neural network can be used to solve the combinatorial optimization problem. 3. While a Hopfield NN is running, the connection strengths or weights of each neuron remain fixed, only the output state is updated. For the network of the same structure, with the change of the network parameters (weights and thresholds), the number of minima of the energy function in the network (called “stabilized balance point in the system”) and the values of the minima also change.

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