Artificial Intelligence A Guide To Intelligent Systems 3rd Edition Michael Negnevitsky - Solutions

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How should we change the ANFIS architecture shown in Figure 8.10 if we want to implement a zero-order Sugeno fuzzy model? x1 x2- Layer 1 Layer 2 A1 A2 B1 B2 Layer 3 II1 Π2 113 114 Layer 4 N1 N2 N3 N4 x1 x2 Layer 5 Layer 6 Figure 8.10 Adaptive neuro-fuzzy inference system (ANFIS) त 2 3 4 Σ
Develop a rule-based expert system for diagnosing an air conditioning system. An air conditioning system is a complex device with many areas and parts which are not accessible to an untrained person. Despite this, several things can be checked before calling for service. This expert system should
Define artificial intelligence as a science. When was artificial intelligence born?
Define expert systems. What is the main difference between weak methods and the expert system technology?
List the common characteristics of early expert systems such as DENDRAL, MYCIN and PROSPECTOR.
What are the premises on which fuzzy logic is based? When was fuzzy set theory introduced?
What are the main advantages of applying fuzzy logic in knowledge-based systems?
What is knowledge? Explain why experts usually have detailed knowledge of a limited area of a specific domain. What do we mean by heuristic?
What is a production rule? Give an example and define two basic parts of the production rule.
What are the fundamental characteristics of an expert system? What are the differences between expert systems and conventional programs?
Can an expert system make mistakes? Why?
What is a prior probability? Give an example of the rule representation in the expert system based on Bayesian reasoning.
Define inheritance in frame-based systems. Why is inheritance an essential feature of the frame-based systems?
What are the problems with using a perceptron as a biological model? How does the perceptron learn? Demonstrate perceptron learning of the binary logic function OR.Why can the perceptron learn only linearly separable functions?
What is a recurrent neural network? How does it learn? Construct a single six-neuron Hopfield network and explain its operation. What is a fundamental memory?
Derive the Hopfield network training algorithm. Demonstrate how to store three fundamental memories in the six-neuron Hopfield network.
What is the difference between autoassociative and heteroassociative types of memory? What is the bidirectional associative memory (BAM)? How does the BAM work?
Derive the BAM training algorithm. What constraints are imposed on the storage capacity of the BAM? Compare the BAM storage capacity with the storage capacity of the Hopfield network.
What does Hebb’s Law represent? Derive the activity product rule and the generalised activity product rule. What is the meaning of the forgetting factor? Derive the generalised Hebbian learning algorithm.
What is competitive learning? What are the differences between Hebbian and competitive learning paradigms? Describe the feature-mapping Kohonen model. Derive the competitive learning algorithm.
Describe a typical process of the development of a genetic algorithm for solving a real problem. What is the fundamental difficulty of genetic algorithms?
What is an evolution strategy? How is it implemented? What are the differences between evolution strategies and genetic algorithms?
Why are fuzzy systems particularly well suited for modelling human decision making?Why does fuzzy technology have great potential in areas such as business and finance?
Define intelligence. What is the intelligent behaviour of a machine?
Describe the Turing test for artificial intelligence and justify its validity from a modern standpoint.
What are weak methods? Identify the main difficulties that led to the disillusion with AI in the early 1970s.
What are the limitations of expert systems?
What are the differences between expert systems and artificial neural networks?
Why was the field of ANNs reborn in the 1980s?
What are the benefits of integrating expert systems, fuzzy logic and neural computing?
List and describe the five major players in the expert system development team. What is the role of the knowledge engineer?
What is an expert system shell? Explain why the use of an expert system shell can dramatically reduce the development time of an expert system.
What is a production system model? List and define the five basic components of an expert system.
Describe the forward chaining inference process. Give an example.
Describe the backward chaining inference process. Give an example.
List problems for which the forward chaining inference technique is appropriate. Why is backward chaining used for diagnostic problems?
What is a conflict set of rules? How can we resolve a conflict? List and describe the basic conflict resolution methods.
List advantages of rule-based expert systems. What are their disadvantages?
What is uncertainty? When can knowledge be inexact and data incomplete or inconsistent? Give an example of inexact knowledge.
What is probability? Describe mathematically the conditional probability of event A occurring given that event B has occurred. What is the Bayesian rule?
What is Bayesian reasoning? How does an expert system rank potentially true hypotheses? Give an example.
Why was the PROSPECTOR team able to apply the Bayesian approach as an uncertainty management technique? What requirements must be satisfied before Bayesian reasoning will be effective?
What are the likelihood of sufficiency and likelihood of necessity? How does an expert determine values for LS and LN?
How does a rule-based expert system propagate uncertainties using the Bayesian approach?
Why may conditional probabilities be inconsistent with the prior probabilities provided by the expert? Give an example of such an inconsistency.
Why is the certainty factors theory considered as a practical alternative to Bayesian reasoning? What are the measure of belief and the measure of disbelief? Define a certainty factor.
How does an expert system establish the net certainty for conjunctive and disjunctive rules? Give an example for each case.
How does an expert system combine certainty factors of two or more rules affecting the same hypothesis? Give an example.
Compare Bayesian reasoning and certainty factors. Which applications are most suitable for Bayesian reasoning and which for certainty factors? Why? What is a common problem in both methods?
What is fuzzy logic? Who are the founders of fuzzy logic? Why is fuzzy logic leading to more human intelligent machines?
What are a fuzzy set and a membership function? What is the difference between a crisp set and a fuzzy set? Determine possible fuzzy sets on the universe of discourse for man weights.
Define a linguistic variable and its value. Give an example. How are linguistic variables used in fuzzy rules? Give a few examples of fuzzy rules.
What is a hedge? How do hedges modify the existing fuzzy sets? Give examples of hedges performing operations of concentration, dilation and intensification. Provide appropriate mathematical expressions and their graphical representations.
Define main operations of fuzzy sets. Provide examples. How are fuzzy set operations, their properties and hedges used to obtain a variety of fuzzy sets from the existing ones?
What is a fuzzy rule? What is the difference between classical and fuzzy rules? Give examples.
Define fuzzy inference. What are the main steps in the fuzzy inference process?
How do we evaluate multiple antecedents of fuzzy rules? Give examples. Can different methods of executing the AND and OR fuzzy operations provide different results? Why?
What is clipping a fuzzy set? What is scaling a fuzzy set? Which method best preserves the original shape of the fuzzy set? Why? Give an example.
What is defuzzification? What is the most popular defuzzification method? How do we determine the final output of a fuzzy system mathematically and graphically?
What are the differences between Mamdani-type and Sugeno-type fuzzy inferences?What is a singleton?
What are the main steps in developing a fuzzy expert system? What is the most laborious and tedious part in this process? Why?
What is a frame? What are the class and instances? Give examples.
Design the class-frame for the object Student, determine its attributes and define several instances for this class.
What is a facet? Give examples of various types of facets.
What is the correct level of decomposition of a problem into frames, slots and facets?Justify your answer through an example.
How are objects related in frame-based systems? What are the ‘a-kind-of’ and ‘a-partof’ relationships? Give examples.
Can a frame inherit attributes from more than one parent? Give an example.
What is a method? What are the most popular types of methods used in frame-based expert systems?
What is a demon? What are the differences between demons and methods?
What are the differences, if any, between rules used in rule-based expert systems and those used in frame-based systems?
What are the main steps in developing a frame-based expert system?
List some advantages of frame-based expert systems. What are the difficulties involved in developing a frame-based expert system?
How does an artificial neural network model the brain? Describe two major classes of learning paradigms: supervised learning and unsupervised (self-organised) learning.What are the features that distinguish these two paradigms from each other
What is a fully connected multilayer perceptron? Construct a multilayer perceptron with an input layer of six neurons, a hidden layer of four neurons and an output layer of two neurons. What is a hidden layer for, and what does it hide?
How does a multilayer neural network learn? Derive the back-propagation training algorithm. Demonstrate multilayer network learning of the binary logic function Exclusive-OR.
What are the main problems with the back-propagation learning algorithm? How can learning be accelerated in multilayer neural networks? Define the generalised delta rule.
The delta rule and Hebb’s rule represent two different methods of learning in neural networks. Explain the differences between these two rules.
Why are genetic algorithms called genetic? Who was the ‘father’ of genetic algorithms?
What are the main steps of a genetic algorithm? Draw a flowchart that implements these steps. What are termination criteria used in genetic algorithms?
What is the roulette wheel selection technique? How does it work? Give an example.
How does the crossover operator work? Give an example using fixed-length bit strings.Give another example using LISP S-expressions.
What is mutation? Why is it needed? How does the mutation operator work? Give an example using fixed-length bit strings. Give another example using LISP S-expressions.
Why do genetic algorithms work? What is a schema? Give an example of a schema and its instances. Explain the relationship between a schema and a chromosome. What is the Schema Theorem?
Draw a block-diagram of the (1 + 1)-evolution strategy. Why do we vary all the parameters simultaneously when generating a new solution?
What is genetic programming? How does it work? Why has LISP become the main language for genetic programming?
What is a LISP S-expression? Give an example and represent it as a rooted point-labelled tree with ordered branches. Show terminals and functions on the tree.
What are the main steps in genetic programming? Draw a flowchart that implements these steps. What are the advantages of genetic programming?
What is a hybrid intelligent system? Give an example. What constitutes the core of soft computing? What are the differences between ‘hard’ and ‘soft’ computing?
Why is a neural expert system capable of approximate reasoning? Draw a neural knowledge base for a three-class classification problem. Suppose that an object to be classified is either an apple, an orange or a lemon.
Why are fuzzy systems and neural networks considered to be natural complementary tools for building intelligent systems? Draw a neuro-fuzzy system corresponding to the Sugeno fuzzy inference model for the implementation of the AND operation. Assume that the system has two inputs and one output, and
Describe the functions of each layer in a neuro-fuzzy system. How is fuzzification done in this system? How does a fuzzy rule neuron combine its multiple inputs? How is defuzzification done in neuro-fuzzy systems?
How does a neuro-fuzzy system learn? What system parameters are learned or tuned during training? How does a neuro-fuzzy system identify false rules given by a human expert? Give an example.
Describe the functions of each layer of an ANFIS. What are activation functions used by fuzzification neurons in Jang’s model? What is a normalised firing strength of a fuzzy rule?
How does an ANFIS learn? Describe a hybrid learning algorithm. What are the advantages of this algorithm?
A set of rules shown below uses certainty factors for assessing chest pain complaints.Determine the certainty of heart disease if the patient suffers from chest pain and his or her electrocardiogram is positive. Provide a full trace of the reasoning process. control of Rule: 1 if chest pain is
What are the differences between a neuro-fuzzy system corresponding to the Mamdani fuzzy inference model and an ANFIS?
How is a set of weights of a neural network encoded in a chromosome? Give an example. Describe the genetic operations used to optimise the weights of a neural network.
How is a neural network topology encoded in a chromosome? Give an example. Outline the main steps of a basic genetic algorithm for evolving an optimal neural network topology.
What is a grid–fuzzy partition? Give an example. Why are multiple fuzzy rule tables needed for a complex pattern classification problem? Describe a genetic algorithm for selecting fuzzy IF-THEN rules.
What is knowledge engineering? Describe the main steps in knowledge engineering.Why is choosing the right tool for the job the most critical part of building an intelligent system?

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Artificial Intelligence A Guide To Intelligent Systems 3rd Edition Michael Negnevitsky - Solutions

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