Introduction to Artificial Intelligence 2070

The Turing test, proposed by Alan Turing  was designed to convince the people that whether a particular machine can think or not. The test involves an interrogator who interacts with one human and one machine. Within a given time the interrogator has to find out which of the two the human is, and which one the machine.

To pass a Turing test, a computer must have following capabilities:

• Natural Language Processing: To communicate easily.
• Knowledge Representation: To store facts and rules.
• Automated Reasoning: To draw conclusion from stored knowledge.
• Machine Learning: To adopt new circumstances and detect pattern.

Additional requirements for the “total Turing test”: computer vision, speech recognition, speech synthesis, robotics.

Critics of Turing test:

• Test is not reproducible, amenable or constructive to mathematical analysis as it is more important to study the underlined principles of intelligence than to duplicate example.

• Trying to evaluate machine intelligence in terms of human intelligence is fundamental mistake. It focuses too much on the behavior of conversation.

Uninformed (Blind) Search does not use any domain knowledge. This means it does not any information to judge where the solution is likely to lie. Uninformed search methods use only the information available in the problem definition.

For E.g.

Breadth First Search

It expands the shallowest unexpanded node first. Starting from the root node (initial state) explores all children of the root node, left to right. If no solution is found, expands the first (leftmost) child of the root node, then expands the second node at depth 1 and so on …until a solution is found.

E.g.

An expert system is a set of program that manipulate encoded knowledge to solve problem in a specialized domain that normally requires human expertise.

An expert system's knowledge is obtained from expert sources and code in a form suitable for the system to use in its inference process.

Architecture of expert system

Knowledge Base (KB): The component of an expert system that contains the system’s knowledge is called its knowledge base. It contains the rules of in IF-THEN rule. The KB contains the knowledge necessary for understanding, formulating & solving problems. The KB of an expert system contains both declarative knowledge and procedural knowledge.

Inference Engine: It carries out the reasoning by interplaying the information or facts obtained from the user with the knowledge stored in knowledge base whereby the expert system reaches a solution.

Working memory: It is a data structure which stores information about a specific problem.

User Interface: The component of an expert system that communicates with the user is known as the user interface. The communication performed by a user interface is bidirectional. At the simplest level, we must be able to describe our problem to the expert system, and the system must be able to respond with its recommendations. 

Features of expert system

1. It is useful program/software.

2. It should help users to accomplish their goals in shortest possible way.

3. It should be educational when appropriate.

4. It should be able to respond to simple questions.

5. It should be able to learn new knowledge.

6. It should be easily modified.

7. It should be goal oriented.

8. It should be adaptive and flexible.

Conceptual graph is a graph representation for logic based on the semantic networks and the existential graphs of Charles sanders Peirce. 

A conceptual graph consists of concept nodes and relation nodes.

• The concept node represents entities, attributes, states and events.
• The relation node show how the concepts are interrelated.

Conceptual graph for given sentence:

Machine learning is a branch of AI that provides computer the ability to learn and improve its learning from experience without being written rules explicitly. It focuses on prediction based on known properties learned from the training data.

Learning by Analogy

• Learning by analogy means acquiring new knowledge about an input entity by transferring it from a known similar entity.

• This technique transforms the solutions of problems in one domain to the solutions of the problems in another domain by discovering analogous states and operators in the two domains.

E.g.  Infer by analogy the hydraulics laws that are similar to Kirchhoff's laws.

Instance Based Learning

• Instance based learning is a supervised classification learning algorithm that performs operation after comparing the current instances with the previously trained instances, which have been stored in memory.
• Time complexity of Instance based learning algorithm depends upon the size of training data. Time complexity of this algorithm in worst case is O (n), where n is the number of training items to be used to classify a single new instance.
• To improve the efficiency of instance based learning approach, preprocessing phase is required. Preprocessing phase is a data structure that enables efficient usage of run time modeling of test instance.
• Advantage of using Instance based learning over others is that it has the ability to adapt to previously unseen data, which means that one can store a new instance or drop the old instance.

Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations.

• Bayesian Network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
• Nodes in the graph represent the random variables and the directed edges between nodes represent conditional dependencies.
• The edge exists between nodes if there exists conditional probability i.e. a link from X to Y means Y is dependent of X.
• Each nodes are labelled with probability.

E.g.

P(x) = 0.5

P(y/x) = 0.7

P(z) = 0.8

P(u/y) = 0.47

Inference with Bayesian Network:

        Using a Bayesian network to compute probabilities is called inference in Bayesian network.

The first task is to compute the posterior probability distribution for the query variable X, given some assignment of values e to the set of evidence variable E = E₁,......., E_n and the hidden variables are Y = Y₁,.....Y_n. From the full joint probability distribution we can answer the query P(X/e) by computing

        P(x/e) = αP(X, e) = αΣ_Y(X, e, Y)

A Bayesian network gives a complete representation of the full joint distribution, specifically, the terms P(X, e, Y) can be written as products of conditional probabilities from the network.

        Therefore, a query can be answered using a Bayesian network by computing sums of products of conditional probabilities from the network.