Introduction to Artificial Intelligence 2068

a. Medical Diagnosis System: Utility Based Agent

b. Satellite image analysis system: Goal Based Agent

c. Part-picking robot: Goal Based Agent

d. Refinery Controller: Utility Based Agent

The state space is commonly defined as a directed graph in which each node is a state and each arc represents the application of an operator transforming a state to a successor state. A solution is a path from the initial state to a goal state.

State Space representation of Vacuum World Problem:

States: two locations with or without dirt: = 8 states.

Initial state:  Any state can be initial

Actions:  {Left, Right, Suck}

Goal test: Check whether squares are clean.

Path cost:  Number of actions to reach goal.

 Representing the root finding problem having four cities in to state representation:

The above problem can be formulate as:

States: All four cities. {Oradea, Zerind, Sibiu, Arad}

Initial State:  Current city where we are. For e.g. Oradea

Actions: Drive between cities or choose next city.

Goal test: Check whether the agent is in Arad and 4 cities have been visited.

Path Cost: Sum of distances.

    A solution is a sequence of actions leading from the initial state to a goal state

The information which is used to search the space more efficiently is called heuristic information.

Ways of using heuristic information:

• Deciding which node to expand next, instead of doing the expansion in a strictly breadth-first or depth-first order;

• In the course of expanding a node, deciding which successor or successors to generate, instead of blindly generating all possible successors at one time;

• Deciding that certain nodes should be discarded, or pruned, from the search space.

The function g(n) gives the cost of the path from initial state to the node n. Using h(n) = -g(n) for the heuristic function in a greedy search, then, will cause the algorithm to always select the node with the highest path cost so far (the largest g(n)) to expand next, since this will give us the smallest h(n) (i.e. the most negative value). If all operations have the same cost value associated with them, then the largest g(n) will always correspond to the longest path in the search tree and the greedy search will emulate depth-first search.

If we set h(n) = g(n) we get breadth first search.

Knowledge Representation is an area of AI whose fundamental goal is to represent knowledge in manner that facilitates inference i.e. drawing conclusion for knowledge. It analyzes how to think formally, how to use symbol to represent a domain of discourse along with function that allow inference about the objects.

Characteristics of knowledge representation:

i. Represential Adequacy:

    It is the ability to represent all kind of knowledge that are needed in the domain.

ii. Inferential Adequacy:

    It is the ability to manipulate the represential structure in such a way as to derive new structures corresponding to new knowledge inferred from old.

iii. Inferential Efficiency:

    It is the ability to incorporate into the knowledge structure additional information that can be used to focus the attention of the inference mechanism in the most efficient directions.

v. Acquisitional Efficiency:

    It is the ability to acquire new information easily.

Model Based System

• A model-based system is based on a model of the structure and behavior of the device that the system is designed to simulate.
• It is used for well structured problems. E.g. Engineering problems: Diagnosing hardware or  a machine, Automobile diagnostics
• It is based on written document.
• Observed behavior (what the device is actually doing) is compared with predicted behavior (what the device should do).

Case Based System

• A case-based system is a collection of a set of cases. It stores a case in the Case Base.
• It is based on human information processing (HIP) model in some problem areas: Thinking about how human processes information, Try to remember previous case/recall similar cases & modify to fit a new situation.
• E.g. Law, diagnosis, strategic planning
• It retrieves cases relevant to the present problem situation from the case base and decides on the solution to the current problem on the basis of the outcomes from the previous cases.

a. Electronic Circuit Testing --> Model based system is suitable.

b. Legal Reasoning --> Case based system is suitable.

c. Disease Recognition --> Case based system is suitable.

Baye's theorem is a way to apply conditional probability for prediction. Conditional probability is the probability of an event happening, given that it has some relationship to one or more other events.

Mathematically, Baye's theorem is defined as:

Proof:

Bayes theorem provides a way to resolve existing predictions or theories (update probabilities) given new or additional evidence. This, in turn, makes the prediction more accurate.

Applications of Bayes theorem:

1.  Medical science: Baye's rule is used for predicting a particular disease based on the symptoms and physical condition of the patient.

2. Weather forcasting: Baye's rule is a powerful algorithm for predictive modeling weather forcast.

3. Robotics: Baye's rule is used to calculate the probability of a robot's next steps given the steps the robot has already executed.

4. Finance: Baye's theorem can be used to rate the risk of lending money to potential borrowers.

    etc.