Basic Questions and Answers on Artificial Intelligence

1) What is AI?

Systems that think like humans

Systems that think rationally

Systems that act like humans

Systems that act rationally

2) Define an agent.

     An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators.

3) What is an agent function?

   An agent’s behavior is described by the agent function that maps any given percept sequence  to an action.

4) Differentiate an agent function and an agent program.

Agent Function Agent Program

An abstract mathematical description A concrete implementation,running on the agent Architecture.

5) What can Ai do today?

 

 

6) What is a task environment? How  it is specified?

Task environments   are essentially the "problems" to which rational agents are the "solutions."

A Task environment is specified using PEAS (Performance, Environment,    Actuators, Sensors) description.

     

7) Give an example of PEAS description for an automated taxi.

 

8) Give PEAS description for different agent types.

9) List the properties of task environments.

 Fully observable vs. partially observable.

 Deterministic vs. stochastic.

 Episodic vs. sequential

 Static vs, dynamic.

 Discrete vs. continuous.

 Single agent vs. multiagent.

10) Write a function for the table driven agent.

11) What are the four different kinds of agent programs?

Simple reflex agents;

Model-based reflex agents;

Goal-based agents; and

Utility-based agents.

12) Explain a simple reflex agent with a diagram.

Simple reflex agents

 The simplest kind of agent is the simple reflex agent. These agents select actions on the basis AGENT

of the current percept, ignoring the rest of the percept history.

 

13) Explain with a diagram the model based reflex agent.

13a)  Explain with a diagram the goal based reflex agent.

Knowing about the current state of the environment is not always enough to decide what

to do. For example, at a road junction, the taxi can turn left, turn right, or go straight on.

The correct decision depends on where the taxi is trying to get to. In other words, as well

as a current state description, the agent needs some sort of goal information that describes

situations that are desirable-for example, being at the passenger's destination.

13b) What are utility based agents?

Goals alone are not really enough to generate high-quality behavior in most environments.

For example, there are many action sequences that will get the taxi to its destination (thereby

achieving the goal) but some are quicker, safer, more reliable, or cheaper than others.

A utility function maps a state (or a sequence of states) onto a real number, which

describes the associated degree of happiness.

13c) What are learning agents?

A learning agent can be divided into four conceptual components, as shown in Fig-

 2.15. The most important distinction is between the learning element, which is re-

ELEMENT sponsible for making improvements, and the performance element, which is responsible for

selecting external actions. The performance element is what we have previously considered

to be the entire agent: it takes in percepts and decides on actions. The learning element uses

CRITIC feedback from the critic on how the agent is doing and determines how the performance

element should be modified to do better in the future.

 

Searching Techniques

14) Define the problem solving agent.

           A Problem solving agent is a goal-based agent . It decide what to do by finding sequence of actions that lead to desirable states. The agent can adopt a goal and aim at satisfying it. 

Goal formulation is the first step in problem solving.

      

15) Define the terms goal formulation and problem formulation.

               Goal formulation,based on the current situation and the agent’s performance measure,is the first step in problem solving. 

                       The agent’s task is to find out which sequence of actions will get to a goal state.

Problem formulation is the process of deciding what actions and states to consider given a goal

16) List the steps involved in simple problem solving agent.

(i) Goal formulation

(ii) Problem formulation

(iii) Search

(iv) Search Algorithm

(v) Execution phase

17) Define search and search algorithm.

                    The process of looking for sequences actions from the current state to reach the goal state is called search.

The search algorithm takes a problem as input and returns a solution in the form of action sequence. Once a solution is found,the execution phase consists of carrying out the recommended action..

18) What are the components of well-defined problems?

o The initial state that the agent starts in . The initial state for our agent of example problem is described by In(Arad)

o A Successor Function returns  the possible actions available to the agent. Given a state x,SUCCESSOR-FN(x) returns a set of {action,successor} ordered pairs where each action is one of the legal actions in state x,and each successor is a state that can be reached from x by applying the action.

      For example,from the state In(Arad),the successor function for the Romania  

       problem would return 

{ [Go(Sibiu),In(Sibiu)],[Go(Timisoara),In(Timisoara)],[Go(Zerind),In(Zerind)] }

o Thr goal test determines whether the given state is a goal state.

o A path cost function assigns numeric cost to each action. For the Romania problem the cost of path might be its length in kilometers.

19) Differentiate toy problems and real world problems.

TOY PROBLEMS REAL WORLD PROBLEMS

A toy problem is intended to illustrate various problem solving methods. It can be easily used by different  researchers to compare the performance of algorithms.

A real world problem is one whose solutions people actually care about.

20) Give examples of real world problems.

(i) Touring problems

(ii) Travelling Salesperson Problem(TSP)

(iii) VLSI layout

(iv) Robot navigation

(v) Automatic assembly sequencing

(vi) Internet searching

21) List the criteria to measure the performance of different search strategies.

o Completeness : Is the algorithm guaranteed to find a solution when there is one?

o Optimality : Does the strategy find the optimal solution?

o Time complexity : How long does it take to find a solution?

o Space complexity : How much memory is needed to perform the search?

22) Differentiate Uninformed Search(Blind search) and Informed Search(Heuristic Search) strategies.

Uninformed or Blind Search Informed or Heuristic Search

o No additional information beyond that provided in the problem definition

o Not effective

o No information about number of steps or path cost o More effective

o Uses problem-specific knowledge beyond the definition of the problem itself.

23) Define Best-first-search.

Best-first search is an instance of the general TREE-SEARCH or GRAPH-SEARCH algorithm in which a node is selected for expansion based on the evaluation function f(n ). Traditionally,the node with the lowest evaluation function is selected for expansion.