1. EA C461 – Artificial Intelligence Problem Solving Agents
S.P.Vimal

2. To discuss… Structure of an Agent Problem Solving Agents
Vimal EA C461- Artificial Intelligence

3. Structure of Agents Agent = Architecture + Program
Implements Agent Function, performs mapping of percepts to actions
Computing device Running Agent Program, with sensors & actuators
Vimal EA C461- Artificial Intelligence

4. Agent Programs
function Table-Driven-Agent (percept) returns an action
static : percepts, a sequence, initially empty
table, a table of actions, indexed by percept sequence, initially fully specified
append percept to the end of percepts
action  LookUp (percepts,table)
return action
The challenge is to devise a small piece of code which exhibit a rational behavior, rather than having a prohibitively large table implementing the desired behavior
For P percepts, T Life time, Lookup table will have Σt|P|t entries.
Large table size
Need small programs implement rational behavior
Vimal EA C461- Artificial Intelligence

5. Agent Programs Kinds of Agent Programs Simple Reflex Agents
Model-based Reflex Agents
Goal Based Reflex Agents
Utility-based Reflex Agents
Vimal EA C461- Artificial Intelligence

6. Simple Reflex Agents
Considers only the current percept, ignores rest of percept history
Condition-action rules encoded
If car-in-front-is-braking then initiate-braking
function Reflex-Vacuum-Agent ([location, status]) returns an action
If status=Dirty then return Suck
else if location=A then return Right
else if location=B return Left
function Simple-Reflex-Agent (percept) returns an action
static: rules, a set of condition-action rules
state  Interpret-Input (percept)
rule  Rule-Match (state, rules)
action  Rule-Action [rule]
return action
Vimal EA C461- Artificial Intelligence

7. Simple Reflex Agents
rectangles  the current internal state; Ovals  background information
Vimal EA C461- Artificial Intelligence

8. Model-Based Reflex Agents
Keep track of the part of the world which the agent can’t see now
Handling partial observability
Maintain an internal state depends the percept history
Updating internal state of the agent needs some information about
how the world evolves
the agent’s own action affects the world
Vimal EA C461- Artificial Intelligence

9. Model-Based Reflex Agents
rectangles  the current internal state; Ovals  background information
Vimal EA C461- Artificial Intelligence

10. Model-Based Reflex Agents
function Reflex-Agent-With-State (percept) returns an action
static: rules, a set of condition-action rules
state, a description of the current world state
action, the most recent action, initially none
state  Update-Input (state, action, percept)
rule  Rule-Match (state, rules)
action  Rule-Action [rule]
return action
Vimal EA C461- Artificial Intelligence

11. Goal-Based Agents
Having a goal, combined with the current state information can help select the possible next action
Possibly agent may need to consider every alternative action sequences leading to the goal  search for a sequence leading to goal
Vimal EA C461- Artificial Intelligence

12. Goal-Based Agents
rectangles  the current internal state; Ovals  background information
Vimal EA C461- Artificial Intelligence

13. Utility-Based Agents
Goals provide crude binary distinction between “happy” and “un happy”
If one state is preferred over the other, then it has higher utility for the agent
utility-function (state) = real number (degree of happiness)
Complete specification of utility-function allows rational decisions in the following circumstances
Taking decision when in presence of Conflicting goals
When there are several goals that the agent can aim for.
Vimal EA C461- Artificial Intelligence

14. Utility-Based Agents
rectangles  the current internal state; Ovals  background information
Vimal EA C461- Artificial Intelligence

15. Learning Agents Build a learning machine and teach it
Learning agent has the following components
Learning element
Suggests modification to the existing rule to the critic
Performance element
Collection of knowledge and procedures for selecting the driving actions
Choice depends on Learning element
Critic
Observes the world and passes information to the learning element
Problem generator
Identifies certain areas of behavior needs improvement and suggest experiments
Vimal EA C461- Artificial Intelligence

16. Learning Agents
rectangles  the current internal state; Ovals  background information
Vimal EA C461- Artificial Intelligence

17. Formulate Goal, Formulate Problem
Problem Solving Agent
A kind of Goal based Agent
Decides what to do by finding the sequences of actions that lead to desirable states
Formulate Goal, Formulate Problem
Search
Execute
Vimal EA C461- Artificial Intelligence

18. Problems Four components of problem definition
Initial state
Possible Actions
Uses a Successor Function
Returns <action, successor> pair
State Space
Path
Goal Test
Path cost
Step cost
Problem formulation is the process of deciding what actions and states to consider, given a goal
Vimal EA C461- Artificial Intelligence

19. Solutions
A Solution to the problem is the path from the initial state to the final state
Quality of solution is measured by path cost function
Optimal Solution has the lowest path cost among other solutions
An Agent with several immediate options of unknown value can decide what to do by first examining different possible sequences of actions that lead to a state of known value, and then choosing the best sequence  Searching Process
Input to Search : Problem
Output from Search : Solution in the form of Action Sequence
Vimal EA C461- Artificial Intelligence

20. Problem Solving Agent
A Problem solving Agent, Assuming the environment is
Static
Observable
Discrete
Deterministic
Vimal EA C461- Artificial Intelligence

21. Problem Solving Agent : Example
A Simplified Road Map of Part of Romania
Vimal EA C461- Artificial Intelligence

22. Problem Solving Agent : Example
On holiday in Romania; currently in Arad
Flight leaves tomorrow from Bucharest
Formulate goal:
be in Bucharest
Formulate problem:
states: various cities
actions: drive between cities
Find solution:
sequence of cities, e.g., Arad, Sibiu, Fagaras, Bucharest
Vimal EA C461- Artificial Intelligence

23. Example-1 : Vacuum World
Problem Formulation
States
2 x 22 = 8 states
Initial State
Any one of 8 states
Successor Function
Legal states that result from three actions (Left, Right, Suck)
Goal Test
All squares are clean
Path Cost
Number of steps (each step costs a value of 1)
Vimal EA C461- Artificial Intelligence

24. Example-1 : Vacuum World
State Space for the Vacuum World.
Labels on Arcs denote L: Left, R: Right, S: Suck
Vimal EA C461- Artificial Intelligence

25. Example-2 : The 8-Puzzle States ? Initial State ? Successor Function ?
A typical Instance of 8-puzzle
States ?
Initial State ?
Successor Function ?
Goal Test ?
Path Cost ?
Vimal EA C461- Artificial Intelligence

26. Example-2 : The 8-Puzzle States : Location of Tiles
A typical Instance of 8-puzzle
States : Location of Tiles
Initial State : One of States
Successor Function: Move blank left, Right, Up, down
Goal Test : Shown in Fig. Above
Path Cost : 1 for each step
Vimal EA C461- Artificial Intelligence