EA C461 Artificial Intelligence Problem Solving Agents

1. S.P.Vimal http://discovery.bits-pilani.ac.in/~vimalsp/1910AI/ EA C461 � Artificial IntelligenceProblem Solving Agents

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

3. Vimal EA C461- Artificial Intelligence Structure of Agents Agent = Architecture + Program

4. Vimal EA C461- Artificial Intelligence Agent Programs For P percepts, T Life time, Lookup table will have St|P|t entries. Large table size Need small programs implement rational behavior 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 behaviorThe 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

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

6. Vimal EA C461- Artificial Intelligence 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

7. Vimal EA C461- Artificial Intelligence Simple Reflex Agents

8. Vimal EA C461- Artificial Intelligence 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

9. Vimal EA C461- Artificial Intelligence Model-Based Reflex Agents

10. Vimal EA C461- Artificial Intelligence Model-Based Reflex Agents

11. Vimal EA C461- Artificial Intelligence 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

12. Vimal EA C461- Artificial Intelligence Goal-Based Agents

13. Vimal EA C461- Artificial Intelligence 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.

14. Vimal EA C461- Artificial Intelligence Utility-Based Agents

15. Vimal EA C461- Artificial Intelligence 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

16. Vimal EA C461- Artificial Intelligence Learning Agents

17. Vimal EA C461- Artificial Intelligence 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

18. Vimal EA C461- Artificial Intelligence 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

19. Vimal EA C461- Artificial Intelligence 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

20. Vimal EA C461- Artificial Intelligence Problem Solving Agent

21. Vimal EA C461- Artificial Intelligence Problem Solving Agent : Example

22. Vimal EA C461- Artificial Intelligence 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

23. Vimal EA C461- Artificial Intelligence 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)

24. Vimal EA C461- Artificial Intelligence Example-1 : Vacuum World

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

26. Vimal EA C461- Artificial Intelligence Example-2 : The 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