1. More with Ch. 2 Ch. 3 Problem Solving Agents

2. REVIEW - PEAS
To design a rational agent, we must specify the task environment (the “problems” to which rational agents are the “solutions”).
Performance measure
Environment
Actuators
Sensors

3. Environment Types
We often describe the environment based on six attributes.
Fully/partially observable
Deterministic/stochastic
Episodic/sequential
Static/dynamic
Discrete/continuous
Single agent/multiagent

4. Environment Types Categorization of environment tasks:
Fully/partially observable extent to which an agent’s sensors give it access to the complete state of the environment
Deterministic/stochastic (also strategic) extent to which the next state of the environment is determined by the current state and the current action

5. Environment Types Categorization of environment tasks:
Episodic/sequential extent to which the agent’s experience is divided into atomic episodes
Static/dynamic extent to which the environment can change while the agent is deliberating

6. Environment Types Categorization of environment tasks:
Discrete/continuous extent to which state of the environment, time, percepts and actions of the agent are expressed as a set of discrete values
Single agent/multiagent

9. Environment Types
The environment type largely determines the agent design
The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent

10. RoboCup
“By the year 2050, develop a team of fully autonomous humanoid robots that can win against the human world soccer champion team. “ (
Develop a PEAS description of the task environment for a RoboCup participant.
Include a classification of the environment using R&N’s six properties of task environments.

11. So Far… Traditional AI begins with some simple premises:
An intelligent agent lives in a particular environment.
An intelligent agent has goals that it wants to achieve.
The environment in which an agent is expected to operate has a large effect on what sort of behaviors it will need and what we should expect it to be able to do.

12. Chapter 3 : Problem Solving by Searching
“In which we see how an agent can find a sequence of actions that achieves its goals when no single action will do.”
Such agents must be able to:
Formulate a goal
Formulate the overall problem
Find a solution

13. Recently I gave you this problem
Three missionaries and three cannibals
Want to cross a river using one canoe.
Canoe can hold up to two people.
Can never be more cannibals than missionaries on either side of the river.
Aim: To get all safely across the river without any missionaries being eaten.

14. Problem Solving Agents
Formulate goal:
get everyone across the river
Formulate problem:
states: various combinations of people on either side of the river
actions: take the canoe (with some people) across the river
restrictions: certain combinations of people are “illegal”
Find solution:
sequence of canoe trips that get everybody (safely) across the river

15. Problem Solving Agents
Example: Traveling in Romania
On holiday in Romania; currently in Arad. Flight leaves tomorrow from Bucharest

16. Problem Solving Agents
Example: Traveling in Romania
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

17. Problem Solving Agents
Formulate goal:
get the set of rooms clean
Formulate problem:
states: various combinations of dirt and vacuum location
actions: right, left, suck, no-op
Find solution:
sequence of actions that cause all rooms to be clean

19. Appropriate environment for Searching Agents
Observable??
Deterministic??
Episodic??
Static??
Discrete??
Agents??
Yes
Either

20. Problem Types Deterministic, fully observable  single-state problem
Agent knows exactly which state it will be in
Solution is a sequence
Non-observable  conformant problem
Agent may have no idea where it is
Solution (if any) is a sequence
Nondeterministic and/or partially observable  contingency problem
percepts provide new information about current state
solution is a tree or policy
often interleave search, execution
Unknown state space  exploration problem ( “online” )

21. Problem Types Example: vacuum world Start in #5. Solution??
[Right, Suck]

22. Problem Types Deterministic, fully observable  single-state problem
Agent knows exactly which state it will be in
Solution is a sequence
Non-observable  conformant problem
Agent may have no idea where it is
Solution (if any) is a sequence
Nondeterministic and/or partially observable  contingency problem
percepts provide new information about current state
solution is a tree or policy
often interleave search, execution
Unknown state space  exploration problem ( “online” )

23. Problem Types
Conformant, start in {1,2,3,4,5,6,7,8} Solution??

24. Problem Types Conformant, start in {1,2,3,4,5,6,7,8}
e.g., Right goes to {2,4,6,8}.
[Right, Suck, Left, Suck]

25. Problem Types Deterministic, fully observable  single-state problem
Agent knows exactly which state it will be in
Solution is a sequence
Non-observable  conformant problem
Agent may have no idea where it is
Solution (if any) is a sequence
Nondeterministic and/or partially observable  contingency problem
percepts provide new information about current state
solution is a tree or policy
often interleave search, execution
Unknown state space  exploration problem ( “online” )

26. Problem Types Contingency, start in #5
Murphy’s Law: Suck can dirty a clean carpet
Local Sensing: dirt, location only.
Solution??
[Right, if dirt then Suck]

27. Problem Types Deterministic, fully observable  single-state problem
Agent knows exactly which state it will be in
Solution is a sequence
Non-observable  conformant problem
Agent may have no idea where it is
Solution (if any) is a sequence
Nondeterministic and/or partially observable  contingency problem
percepts provide new information about current state
solution is a tree or policy
often interleave search, execution
Unknown state space  exploration problem
“online” search