More with Ch. 2 Ch. 3 Problem Solving Agents

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

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

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

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

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

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

RoboCup “By the year 2050, develop a team of fully autonomous humanoid robots that can win against the human world soccer champion team. “ (www.robocup.org). 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.

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.

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

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.

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

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

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

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

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

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” )

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

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” )

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

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]

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” )

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]

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