Intelligent Agents R & N Chapter 2

Rationality “A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome.” What does “best” mean? What’s the outcome? How much does it cost to get it? How can we compute the best expected outcome? Bounded rationality (satisficing search) Best: someone, usually outside the agent, has to decide

PEAS Descriptions and State Space Search PEAS: What’s observable in the world **Given by the task Performance Measure Environment Actuators Sensors State space search: Internal reasoning **Created by designer States Initial state Goal state(s) Successor function Costs (of paths and/or states)

How Do They Relate? Criminal defense lawyer Perry Mason style: PEAS: What’s observable in the world Performance Measure Environment Actuators Sensors State space search: Internal reasoning States Initial state Goal state(s) Successor function Costs (of paths and/or states) Environment: arrests, warrants, cops doing things, evidence collected Actuators: request warrents, interview witnesses, collect data States: models (usually incomplete) for what happened Successor function: inference engine that strings known facts together + ops corresponding to sensors But note that a lot of internal reasoning may happen in between externally visible actions performed by actuators

How Do They Relate? Driving to California: PEAS: What’s observable in the world Performance Measure Environment Actuators Sensors State space search: Internal reasoning States Initial state Goal state(s) Successor function Costs (of paths and/or states) Environment: roads, places Actuators:Turn the wheel, accelerate, brake States: models (usually incomplete) for what happened. Can be much higher level. Must ignore things that can go wrong, like detours, since info not available at planning time. Successor function: go from one location to another. This time, note that there are a lot more physical actions than there are planning steps.

PEAS Examples Perf Envir Acts Sensors Measure Robot soccer player Internet book-shopping agent Autonomous Mars rover Mathematicians’s assistant Ask Jeeves School lunch planning

Performance Measures Two kinds of considerations: Where do we end up? How much does it cost (in time, gas, or whatever) to get there? Sometimes only one of these matters, sometimes they both do. Examples: Just 1 matters: Just 2 matters: Both 1 and 2 matter: 1: chess 2: Mars Rover: learn as much as possible as cheaply as possible 3: Defense lawyer

Is the Solution a Path or a State? Sometimes the solution is a state: But sometimes we need to output the best path that we found: State: find a consistent interpretation for a sentence like, “the bank president ate a dish of pasta salad with a fork” book shopping agent school lunch planner Path: Route finding theorem proving

Characterizing the Environment Obs Det Epi Static Disc Agents Complexity Soccer player Defense lawyer Ask Jeeves Episodic: maybe no at lower levels but yes at higher. For lawyer, no within a case, yes across cases. Static vs. dynamic Discrete vs continuous applied to state, time, percepts or actions Complexity – not in book. How regular or not are the connections between states? Another way to look at this? How much knowledge do you have to have about the domain?