Chapter 2.1 - 2.3

Agents and Environments

• Agent
• Environment
• Sensors (percept, percept sequence)
• Actuators (actions)
• Figure 2.1
• An agent function maps a percept sequence to an action
• Vacuum World: Figures 2.2, 2.3

Good Behavior: The Concept of Rationality

• Performance measure: it is better to design the performance measure according to what one actually wants in the environment, rather than according to how one thinks the agent should behave
• Rationality depends on performance measure, prior knowledge, performable actions, percept sequence
• Rational agent: for each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has
• Rationality does not mean omniscience - an agent tries to maximize its expected performance, not its actual performance
• Agents can engage in information gathering, exploration and learning

Task Environments

• PEAS: performance measure, environment, actuators, sensors
• Figures 2.4 and 2.5

Task Environment Properties

• Fully observable vs. partially observable
• Deterministic vs. stochastic
• Episodic vs. sequential
• Static vs. dynamic
• Discrete vs. continuous
• Single agent vs. multiagent
• Figure 2.6

Agent Examples

• AI Agent
• Robot Swarms