This document provides an overview of agents and rationality in artificial intelligence from the 1990s. It discusses key concepts like rational agents perceiving their environment and acting through sensors and actuators. Rational agents are defined as those that consider all possible actions and consequences to make optimal decisions given their goals and environment. The document also covers agent architectures like model-based agents that use internal models to select actions, as well as multi-agent systems and examples like the Prisoner's Dilemma.
3. Agent
0 Agent:
0 Perceive the environment through sensors.
0 Act on the environment through actuators.
0 The environment can be non‐physical.
0 Percept: the set of perceptions at some point in time.
0 Percept sequence: the set of a perception‐time pairs.
0 Agent function: percept sequence action
0 Agent program: an implementation of an agent
function.
0 Agent architecture
16. Multiagent
0 Cooperation
0 Competition
0 Swarm intelligence: performance measure applied to
collective behavior.
0 Decentralized representation
0 Emergent behavior
0 Weak emergence: the qualities of the system are
reducible to the system's constituent parts.
0 Strong emergence: e.g. qualia.
0 The concepts of utility and rationality change!
17. Prisoner’s dilemma
Prisoner B silent Prisoner B betray
Prisoner A silent A:0.5, B:0.5 A:10, B:0
Prisoner A betray A:0, B:10 A:5, B:5
Two suspects are arrested. If one testifies against the other (betray) and
the other remains silent, the betrayer goes free and the silent accomplice
receives the full 10‐year sentence. If both remain silent, both prisoners
are sentenced to only six months for a minor charge. If each betrays the
other, each receives a 5‐year sentence. How should the prisoners act?
• No matter what the other player does, a player will always
gain a greater payoff by playing defect.
• Since in any situation betraying is more beneficial than
remaining silent, all rational players will betray.