代写代考 FIT3080

INTELLIGENT AGENTS AND RATIONALITY FIT3080

• Agents and environments

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• Rationality
• PEAS (Performance measure, Environment, Actuators, Sensors) • Environment types

• An agent is anything that can be viewed as perceiving its environment through sensors, and acting upon that environment through actuators
• Human agent:
􏰀 eyes, ears and other organs for sensors
􏰀 hands, legs, mouth and other body parts for actuators
• Robotic agent:
􏰀 cameras and infrared range finders for sensors 􏰀 various motors for actuators
• Software / virtual agent:
􏰀 keyboard input, file input, receiving from network 􏰀 screen, file output, sending to network

Agents and Environments
• The agent function f maps from percept histories P⋆ to actions A: f: P⋆ → A
• The agent program runs on the physical architecture to produce f • agent = architecture + program

Example: Vacuum-cleaner World and Agent
• Percepts: location and contents, e.g., [A, Dirty] or [B, Clean]
• Actions: Left, Right, Vacuum
• Program:
if status = Dirty return Vacuum else if Location = A return Right else if Location = B return Left

Rationality and Rational Agents
• Rationality depends on 􏰀 Performance measure
􏰀 The agent’s prior knowledge of the environment 􏰀 The actions that the agent can perform
􏰀 The percept sequence to date
• Definition:
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 the agent’s built-in knowledge

Rational, Autonomous Agents
• Rationality is NOT omniscience
• Agents can perform actions to modify future percepts in order to obtain useful information
→ exploration, learning
• An agent is autonomous if its behavior is determined by its own experience

Task Environment – PEAS
To design a rational agent, we must specify the Task Environment • PEAS
􏰀 Performance measure 􏰀 Environment
􏰀 Actuators

PEAS – Automated Taxi example
• Performance measure
􏰀 Safe, fast, legal, comfortable trip, minimize fuel consumption, maximize profit
• Environment
􏰀 Road types, road contents, customers, operating conditions
• Actuators
􏰀 Control over the car, interfaces for informing other vehicles and informing passengers
􏰀 Cameras, sonar, speedometer, GPS, odometer, engine sensors, interface for receiving information from other vehicles and passengers (e.g., speech recognizer)

Performance measure considerations
Rational agent:
• maximize expected performance measure, considering both now and the future.
Performance Safe
Legal Comfortable
Fuel consumption
Every crash: -100 Arrives unharmed: +500 Every second -0.1
Every violation: -500 Every speed bump -10 Sudden turn, braking -50 Every liter: -0.5
General rule: better to design performance measures according to what one actually wants, than according to how one thinks an agent should behave

PEAS Example: Vision-Language Navigation
Chen et al., Topological Planning with Transformers for Vision-and-Language Navigation, CVPR 2021.

PEAS Example: Vision-Language Navigation
In break-out rooms,
• Check https://bringmeaspoon.org/ and the other links in this web-page (5 min) • Discuss what you think PEAS should be in this problem (5 min)
• Afterwards, we will discuss the responses

Environment Types (I)
The environment type largely determines the agent design
• Fully (partially) observable – An agent’s sensors give it access to the complete state of the environment at all times
• Known (unknown) – An agent knows the “laws” of the environment
• Single (multi) agent – An agent operating by itself in an environment
• Deterministic (stochastic) – The next state is completely determined by the current state and the action executed by the agent

Environment Types (II)
• Episodic (sequential) – The agent’s experience is divided into atomic episodes. The next episode does NOT depend on previous actions
􏰀 In each episode an agent perceives a percept and performs a single action
• Static (dynamic) – The environment is unchanged while an agent is deliberating
• Discrete (continuous) – Pertains to number of states, the way time is handled, and number of percepts and actions
􏰀 E.g., a state may be continuous, but actions may be discrete

Next week: Search algorithms

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