Podcast
Questions and Answers
What does the acronym PEAS stand for in the context of intelligent agents?
What does the acronym PEAS stand for in the context of intelligent agents?
Which characteristic distinguishes agents from other software types?
Which characteristic distinguishes agents from other software types?
What aspect of intelligent agents allows them to adapt to environmental changes?
What aspect of intelligent agents allows them to adapt to environmental changes?
How do intelligent agents communicate to accomplish tasks?
How do intelligent agents communicate to accomplish tasks?
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What role do actuators play in an intelligent agent's functionality?
What role do actuators play in an intelligent agent's functionality?
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What defines a rational action according to the performance measure?
What defines a rational action according to the performance measure?
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How does rationality differ from omniscience?
How does rationality differ from omniscience?
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Which statement accurately describes the concept of rationality in artificial intelligence?
Which statement accurately describes the concept of rationality in artificial intelligence?
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What is meant by the autonomy of an agent?
What is meant by the autonomy of an agent?
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Which statement best reflects the relationship between rationality and success?
Which statement best reflects the relationship between rationality and success?
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What is the primary purpose of an agent's look-up table?
What is the primary purpose of an agent's look-up table?
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Which of the following best describes a rational agent?
Which of the following best describes a rational agent?
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What role does architecture play in the context of intelligent agents?
What role does architecture play in the context of intelligent agents?
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Which of the following is NOT a component of rationality in agents?
Which of the following is NOT a component of rationality in agents?
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How do agents utilize their percepts in terms of memory updates?
How do agents utilize their percepts in terms of memory updates?
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In the context of agent migration, what is the primary reason agents move between systems?
In the context of agent migration, what is the primary reason agents move between systems?
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What structure allows an agent to return an action based on its percept?
What structure allows an agent to return an action based on its percept?
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Which percepts are primarily involved in the vacuum-cleaner world?
Which percepts are primarily involved in the vacuum-cleaner world?
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What is an example of complete autonomy in agents?
What is an example of complete autonomy in agents?
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What is the primary goal of a Collision Avoidance Agent (CAA)?
What is the primary goal of a Collision Avoidance Agent (CAA)?
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In the PEAS model, which component relates to the functionalities of the agent's physical capabilities?
In the PEAS model, which component relates to the functionalities of the agent's physical capabilities?
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Which of the following reflects the environment of an automated taxi driver as part of its PEAS model?
Which of the following reflects the environment of an automated taxi driver as part of its PEAS model?
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What performance measure might characterize a spam filter agent?
What performance measure might characterize a spam filter agent?
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Which function do sensors serve in an agent's architecture?
Which function do sensors serve in an agent's architecture?
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How do Lane Keeping Agents (LKA) determine their actions?
How do Lane Keeping Agents (LKA) determine their actions?
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In a medical diagnosis system, which aspect is represented by the 'performance measure' component?
In a medical diagnosis system, which aspect is represented by the 'performance measure' component?
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What is a key challenge for conflict resolution in action selection agents?
What is a key challenge for conflict resolution in action selection agents?
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Study Notes
Intelligent Agents
- Agents are entities that perceive and act on their environment
- An agent's behavior is described by an agent function
- Agent = Architecture + Program
- An agent program runs in cycles of: perceive, think, and act
- Agent programs map percept histories to actions
Agent Function
- Maps percept histories to actions
- Formally represented as f: P* → A
Structure of Intelligent Agents
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Agent program: the implementation of the agent's perception-action mapping
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Function Skeleton-Agent(Percept) that returns an Action
- memory ← UpdateMemory(memory, Percept)
- Action ← ChooseBestAction(memory)
- memory ← UpdateMemory(memory, Action)
- return Action
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Architecture: a device capable of executing the agent program (e.g., computer)
Vacuum-cleaner World
- Percepts: Location (A or B) and contents (dirt or not), e.g., [A, Dirty]
- Actions: Left, Right, Suck, NoOp
- Agent's function: lookup table
- Lookup table is very large for many agents
Vacuum-cleaner Agent Function
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function Vacuum-Agent([location, status]) returns an action -
if status = Dirty then return Suck -
else if location = A then return Right -
else if location = B then return Left
Agent Function – Lookup Table
- A trivial agent program tracks the percept sequence to index into a table and then choose an action
- The designers create a table with the appropriate action for every percept sequence
- Drawbacks:
- Huge table (PT), P: set of possible percepts, T: lifetime
- Space to store the table
- Table takes a long time to build
- Limited autonomy
Rational Agent
- Strives to "do the right thing" based on perception and actions
- Right action maximizes the agent's success
- Performance measure: Objective criterion for agent's success.
Rationality
- Performance measuring success
- Agents prior knowledge of environment
- Actions that agent can perform
- Agent's percept sequence to date
- 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.
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Rational is different from omniscience (all knowing with infinite knowledge)
- Percepts may not supply all relevant information.
- E.g., in card game, don't know cards of others.
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Rational is different from being perfect
- Rationality maximizes expected outcome.
- Perfection (omniscience) maximizes actual outcome.
Back to Vacuum Cleaner Agent
- Is this agent rational?
- Depends on performance measure and environment properties
- Performance measure Awards one point for each clean square in a 1,000 time-step lifetime
- Geography of the environment is known a priori
- Dirt distribution and initial location are not known
- agent correctly perceives its location, and whether that location contains dirt
- Under these circumstances the agent is rational: its expected performance is at least as high as any other agent.
Vacuum Cleaner Agent – Irrational
- Same agent would be irrational under different circumstances
- Once all dirt is cleaned up, it oscillates needlessly
Autonomy in Agents
- The autonomy of an agent is determined by its own experience, rather than designer knowledge
- Extremes:
- No autonomy—ignores environment/data
- Complete autonomy—must act randomly/no program
- Example—baby learning to crawl.
Specifying Task Environment (PEAS)
- Performance measure, Environment, Actuators, Sensors (PEAS)
- The first step in designing an agent is specifying the task environment as fully as possible
PEAS – Examples
- Vacuum Cleaner
- Automated Taxi Driver
- Medical Diagnosis System
- Spam Filter
Interacting Agents
- Agents can interact with each other and resolve conflicts.
Collision Avoidance Agent (CAA)
- Goals: Avoid collisions with obstacles
- Percepts: Obstacle distance, velocity, trajectory
- Sensors: Vision, proximity sensing
- Actuators: Steering wheel, accelerator, brakes, horn, headlights
- Actions: Steer, speed up, brake, blow horn, signal (headlights)
- Environment: Freeway
Lane Keeping Agent (LKA)
- Goals: Stay in current lane
- Percepts: Lane center, lane boundaries
- Sensors: Vision
- Actuators: Steering wheel, accelerator, brakes
- Actions: Steer, speed up, brake
- Environment: Freeway
Conflict Resolution by Action Selection Agents
-
Arbitrate:
- If Obstacle is Close then CAA, else LKA
- Challenges: Doing the right thing
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Description
Explore the foundational concepts of intelligent agents, including their structure and functions. This quiz covers agent perception, action mapping, and specific examples like the vacuum-cleaner world. Test your understanding of how agents operate and interact with their environment.
