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Questions and Answers
What is the term used to describe the computing devices and physical sensors and actuators that agent programs run on?
What is a limitation of table driven agents?
What do simple reflex agents ignore when selecting actions?
What is a characteristic of model-based agents?
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What is the term used to describe how the world changes over time in model-based agents?
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What does PEAS stand for?
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What type of agents are of limited intelligence?
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What is a fully observable task environment?
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Why are simple reflex agents often unavoidable in partially observable environments?
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What is a single-agent task environment?
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How many categories of agents based on their agent programs are mentioned?
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What is a deterministic task environment?
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What is an episodic task environment?
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What is a dynamic task environment?
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What is a discrete task environment?
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What is a known task environment?
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What is an agent?
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What is a percept sequence?
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What determines an agent's behavior?
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What is the term used to describe the knowledge about how the state of the world is reflected in the agent’s percepts?
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What type of agents are less efficient but more flexible?
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What is a rational agent?
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What philosophy is often used in AI to determine an agent's actions?
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What is the purpose of the utility function in an agent?
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What is the primary advantage of a learning agent?
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What are used to evaluate an agent's behavior or actions?
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What is the component of a learning agent responsible for making improvements?
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What is a task environment?
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What is a widely accepted method of describing task environments?
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What type of agent combines its goal with the knowledge described by the transition model and sensor models?
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What is the primary purpose of a goal-based agent?
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What is the term used to describe the internalization of the performance measure in an agent?
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What is the primary function of the performance element in a learning agent?
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What is the role of the critic in a learning agent?
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What is the primary function of the problem generator in a learning agent?
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Which of the following terms is related to the ability of an AI agent to make decisions based on its own intentions and goals?
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Which of the following terms is related to the ability of an AI agent to make optimal decisions based on complete knowledge of the environment?
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Which of the following terms is related to the ability of an AI agent to improve its performance over time based on feedback?
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Study Notes
Intelligent Agents
- An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators.
- An agent's percept sequence is the complete history of everything the agent has ever perceived.
Rationality
- A rational agent is one that does the right thing, determined using methods such as consequentialism philosophy.
- Rationality depends on:
- Performance measure that defines the criterion of success
- Agent's prior knowledge of the environment
- Actions that the agent can perform
- Agent's percept sequence to date
- A rational agent, for each possible percept sequence, selects an action that is expected to maximize its performance measure.
Environments
- Task environments are the problems that rational agents are created to solve.
- PEAS is a widely accepted method of describing task environments, standing for:
- Performance measure
- Environment
- Actuators
- Sensors
Properties of Task Environments
- Fully observable vs partially observable:
- Fully observable: agent's sensors give it a complete state of the environment at each point in time
- Partially observable: environment might be partially observable due to noisy and inaccurate sensors or missing state
- Single-agent vs multiagent:
- Single-agent: one agent is needed to complete the task
- Multiagent: multiple agents are needed to complete the task
- Deterministic vs nondeterministic:
- Deterministic: next state of the environment is completely determined by the current state and the action executed by the agent(s)
- Nondeterministic: next state of the environment is not completely determined
- Episodic vs sequential:
- Episodic: agent's experience is divided into atomic episodes, with a percept and a single action in each episode
- Sequential: current decisions could affect all future decisions
- Static vs dynamic:
- Static: environment does not change while an agent is deciding
- Dynamic: environment can change while an agent is deciding
- Discrete vs continuous:
- Discrete: distinction applies to the state of the environment, time, and percepts and actions of the agent
- Continuous: distinction does not apply
Structure of Agents
- Agent programs run on computing devices with physical sensors and actuators, referred to as agent architecture.
- Categories of agents based on their agent programs:
- Table driven agents
- Simple reflex agents
- Model-based agents
- Goal-based agents
- Utility-based agents
- Table driven agents:
- In this type of agent, the engineer must construct a table that contains the appropriate action for every possible sequence
- These agents are impractical due to storage limitations, time, and learning
- Simple reflex agents:
- Select actions based on the current percept, ignoring the rest of the percept history
- Limited intelligence, prone to infinite loops
- Model-based agents:
- Maintain some sort of internal state that depends on the percept history
- Information on how the world changes over time and how the state of the world is reflected in the agent's percepts is needed
Goal-Based Agents
- Information about 'desirable' situations (goal) is provided
- Agent combines this goal with knowledge on how the world works (transition model) and how the state of the world is reflected in the agent's percepts (sensor model) to choose actions to achieve the goal
Utility-Based Agents
- Goals are typically not enough to provide the excellent behaviour desired in most environments
- Utility: goals should mean the most possible best desired scenario
- An agent's utility function is essentially an internalization of the performance measure
Learning Agents
- Any type of agent can be built as a learning agent
- Learning allows the agent to operate in initially unknown environments and to become more competent than its initial knowledge alone might allow
- A learning agent can be divided into four conceptual components:
- Learning element
- Critic
- Performance element
- Problem generator
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Description
This lecture covers the basics of intelligent agents, including their definition, rationality, and structure. It also explains how agents perceive their environment through sensors and act upon it through actuators.
