AutonomousAgents

Question 1

What is the literal meaning of “agent” from Latin?

Answer 1

Those who act

Question 2

What is the fundamental role of an agent?

Answer 2

Someone who acts on behalf of others with power derived from delegation

Question 3

Name four real-world examples of agents

Answer 3

Secret Agents; Travel Agents; Sports/Showbiz Agents; Purchasing Agents

Question 4

What are the four common characteristics of real-world agent jobs?

Answer 4

They engage in specific goal tasks; Are delegated by someone; Have power and knowledge to act; Can decide actions based on conditions

Question 5

What defines a software agent’s goal?

Answer 5

Achieving or maintaining a specific situation

Question 6

Name two examples of filtering software agents

Answer 6

Antivirus and anti-spam agents

Question 7

What are the four key characteristics of software agents?

Answer 7

Goal-oriented; Autonomous; Situated; Social

Question 8

How does a software agent differ from a typical process in goal handling?

Answer 8

A software agent is designed to achieve goals and can change behavior while a process simply executes predefined algorithmic code

Question 9

What makes modern processes similar to agents?

Answer 9

They adapt to serve best; Capable of unsolicited execution; Adaptive; Situated; Social

Question 10

What does it mean for an agent to be “situated”?

Answer 10

It executes in a specific environment and can sense and affect that environment

Question 11

What does “social” mean in the context of software agents?

Answer 11

Interacts with other agents in a multiagent system

Question 12

What is meant by “goal-orientedness” in agents?

Answer 12

Agents don’t execute algorithms but aim to achieve a specific state of affairs

Question 13

How does agent autonomy differ from traditional software control?

Answer 13

Agents use distributed decision-making with sub-goals rather than centralized control

Question 14

What aspects define agent sociality?

Answer 14

Exchange of knowledge; Delegation of tasks; Assignment of roles

Question 15

What are three key differences between processes and agents?

Answer 15

Function-oriented vs Goal-oriented; Centralized vs Decentralized decision-making; Simple vs Sophisticated interactions

Question 16

What makes self-driving cars exemplary autonomous agents?

Answer 16

They have goals navigation goals; Have knowledge of rules; Can sense conditions; Act on controls; Must minimize danger; Decide actions dynamically

Question 17

What are “beliefs” in agent knowledge?

Answer 17

Knowledge about the current state of affairs

Question 18

What are “desires” in agent knowledge?

Answer 18

Knowledge about the desired state of affairs

Question 19

What are “effects” in agent knowledge?

Answer 19

Knowledge of how actions can change the current state of affairs

Question 20

What is theoretical reasoning in agents?

Answer 20

A process which affects beliefs to increase knowledge about what’s happening

Question 21

What is practical reasoning in agents?

Answer 21

Weighing conflicting considerations based on desires and beliefs to choose actions

Question 22

What are the two components of practical reasoning?

Answer 22

Deliberation (deciding to act) and Means-End Reasoning (deciding how to act)

Question 23

What is the main challenge of using deep learning in agents regarding XAI?

Answer 23

Deep learning makes agents “black boxes” making it difficult to explain their decisions

Question 24

What is the key contrast between deep learning and traditional agents?

Answer 24

Deep Learning: Black-box not explainable not symbolic; Agents: White-box explainable symbolic

Question 25

What are the six main types of agent architectures?

Answer 25

Reactive; Reactive with State; Proactive goal-oriented; Proactive utility-oriented; Hybrid proactive; Reinforcement Learning

Question 26

What defines a reactive agent?

Answer 26

Perceives events and acts based on simple rules with implicit goals

Question 27

What are the advantages and disadvantages of reactive agents?

Answer 27

Advantages: Simple and efficient; Disadvantages: Not flexible and not truly autonomous

Question 28

Give three examples of reactive agents

Answer 28

Temperature agent; Simple mail agent; Roomba robot

Question 29

How does a reactive agent with state differ from a simple reactive agent?

Answer 29

Maintains internal knowledge and can make decisions based on both current perception and past state

Question 30

What are two examples of reactive agents with state?

Answer 30

Mail agent that tracks user markings; Roomba robot that maps environment

Question 31

What defines a proactive goal-oriented agent?

Answer 31

Has explicit knowledge of goals and selects actions to approach the goal

Question 32

What are the two types of planning in goal-oriented agents?

Answer 32

Offline planning (predefined plans) and Online planning (using planning algorithm)

Question 33

What is a causal model in agent planning?

Answer 33

Specifies the effect of actions on the environment probabilistically

Question 34

What are the two types of planning algorithms?

Answer 34

Greedy planning (single action at a time) and Multistep planning (sequences of actions)

Question 35

What defines a utility-oriented agent?

Answer 35

Aims to maximize a specific current utility or preserve quality of specific conditions

Question 36

What is the key difference between goal-oriented and utility-oriented agents?

Answer 36

Goal-oriented evaluate current vs desired state; Utility-oriented evaluate a utility function against desired value

Question 37

What is a hybrid architecture in agents?

Answer 37

Mixes utility and goals requiring trade-offs between the two

Question 38

What is Newell's Principle of Rationality?

Answer 38

The rational choice is the one that given current knowledge is most suitable to accomplish/approach the goal

Question 39

What are the two main limitations of the rationality principle?

Answer 39

Knowledge can be partial/incorrect; World can dynamically change as agent acts

Question 40

What defines reinforcement learning in agents?

Answer 40

Self-supervised learning where agent learns how to act through rewards

Question 41

What are the key components of reinforcement learning?

Answer 41

Set of actions; Policy for selecting actions; Observation of environment; Reward measurement

Question 42

What is model-based reinforcement learning?

Answer 42

Focuses on learning explicit representation of relationship between environment state and agent actions

Question 43

What is the main advantage of model-based RL?

Answer 43

Builds a reusable inspectable and explainable model of the world

Question 44

What is model-free reinforcement learning?

Answer 44

Doesn't construct explicit world model relies on exploration to determine rewarding actions

Question 45

What is Q-Learning?

Answer 45

Traditional approach to model-free RL that builds a Q-Table matrix associating values to actions in given states

Question 46

What is Deep Q-Learning?

Answer 46

Embeds the policy (Q-table) into a Deep Neural Network for handling large state dimensions

Question 47

What are the advantages and disadvantages of Deep Q-Learning?

Answer 47

Advantages: Learns complex behaviors handles large spaces; Disadvantages: Lacks explainability not reusable costly learning

Question 48

What is SOTA in agent systems?

Answer 48

State of Affairs representing current desired condition within environment

Question 49

What defines a goal in SOTA phase space?

Answer 49

Reaching a particular area in the SOTA phase space

Question 50

What are the two conditions that characterize goals?

Answer 50

Precondition (when goal should activate) and Postcondition (what achievement implies)

Question 51

What are utilities in SOTA phase space?

Answer 51

Constraints on how goals can be reached within the system

Question 52

What is the BDI model?

Answer 52

Belief-Desire-Intention model for operational way to think about goal-oriented agents

Question 53

What are the three key components of BDI?

Answer 53

Beliefs (knowledge about world); Desires (goals); Intentions (currently activated goals)

Question 54

What is JADE?

Answer 54

A goal-oriented agent system where agents have state and behaviors for sub-tasks

Question 55

What is Jason?

Answer 55

A declarative language for BDI agent-oriented programming using logic-programming approaches