week 2 Flashcards
chp 2
INTRODUCTION
TO INTELLIGENT
AGENT (definition)
An intelligent agent is an autonomous entity
that perceives its environment through
sensors and acts upon that environment
using actuators to achieve specific goals.
history
1950: Turing Test
1956: First Dartmouth College Conference on AI
1970-1980: Expert systems
1974-1980: First AI winter
1980: Natural language processors
1987-1993: Second AI winter
1990: Intelligent Agents
2011: Virtual assistants
2016: Sophia
2018: BERT by Google
2020: Autonomous AI
2022: GATO by Deep Mind
2022: vehicleDRX by Algotive
CHARACTERISTICS
OF AN
INTELLIGENT
AGENT
Autonomy
Social Ability
Reactivity
Proactiveness
Learning and Adaptation
AUTONOMY (definition, importance, examples)
Definition:
- The ability of an
agent to operate
without the direct
intervention of
humans or other
systems.
Importance:
- Empower systems
to make
independent
decisions. - Essential for real-
time applications
where human
intervention can be
slow or impractical.
Examples:
- Autonomous drones
navigating
obstacles. - Self-driving cars
making lane-change
decisions.
SOCIAL ABILITY (Definition, Importance, Examples)
Definition:
- The capability of an
agent to interact
with other agents,
systems, or humans
effectively.
Importance:
- Allows agents to
gather, share, and
act upon collective
information. - Crucial for systems
where collaboration
enhances efficiency.
Examples:
- Chatbots engaging
in human-like
conversations. - Multi-agent systems
collaborating to
solve complex
problems.
REACTIVITY (Definition, Importance, examples)
Definition:
The ability of an agent to
perceive and respond to
its environment in real-
time.
Importance:
Enables agents to adapt
to changing conditions.
Vital for safety-critical
applications.
Examples:
Industrial robots adjusting
to unexpected obstacles.
Voice assistants reacting
to vocal commands.
Proactiveness (definition,importance, examples)
Definition:
- The capability of an
agent to take
initiative based on
predictive modeling
and anticipation of
future events.
Importance:
- Enables agents to be
forward-looking,
planning actions
ahead of time. - Provides
competitive
advantage in
dynamic
environments.
Examples:
- Smart thermostats
predicting user behavior to pre-adjust temperatures. - Trading bots
anticipating market
shifts and making
pre-emptive trades.
LEARNING AND ADAPTATION (definition, importance, examples)
Definition:
- The ability of an
agent to improve
its performance
over time by
learning from its
experiences and
adapting its
actions
accordingly.
Importance:
- Ensures
continuous
improvement and
refinement. - Allows agents to
remain relevant
and efficient in
evolving
scenarios.
Examples:
- Personalized
content
recommendations
based on user
history. - Adaptive AI in
gaming adjusting
to players’ skill
levels.
CRITERIA TO DESIGN
AN AGENT
PEAS(Performance, Environment, Actuators, Sensors)
example: designing an automated taxi driver
Performance measure:
safe, fast, legal, comfortable trip, maximize profits
Environment types:
-fully observable (vs partially observable)
-deterministic (vs stochastic)
-episodic (vs sequential)
-static (vs dynamic)
-discrete (vs continuous)
-single agent(vs multi-agent)
Fully Observable Environment:(give definition and example)
Partially Observable Environment:(give definition and example)
Fully Observable Environment:
* Definition: Agents can access all the states and details of the
environment at any point in time.
* Example: A game of Chess where all pieces and their positions are
visible to both players.
Partially Observable Environment:
* Definition: Agents have limited access to the states or details of
the environment. Some information might be hidden or unknown.
* Example: A card game like Poker where players can’t see each
other’s hands.
Deterministic Environment:
(give definition and example)
Stochastic Environment:
(give definition and example)
Deterministic Environment:
* Definition: Outcomes of actions are predetermined and certain.
* Icon/Image: Dominoes, symbolizing predictability.
* Example: Chess (Given a state and a move, the resulting state is always the
same).
Stochastic Environment:
* Definition: Outcomes of actions are probabilistic and can vary.
* Icon/Image: A branching or splintering arrow, symbolizing multiple possible
outcomes.
* Example: Stock market predictions (Actions or decisions can lead to various
outcomes due to many unpredictable factors).
Static Environment:
(give definition and example)
Dynamic Environment:
(give definition and example)
Static Environment:
* Definition: The environment remains unchanged until the
agent performs an action.
* Example: A puzzle game like Sudoku. The game board
remains the same until a player makes a move.
Dynamic Environment:
* Definition: The environment can change while the agent is
deliberating, either due to external factors or other agents.
* Example: Stock market trading. Prices of stocks can
fluctuate based on various factors even if a trader hasn’t
made any decisions.
Discrete Environment: (give definition and example)
Continuous Environment:
(give definition and example)
Discrete Environment:
* Definition: The environment has a finite number of distinct,
separate states or outcomes.
* Example: A chess game, where an intelligent agent has to choose
from a set number of legal moves at any given point in the game.
Continuous Environment:
* Definition: The environment can take on an infinite number of
states within a given range.
* Example: An autonomous vehicle navigating through traffic.
single-agent environment(give definition and example)
multi-agent environment(give definition and example)
Single-Agent Environment:
* Definition: Only one agent operates and makes decisions. The success of its
actions depends solely on its own decisions and not on the decisions of other
agents.
* Example: A vacuum cleaner robot operating in a room. Its sole task is to
clean, and there aren’t any other agents it needs to interact or negotiate with.
Multi-Agent Environment:
* Definition: Multiple agents operate simultaneously, and the success of their
actions may depend on the actions of other agents. Agents might collaborate
or compete with each other.
* Example: Multiple self-driving cars navigating a busy intersection. Each car
must not only navigate based on traffic rules but also predict and react to the
actions of other cars.
AGENT FUNCTIONS
& PROGRAM
- An agent is completely
specified by the agent
function mapping
percept sequences to
actions. - Agent = Architecture +
Program
Environment types: (give examples)
Task
environment, Observable, Deterministic, Episodic, Static, Discrete, Agents
Crossword
puzzle: Fully, Deterministic, Sequential, Static, Discrete, Single
Chess: Fully, Strategic, Sequential, Static, Discrete, Multi
Taxi driving: Partially, Stochastic, Sequential, Dynamic, Continuous, Multi
Medical diagnosis: Partially, Stochastic, Sequential, Dynamic, Continuous, Single
Agent types (Types of intelligent agents)
Simple reflex agents, model-based agents, goal-based agents, utility agents, learning agents
how u draw and design the agents (Simple reflex agents, model-based agents, goal-based agents, utility agents, learning agents)
draw on paper(slide 25 until slide 34)
APPLICATIONS OF INTELLIGENT AGENTS
- Personal assistant (e.g. Alexa,
Siri, Google) - Autonomous vehicles
- Recommender systems
*Health Monitoring
FUTURE OF
INTELLIGENT AGENTS
- Integration with IoT devices
- Increasing human-like interaction
capabilities - Ethical considerations in AI agents
- Potential challenges: safety,
security, trustworthiness
conclusion:
Nature of Intelligent Agents,
Architectural Diversity, Applications in Real-world,
Future Landscape,
The Imperative to Understand
1.Nature of Intelligent Agents: At their essence, intelligent agents are autonomous entities that
perceive their environment, reason, and make decisions to achieve specific goals. Their
applications range from simple automated systems to sophisticated AI-driven tools.
2.Architectural Diversity: We delved into various architectures underpinning different type of
agents, from simple reflex agent up to learning agents. Different features that being added in
the architecture to serve the purpose and improving the performance of an agent.
3.Applications in Real-world: The ubiquity of intelligent agents in our daily lives, from virtual
assistants to recommendation systems, highlights their growing significance in modern
technology.
4.Future Landscape: With deeper integration into IoT devices and the pursuit of human-like
interaction capabilities, the future of intelligent agents is laden with opportunities. However,
this evolution is not without challenges. Ensuring ethical alignment, safety, and trustworthiness
remain at the forefront of our concerns.
5.The Imperative to Understand: As the lines between our digital and physical worlds blur,
understanding the mechanics, potentials, and pitfalls of intelligent agents becomes crucial. Not
only do they hold the promise to redefine industries and elevate user experiences, but they
also pose philosophical and ethical questions about the role of machines in our lives.
QUESTION 1
For each of the following agents, develop the PEAS description of the
task environment and determine their environment types:
a. Robot soccer-player
b. ChatGPT
c. Roomba (Automated Vacuum Cleaner)
d. Wearable Fitness Tracker
e. Drone (For package delivery or surveillance)
a. Robot Soccer-Player
Score goals, assist teammates, avoid penalties, maintain ball possession. Soccer field, dynamic, competitive with other players, changing positions of the ball and teammates. Motors for movement, servos for kicking and dribbling, communication systems for coordination. Cameras for vision, gyroscopes for orientation, GPS for positioning, and touch sensors for ball interaction. Dynamic, partially observable, and competitive.
b.ChatGPT Provide coherent and contextually relevant responses, maintain user engagement, and answer queries accurately. Text-based conversational platform, user queries can vary widely. Text generation (output responses).
Natural language processing (input from users), context tracking. Static (once a query is received, the environment doesn’t change), fully observable.
c. Roomba (Automated Vacuum Cleaner) Clean floors efficiently, avoid obstacles, return to charging station when low on battery. Indoor home environment, varying layouts, and obstacles like furniture. Motors for movement, brushes for cleaning, suction mechanisms. Bump sensors (for obstacles), cliff sensors (to avoid stairs), dirt detection sensors. Dynamic, partially observable.
d. Wearable Fitness Tracker Accurately track fitness metrics (steps, heart rate), provide feedback, and sync data with apps. Wearer’s body and surroundings, can vary in activity levels and conditions (indoor/outdoor). Vibration motor for alerts, display for feedback. Accelerometer, heart rate monitor, GPS (in some models). Semi-dynamic, partially observable.
e. Drone (For package delivery or surveillance) Deliver packages to designated locations, avoid obstacles, maintain stability, capture images (for surveillance). Airspace with varying weather conditions, geographical features, and urban landscapes. Propellers for movement, gimbals for camera stabilization, payload release mechanisms. GPS for navigation, cameras for visual input, ultrasonic sensors for distance measurement. Dynamic, partially observable.
QUESTION 2
An e-commerce recommendation agent suggests
products based on user browsing history.
What could be the potential ethical considerations to
be kept in mind while designing such an agent?
When designing an e-commerce recommendation agent that uses user browsing history, several ethical considerations should be taken into account which is privacy. It is ensure that users browsing histories are collected and used in a manner that respects their privacy. Obtain informed consent and allow users to control their data. On the other hand, data security. It is implement robust security measures to protect user data from breaches and unauthorized access. Beside that, bias and fairness. We must be aware of potential biases in the recommendation algorithms that could lead to unfair treatment of certain products or demographics. Strive for inclusivity and fairness in product recommendations. By addressing these considerations, designers can create a recommendation agent that is not only effective but also ethical and respectful of users’ rights and well-being.
QUESTION 3
Compare and contrast deterministic and stochastic
environments in the context of intelligent agents.
Provide examples.
Environment, Deterministic, Stochastic:
Definition, Outcomes of actions are predetermined and certain., Outcomes of actions are probabilistic and can vary.
Icon/Image, Dominoes, symbolizing predictability., A branching or splintering arrow, symbolizing multiple possible outcomes.
Example, Chess, Stock market predictions
QUESTION 4
With the growing concerns over AI ethics, how do you
envision the design of future intelligent agents to
ensure their ethical behavior?
Ensuring the ethical behavior of future intelligent agents involves several key considerations is Transparency which is Intelligent agents should operate in a way that’s understandable to users. This means clear explanations of how decisions are made and the data used, helping to build trust. Beside that, fairness which is algorithms must be designed to avoid bias and discrimination. This requires diverse training data and continuous monitoring to identify and rectify biases that may arise.
QUESTION 5
How would you incorporate fault tolerance in an
intelligent agent operating in a highly dynamic
environment?
Incorporating fault tolerance into an intelligent agent operating in a highly dynamic environment involves several strategies that is redundancy which is implement multiple systems or components that can take over if one fails. This could include backup algorithms or alternative data sources to ensure continuity. Beside that, Error Detection and Recovery which is Build mechanisms for the agent to recognize when something goes wrong. This includes monitoring system health and performance metrics to identify anomalies quickly. Once a fault is detected, the agent should have predefined recovery procedures to restore normal operation. By integrating these strategies, intelligent agents can better handle unexpected changes and failures in dynamic environments, ensuring more reliable and effective performance.
Question 6
A robot is deployed in a warehouse to assist with moving packages between different storage locations and
loading docks. It has two main tasks:
1. Pick up and place packages in designated storage locations.
2. Load packages onto delivery trucks when the trucks arrive.
The warehouse has varying package priorities based on delivery schedules and package types (fragile, high-
value).
Task:
1. Describe how the robot would operate using the following types of agents:
a) Simple Reflex Agent: How would the robot make decisions about moving and loading packages based on
immediate package availability and truck arrivals?
b) Model-based Reflex Agent: How would the robot utilize an internal model to keep track of package locations and
truck schedules to optimize its tasks?
c) Goal-based Agent: How would the robot prioritize goals such as “ensure all trucks are loaded on time” and “place
high-priority packages in accessible locations”?
d) Utility-based Agent: How would the robot decide whether to move a package or load a truck by maximizing utility,
considering factors like delivery deadlines and the fragility of packages?
Question: Which type of agent would handle the dynamic nature of truck arrivals and package priorities most
effectively, and why?
Let’s explore how the robot would operate using different types of agents in the warehouse scenario is Simple Reflex Agent which is A simple reflex agent would react to immediate stimuli without any internal state or memory. It would make decisions based purely on current conditions, such as Picking Packages. If a package is available and within reach, the robot would pick it up. If it senses a truck approaching, it might immediately prioritize moving closer to the loading dock to prepare for loading. On the other hand, Loading Trucks which is Upon detecting that a truck has arrived, the robot would load any packages that are currently at the dock without considering their priorities or the overall schedule. Beside that is limitations which is this agent would be limited in its effectiveness, as it wouldn’t be able to account for package priorities or upcoming truck schedules, leading to potential delays and inefficiencies.
Model-based Reflex Agent is a model-based reflex agent incorporates a simple internal model of the environment to keep track of the state. Package Tracking which is the robot could maintain an internal map of package locations and their priorities. If a high-priority package is in a designated area, it would be more likely to pick that up first. Truck Schedule Awareness which is by keeping track of the expected arrival times of trucks, the robot can better plan its actions. If a truck is scheduled to arrive soon, it might prioritize loading packages that are ready. Advantages is this agent can operate more effectively than a simple reflex agent because it can make decisions based on a combination of immediate conditions and its internal model, allowing for better planning.
A goal-based agent operates with specific objectives in mind, allowing it to prioritize tasks. Prioritization of Tasks which is the robot would prioritize loading trucks based on their schedules and the priority of packages. For example, it would ensure that high-value and fragile packages are loaded first and in a way that maximizes their protection. Dynamic Adjustment. If a truck arrives ahead of schedule, the robot can adjust its actions to ensure timely loading of all packages. Advantages is this agent is more flexible and can adapt to changing priorities and goals, making it effective in a dynamic environment.
A utility-based agent evaluates different actions based on their utility, maximizing overall efficiency. Utility Calculations is the robot would assign utility values to tasks based on factors such as delivery deadlines, package fragility, and loading priorities. For instance, a high-value fragile package might have a higher utility than a low-value, robust package. Decision Making is When faced with multiple tasks, the robot would choose the one that maximizes total utility, potentially loading a truck that contains urgent packages over moving less critical ones. Advantages is This agent is highly effective because it considers multiple factors and balances trade-offs, leading to optimal decision-making.
The Most Effective Agent is utility-based agent would handle the dynamic nature of truck arrivals and package priorities most effectively. Its ability to evaluate and prioritize tasks based on a variety of factors allows it to adapt to changing conditions, ensuring that the most critical packages are managed appropriately while also maximizing overall efficiency. This level of adaptability and optimization is crucial in a warehouse environment with varying demands and priorities.
