artificial intelligence agents and its environment

Characteristics of intelligent agents
1. Learning /Reasoning
2. Reactivity
3. Goal-Orientedness
4. Situatedness
5. Autonomy
6. Adaptivity
7. Sociability

1.Situatedness
Definition:
The agent exists in a specific environment where it perceives inputs
through sensors and performs actions that can change the environment.
Examples:
Physical World: A robot navigating a room, using sensors to detect
obstacles and adjusting its movement.
Internet: A web crawler that receives input in the form of website content
and modifies its output (e.g., indexing).
Significance:
This property ensures the agent is context-aware and can make decisions
based on its surroundings.

2. Autonomy
Definition:
The agent operates independently, with control over its actions and
internal processes, without constant human or external intervention.
Examples:
Autonomous Vehicles: Deciding routes and avoiding obstacles without human
input.
Smart Assistants: Setting reminders or managing smart devices without direct
supervision.
Significance:
Autonomy is essential for scalability and reducing the need for manual
intervention in complex systems.

3. Adaptivity
• Definition:
The ability of an agent to adapt to its environment, learn from interactions, and
act proactively toward achieving its goals.
• Flexibility: React to unexpected changes (e.g., rerouting during traffic).
• Proactiveness: Take initiative based on goals (e.g., suggesting tasks based on habits).
• Learning: Improve from experience and environment interactions.
• Examples:
• Machine Learning Models: An email spam filter learns to improve based on user feedback.
• Thermostats: Learning preferred room temperatures over time.
• Significance:
Adaptivity ensures the agent remains relevant and effective in dynamic or
unpredictable environments.

4.Sociability
• Definition:
The agent interacts seamlessly with humans or other agents, sharing
information and collaborating to achieve goals.
• Examples:
• Collaborative Robots (Cobots): Working alongside humans in manufacturing.
• Chatbots: Engaging in human-like conversations and assisting customers.
• Multi-Agent Systems: Agents working together to solve a problem, such as traffic
coordination in smart cities.
• Significance:
Sociability enables intelligent agents to integrate into larger systems and
environments, enhancing teamwork and communication.

Future of Artificial Intelligence
• 1. Transportation
• 2 .Manufacturing
• 3. Healthcare
• 4. Education
• 5. Media
• 6.Customer Service

1. Transportation
• Current Status: Autonomous cars are already in development by
companies like Tesla, Waymo, and others.
• Future Potential:
• Fully autonomous vehicles will safely ferry passengers without human
intervention.
• AI will optimize traffic flow, reduce accidents, and improve logistics with
autonomous trucks and delivery drones.
• Example: AI-based systems like Tesla’s Autopilot and Google's Waymo
are paving the way for widespread adoption.

2. Manufacturing
• Current Use:
• AI-powered robots assist in tasks such as assembly, stacking, and material
handling.
• Predictive maintenance systems monitor machinery health and predict
failures to minimize downtime.
• Smart factories with adaptive systems that optimize production in real-time.
• Collaborative robots (cobots) that work seamlessly alongside human workers.
• Example: BMW and FANUC use AI to streamline production processes
and enhance efficiency.

3. Healthcare
• Current Use:
• AI models diagnose diseases such as cancer and detect abnormalities in
medical imaging.
• Virtual nursing assistants provide 24/7 monitoring for patients.
• Drug discovery and personalized medicine powered by big data and AI
algorithms.
• AI-based surgery with robotic precision and reduced recovery times.
• Example: IBM Watson and Google's DeepMind are making
breakthroughs in medical diagnostics and research.

4.Education
• Current Use:
• Digital textbooks and online platforms powered by AI personalize learning
experiences.
• Virtual tutors assist teachers in delivering content.
• AI systems that adapt to student needs by analyzing their emotional states,
engagement levels, and progress.
• Accessible education for remote or underserved areas through AI-powered
tools.
• Example: Duolingo uses AI to personalize language-learning
experiences for millions of users.

5. Media
• Current Use:
• AI generates content for journalism, such as financial reports or sports summaries.
• Natural Language Processing (NLP) tools streamline article writing and fact-
checking.
• AI will create multimedia content tailored to audience preferences.
• Enhanced fact-checking and reduction of misinformation using advanced AI
algorithms.
• Example: Bloomberg’s Cyborg technology analyzes complex data for
financial reporting. (financial journalists in generating news articles quickly
and accurately)

6. Customer Service
• Current Use:
• AI chatbots handle common customer queries, reducing wait times and improving
service efficiency.
• Natural language understanding makes AI assistants more conversational.
• Advanced AI assistants capable of holding natural, nuanced conversations.
• Integration of AI with customer data to provide personalized and proactive support.
Example: Google Duplex can place calls and make appointments while
mimicking human-like speech and understanding context.
•Booking appointments (e.g., hair salons, restaurants).
•Providing business information (e.g., operating hours).

PEAS
In Artificial Intelligence (AI), PEAS stands for Performance measure, Environment,
Actuators, and Sensors. It is a framework used to specify the structure of an
intelligent agent. Understanding PEAS helps define how an agent interacts with its
environment and how its success is measured.

Structure of Agents in Al
Agent = Architecture + Agent Program
The job of Al is to design an agent program that implements the
agent function (that is mapping from percepts to actions).
Percept sequence Action
[A, Clean]
[A, Dirty]
[B, Clean]
[B, Dirty]
Right
Suck
Left
Suck

• Agent Program run on some sort of computing device with physical
sensors and actuators—we call this the architecture.
• The architecture might be just an ordinary PC, or it might be a
robotic car with several onboard computers, cameras, and other
sensors.

Types of Agent environment
• Fully Observable: All relevant information is available, e.g., chess board with
visible pieces.
• Partially Observable: Some information is hidden, e.g., poker with hidden cards.
• Deterministic: Actions have predictable outcomes, e.g., calculator operations.
• Stochastic: Outcomes involve randomness, e.g., weather forecasting.
• Static: The environment doesn’t change while the agent acts, e.g., solving a
crossword.
• Dynamic: The environment changes over time, e.g., traffic during driving.
• Discrete: Limited actions and states, e.g., turn-based board games like Monopoly.
• Continuous: Infinite states or actions, e.g., driving a car in real time.

TYPES OF AGENT- an agent is a system
where it receives information from an environment through sensors and acts upon the environment
through actuators.

• Simple Reflex Agents: A motion-sensor light turns on when it detects
movement.
• Model-Based Reflex Agents: A robotic vacuum cleans based on a map
of the room to avoid obstacles.
• Goal-Based Agents: A GPS navigation system provides directions to
reach a specific destination.
• Utility-Based Agents: A self-driving car chooses the fastest route
considering traffic and fuel efficiency.
• Learning Agents: A streaming app recommends shows based on your
viewing history.

artificial intelligence agents and its environment

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