Because this concept of developing a smart set of design principles for building successful agents, systems that can reasonably be called intelligent, is Central to artificial intelligence we need to know its thinking and action approach. This PPT covers this topic in detail.
Go and take a look and share your suggestions with me.
This lecture discusses intelligent agents and their key components. It defines agents as things that can perceive their environment and take actions. An agent's behavior is defined by its agent function, which maps percept sequences to actions. The lecture then covers the nature of environments agents operate in, describing their properties like observability, determinism, and more. It also outlines the basic structures of agents, including reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. Learning-based agents are introduced as a way to allow agents to improve through experience.
This document discusses agents and environments. It defines an agent as anything that can perceive its environment through sensors and act upon the environment through actuators. A simple example is given of a vacuum cleaner agent that perceives the location and dirt level and can move or suck. The document then discusses key concepts related to agents including percepts, agent functions, rational agents, and PEAS descriptions. It provides examples of different types of agents and their environments. Finally, it outlines different properties of task environments such as observable, deterministic, episodic, and single vs multi-agent.
The document discusses different types of intelligent agents and their characteristics. It defines an agent as anything that can perceive its environment and act upon it. Example agent types include human agents, robotic agents, and software agents. The document also discusses windshield wiper agents as an example and covers agent terminology such as goals, percepts, sensors, effectors, and actions. Later sections discuss rational agents and how they are designed to maximize their performance based on their percept sequences and knowledge. Different types of agents are introduced, including simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. The document also covers properties of task environments and the structure of agents.
An AI assistant summarizes the key points about the Turing Test from the document:
1) The Turing Test proposes that a computer can be considered intelligent if an interrogator cannot distinguish it from a human via conversation.
2) Notable chatbots that have attempted the Turing Test include ELIZA, Parry, and Eugene Goostman. Eugene Goostman convinced 29% of judges it was human.
3) Critics argue that passing the Turing Test does not prove a machine has human-level understanding, as it can mimic responses without true comprehension.
This document discusses intelligent agents and their design. It begins by defining an agent as anything that can perceive its environment and act upon it. It then describes different types of agents including human agents, robotic agents, and software agents. It introduces the concepts of percepts, actions, and agent functions. It also discusses rational agents and the requirements for rational behavior. Finally, it covers different aspects of agent design including performance measures, environments, actuators, sensors (PEAS), environment types, and the four basic types of agents from simple reflex agents to utility-based agents.
AI Agents, Agents in Artificial IntelligenceKirti Verma
The document discusses intelligent agents, defining them as anything that perceives its environment and takes action. It describes the key components of agents including goals, environments, and different classes of agents from simple reflex agents to learning agents. The document also provides examples of applications of intelligent agents such as medical diagnosis, vacuum cleaners, and part-picking robots.
1) Intelligent agents are systems that perceive their environment and act upon it. They can be designed to act or think rationally or humanly.
2) An agent is anything that can perceive its environment through sensors and act upon the environment through effectors. Agents perceive the environment via sensors and act with effectors, mapping percept sequences to actions.
3) Key properties of intelligent agents include autonomy, reactivity, proactiveness, balancing reactive and goal-oriented behavior, and social ability. Agents must be able to operate independently, respond to changes, pursue goals, and interact with other agents.
introduction to inteligent IntelligentAgent.pptdejene3
This document discusses intelligent agents and how they operate in different environments. It defines key concepts like agents, rational agents, and the PEAS framework. An agent is something that perceives its environment and acts upon it. A rational agent strives to maximize its performance measure given its sensors and effectors. The document also categorizes different types of environments based on properties like observability, determinism, episodic vs sequential nature, dynamics, discreteness and the presence of single or multiple agents. Real-world examples are provided for each of these properties and environment types.
This lecture discusses intelligent agents and their key components. It defines agents as things that can perceive their environment and take actions. An agent's behavior is defined by its agent function, which maps percept sequences to actions. The lecture then covers the nature of environments agents operate in, describing their properties like observability, determinism, and more. It also outlines the basic structures of agents, including reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. Learning-based agents are introduced as a way to allow agents to improve through experience.
This document discusses agents and environments. It defines an agent as anything that can perceive its environment through sensors and act upon the environment through actuators. A simple example is given of a vacuum cleaner agent that perceives the location and dirt level and can move or suck. The document then discusses key concepts related to agents including percepts, agent functions, rational agents, and PEAS descriptions. It provides examples of different types of agents and their environments. Finally, it outlines different properties of task environments such as observable, deterministic, episodic, and single vs multi-agent.
The document discusses different types of intelligent agents and their characteristics. It defines an agent as anything that can perceive its environment and act upon it. Example agent types include human agents, robotic agents, and software agents. The document also discusses windshield wiper agents as an example and covers agent terminology such as goals, percepts, sensors, effectors, and actions. Later sections discuss rational agents and how they are designed to maximize their performance based on their percept sequences and knowledge. Different types of agents are introduced, including simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. The document also covers properties of task environments and the structure of agents.
An AI assistant summarizes the key points about the Turing Test from the document:
1) The Turing Test proposes that a computer can be considered intelligent if an interrogator cannot distinguish it from a human via conversation.
2) Notable chatbots that have attempted the Turing Test include ELIZA, Parry, and Eugene Goostman. Eugene Goostman convinced 29% of judges it was human.
3) Critics argue that passing the Turing Test does not prove a machine has human-level understanding, as it can mimic responses without true comprehension.
This document discusses intelligent agents and their design. It begins by defining an agent as anything that can perceive its environment and act upon it. It then describes different types of agents including human agents, robotic agents, and software agents. It introduces the concepts of percepts, actions, and agent functions. It also discusses rational agents and the requirements for rational behavior. Finally, it covers different aspects of agent design including performance measures, environments, actuators, sensors (PEAS), environment types, and the four basic types of agents from simple reflex agents to utility-based agents.
AI Agents, Agents in Artificial IntelligenceKirti Verma
The document discusses intelligent agents, defining them as anything that perceives its environment and takes action. It describes the key components of agents including goals, environments, and different classes of agents from simple reflex agents to learning agents. The document also provides examples of applications of intelligent agents such as medical diagnosis, vacuum cleaners, and part-picking robots.
1) Intelligent agents are systems that perceive their environment and act upon it. They can be designed to act or think rationally or humanly.
2) An agent is anything that can perceive its environment through sensors and act upon the environment through effectors. Agents perceive the environment via sensors and act with effectors, mapping percept sequences to actions.
3) Key properties of intelligent agents include autonomy, reactivity, proactiveness, balancing reactive and goal-oriented behavior, and social ability. Agents must be able to operate independently, respond to changes, pursue goals, and interact with other agents.
introduction to inteligent IntelligentAgent.pptdejene3
This document discusses intelligent agents and how they operate in different environments. It defines key concepts like agents, rational agents, and the PEAS framework. An agent is something that perceives its environment and acts upon it. A rational agent strives to maximize its performance measure given its sensors and effectors. The document also categorizes different types of environments based on properties like observability, determinism, episodic vs sequential nature, dynamics, discreteness and the presence of single or multiple agents. Real-world examples are provided for each of these properties and environment types.
Artificial intelligence agents can be defined as entities that perceive their environment through sensors, and act upon the environment through effectors to achieve goals or perform tasks. The document discusses different types of agents including table-driven agents, reflex agents, agents with memory, goal-based agents, and utility-based agents. It also covers key concepts in agent design like the PEAS framework and properties of environments that agents operate in.
This document discusses different types of intelligent agents. It describes four basic types of agent programs: simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. Simple reflex agents select actions based only on the current percept, while model-based reflex agents maintain an internal model of the world. Goal-based agents use goals to determine desirable situations. Utility-based agents maximize an internal utility function that represents the performance measure. The document also discusses agent functions, percepts, environments, and the PEAS properties of task environments.
Introduction To Artificial IntelligenceNeHal VeRma
Artificial intelligence (AI) aims to create intelligent machines that can perceive and act like humans. Alan Turing first proposed testing machine intelligence with the Turing Test in 1956. An intelligent agent perceives its environment through sensors and acts through effectors. Rational agents are expected to select actions that maximize their performance given perceived inputs and built-in knowledge. Different types of AI agents include simple reflex agents that react to current inputs, model-based reflex agents that track the world state, goal-based agents that consider goals, and utility-based agents that evaluate action outcomes.
Introduction To Artificial IntelligenceNeHal VeRma
Artificial intelligence (AI) aims to create intelligent machines that can perceive and act like humans. Alan Turing first proposed testing machine intelligence with the Turing Test in 1956. An intelligent agent perceives its environment through sensors and acts through effectors. Rational agents are expected to select actions that maximize their performance given perceptual inputs and built-in knowledge. Different types of AI agents include simple reflex agents that react to current inputs, model-based reflex agents that track the world state, goal-based agents that consider goals, and utility-based agents that evaluate action outcomes.
An intelligent agent is anything that can perceive its environment through sensors and act upon that environment through effectors. Intelligent agents include humans, robots, and thermostats. An agent's behavior is determined by its agent function, which maps percept sequences to actions. Rational agents are those that maximize their performance as defined by a performance measure. Agent programs implement agent functions in a way that uses minimal code rather than exhaustive lookup tables. There are different types of agent programs including simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents.
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators
Operates in an environment
Perceive its environment through sensors
Acts upon its environment through actuators/ effectors
Has Goals
The document provides an introduction to intelligent agents. It defines an agent as anything that can perceive its environment and act upon that environment. Agents operate using sensors to perceive the environment and effectors to act upon it. The document outlines different types of agents including simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. It also describes properties of environments including fully/partially observable, single/multi-agent, static/dynamic, episodic/sequential, deterministic/stochastic, and discrete/continuous.
The document discusses different types of intelligent agents in artificial intelligence. It defines an agent as anything that can perceive its environment through sensors and act upon the environment through effectors. It then describes different types of agent programs:
1) Simple reflex agents that take actions based solely on current percepts.
2) Model-based reflex agents that maintain an internal state based on past percepts allowing them to operate in partially observable environments.
3) Goal-based agents that choose actions to achieve goals by considering long sequences of possible actions through planning.
4) Utility-based agents that act not just based on goals but also to maximize utility or success at achieving goals.
5) Learning agents that can
This document discusses intelligent agents and their characteristics. It defines an intelligent agent as an autonomous entity that can perceive its environment through sensors and act upon the environment through effectors to achieve goals. Intelligent agents must be able to perceive, make decisions based on perceptions, take actions as a result of decisions, and take rational actions. The document also describes different types of agents including simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. Finally, it discusses environments that agents can operate in based on characteristics like observability, predictability, event structure, existence of other agents, consistency, certainty, and accessibility.
The document defines different types of agents and environments. It discusses (1) how agents perceive their environment through sensors and act upon it through effectors, (2) the characteristics of rational agents and different environment types, and (3) five classes of intelligent agents from simple reflex agents to learning agents and their applications.
An agent is anything that can perceive its environment and act upon that environment. An intelligent agent must sense, act autonomously, and be rational. There are different types of environments and classes of agents. Intelligent agents are applied as automated online assistants to perceive customer needs and perform individualized customer service.
The document discusses different types of agent programs:
- Simple reflex agents select actions based only on the current percept, ignoring percept history. This is implemented with condition-action rules.
- Model-based reflex agents maintain an internal state to track unobserved aspects of the world based on a model of how the world works. This allows handling partial observability.
- Goal-based agents consider goals that describe desirable situations and choose actions to achieve goals based on the current state and results of actions.
- Utility-based agents assign numeric utilities to states and choose actions that maximize expected utility, allowing comparison of states in achieving multiple goals.
The document provides an introduction to agents and intelligent systems. It defines key concepts such as agents, environments, agent architectures, and rationality. An agent is anything that perceives and acts in an environment. Agent architectures include table-based, reactive, model-based, goal-based, and learning agents. Rational agents act to maximize their performance or utility based on their perceptions, while bounded rational agents are limited by their resources. Environments can be fully or partially observable, deterministic or stochastic, single-agent or multi-agent. The ideal is to build autonomous agents that can learn to achieve goals in dynamic environments.
The document discusses intelligent agents and their components. An agent is anything that perceives its environment through sensors and acts upon the environment through effectors. Agents can be human, robotic, or software based. An agent's behavior is described by its agent function, which maps percept sequences to actions. Practically, an agent's behavior is implemented through an agent program. Different types of agent programs are discussed, including simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. The properties of task environments that agents operate within are also outlined.
This presentation educates you about AI - Agents & Environments, Agent Terminology, Rationality, What is Ideal Rational Agent?, The Structure of Intelligent Agents and Properties of Environment.
For more topics stay tuned with Learnbay.
An agent is anything that perceives its environment through sensors and acts upon the environment through actuators. Intelligent agents can be human, robots with cameras and motors, or thermostats detecting room temperature. An agent's behavior is described by its agent function, which maps percept sequences to actions. Rational agents select actions expected to maximize a performance measure given the agent's knowledge and percept sequence. Learning agents can improve their performance over time by experiencing examples.
This document discusses different types of intelligent agents. It defines an agent as an entity that perceives its environment and acts upon that environment. Rational agents are defined as those that select actions that maximize their performance given the information available. Six main types of agents are described: (1) table-driven agents that use lookup tables; (2) simple reflex agents that act solely based on current percepts; (3) model-based reflex agents that track past states; (4) goal-based agents that consider future actions to achieve goals; (5) utility-based agents that make decisions based on utility theory; and (6) learning agents that improve through experience. The document emphasizes that representing knowledge is important for successful agent design
The document defines different types of intelligent agents and their applications. It describes agents as anything that perceives and acts on its environment. Intelligent agents are classified into five types based on their capabilities: simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. Intelligent agents are applied as automated online assistants that perceive customer needs to perform personalized customer service. Potential future applications include using intelligent agents in smartphones.
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
Artificial intelligence agents can be defined as entities that perceive their environment through sensors, and act upon the environment through effectors to achieve goals or perform tasks. The document discusses different types of agents including table-driven agents, reflex agents, agents with memory, goal-based agents, and utility-based agents. It also covers key concepts in agent design like the PEAS framework and properties of environments that agents operate in.
This document discusses different types of intelligent agents. It describes four basic types of agent programs: simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. Simple reflex agents select actions based only on the current percept, while model-based reflex agents maintain an internal model of the world. Goal-based agents use goals to determine desirable situations. Utility-based agents maximize an internal utility function that represents the performance measure. The document also discusses agent functions, percepts, environments, and the PEAS properties of task environments.
Introduction To Artificial IntelligenceNeHal VeRma
Artificial intelligence (AI) aims to create intelligent machines that can perceive and act like humans. Alan Turing first proposed testing machine intelligence with the Turing Test in 1956. An intelligent agent perceives its environment through sensors and acts through effectors. Rational agents are expected to select actions that maximize their performance given perceived inputs and built-in knowledge. Different types of AI agents include simple reflex agents that react to current inputs, model-based reflex agents that track the world state, goal-based agents that consider goals, and utility-based agents that evaluate action outcomes.
Introduction To Artificial IntelligenceNeHal VeRma
Artificial intelligence (AI) aims to create intelligent machines that can perceive and act like humans. Alan Turing first proposed testing machine intelligence with the Turing Test in 1956. An intelligent agent perceives its environment through sensors and acts through effectors. Rational agents are expected to select actions that maximize their performance given perceptual inputs and built-in knowledge. Different types of AI agents include simple reflex agents that react to current inputs, model-based reflex agents that track the world state, goal-based agents that consider goals, and utility-based agents that evaluate action outcomes.
An intelligent agent is anything that can perceive its environment through sensors and act upon that environment through effectors. Intelligent agents include humans, robots, and thermostats. An agent's behavior is determined by its agent function, which maps percept sequences to actions. Rational agents are those that maximize their performance as defined by a performance measure. Agent programs implement agent functions in a way that uses minimal code rather than exhaustive lookup tables. There are different types of agent programs including simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents.
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators
Operates in an environment
Perceive its environment through sensors
Acts upon its environment through actuators/ effectors
Has Goals
The document provides an introduction to intelligent agents. It defines an agent as anything that can perceive its environment and act upon that environment. Agents operate using sensors to perceive the environment and effectors to act upon it. The document outlines different types of agents including simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. It also describes properties of environments including fully/partially observable, single/multi-agent, static/dynamic, episodic/sequential, deterministic/stochastic, and discrete/continuous.
The document discusses different types of intelligent agents in artificial intelligence. It defines an agent as anything that can perceive its environment through sensors and act upon the environment through effectors. It then describes different types of agent programs:
1) Simple reflex agents that take actions based solely on current percepts.
2) Model-based reflex agents that maintain an internal state based on past percepts allowing them to operate in partially observable environments.
3) Goal-based agents that choose actions to achieve goals by considering long sequences of possible actions through planning.
4) Utility-based agents that act not just based on goals but also to maximize utility or success at achieving goals.
5) Learning agents that can
This document discusses intelligent agents and their characteristics. It defines an intelligent agent as an autonomous entity that can perceive its environment through sensors and act upon the environment through effectors to achieve goals. Intelligent agents must be able to perceive, make decisions based on perceptions, take actions as a result of decisions, and take rational actions. The document also describes different types of agents including simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. Finally, it discusses environments that agents can operate in based on characteristics like observability, predictability, event structure, existence of other agents, consistency, certainty, and accessibility.
The document defines different types of agents and environments. It discusses (1) how agents perceive their environment through sensors and act upon it through effectors, (2) the characteristics of rational agents and different environment types, and (3) five classes of intelligent agents from simple reflex agents to learning agents and their applications.
An agent is anything that can perceive its environment and act upon that environment. An intelligent agent must sense, act autonomously, and be rational. There are different types of environments and classes of agents. Intelligent agents are applied as automated online assistants to perceive customer needs and perform individualized customer service.
The document discusses different types of agent programs:
- Simple reflex agents select actions based only on the current percept, ignoring percept history. This is implemented with condition-action rules.
- Model-based reflex agents maintain an internal state to track unobserved aspects of the world based on a model of how the world works. This allows handling partial observability.
- Goal-based agents consider goals that describe desirable situations and choose actions to achieve goals based on the current state and results of actions.
- Utility-based agents assign numeric utilities to states and choose actions that maximize expected utility, allowing comparison of states in achieving multiple goals.
The document provides an introduction to agents and intelligent systems. It defines key concepts such as agents, environments, agent architectures, and rationality. An agent is anything that perceives and acts in an environment. Agent architectures include table-based, reactive, model-based, goal-based, and learning agents. Rational agents act to maximize their performance or utility based on their perceptions, while bounded rational agents are limited by their resources. Environments can be fully or partially observable, deterministic or stochastic, single-agent or multi-agent. The ideal is to build autonomous agents that can learn to achieve goals in dynamic environments.
The document discusses intelligent agents and their components. An agent is anything that perceives its environment through sensors and acts upon the environment through effectors. Agents can be human, robotic, or software based. An agent's behavior is described by its agent function, which maps percept sequences to actions. Practically, an agent's behavior is implemented through an agent program. Different types of agent programs are discussed, including simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. The properties of task environments that agents operate within are also outlined.
This presentation educates you about AI - Agents & Environments, Agent Terminology, Rationality, What is Ideal Rational Agent?, The Structure of Intelligent Agents and Properties of Environment.
For more topics stay tuned with Learnbay.
An agent is anything that perceives its environment through sensors and acts upon the environment through actuators. Intelligent agents can be human, robots with cameras and motors, or thermostats detecting room temperature. An agent's behavior is described by its agent function, which maps percept sequences to actions. Rational agents select actions expected to maximize a performance measure given the agent's knowledge and percept sequence. Learning agents can improve their performance over time by experiencing examples.
This document discusses different types of intelligent agents. It defines an agent as an entity that perceives its environment and acts upon that environment. Rational agents are defined as those that select actions that maximize their performance given the information available. Six main types of agents are described: (1) table-driven agents that use lookup tables; (2) simple reflex agents that act solely based on current percepts; (3) model-based reflex agents that track past states; (4) goal-based agents that consider future actions to achieve goals; (5) utility-based agents that make decisions based on utility theory; and (6) learning agents that improve through experience. The document emphasizes that representing knowledge is important for successful agent design
The document defines different types of intelligent agents and their applications. It describes agents as anything that perceives and acts on its environment. Intelligent agents are classified into five types based on their capabilities: simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. Intelligent agents are applied as automated online assistants that perceive customer needs to perform personalized customer service. Potential future applications include using intelligent agents in smartphones.
Similar to Intelligent Agents, A discovery on How A Rational Agent Acts (20)
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Open Channel Flow: fluid flow with a free surfaceIndrajeet sahu
Open Channel Flow: This topic focuses on fluid flow with a free surface, such as in rivers, canals, and drainage ditches. Key concepts include the classification of flow types (steady vs. unsteady, uniform vs. non-uniform), hydraulic radius, flow resistance, Manning's equation, critical flow conditions, and energy and momentum principles. It also covers flow measurement techniques, gradually varied flow analysis, and the design of open channels. Understanding these principles is vital for effective water resource management and engineering applications.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Software Engineering and Project Management - Software Testing + Agile Method...Prakhyath Rai
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Mechatronics is a multidisciplinary field that refers to the skill sets needed in the contemporary, advanced automated manufacturing industry. At the intersection of mechanics, electronics, and computing, mechatronics specialists create simpler, smarter systems. Mechatronics is an essential foundation for the expected growth in automation and manufacturing.
Mechatronics deals with robotics, control systems, and electro-mechanical systems.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
2. Why Rational Agent Approach?
Because this concept of
developing a smart set of
design principles for
building successful
agents, systems that can
reasonably be called
intelligent, is Central to
artificial intelligence.
3.
4.
5. ● An agent perceives its
environment through sensors
and then takes action upon that
environment through actuators,
a mechanism that puts
something into automatic action.
● A software agent receives
keystrokes, file contents, And
network packets as input and
Then it acts on the environment
by displaying output on the
screen or sending network
packets.
6. ● The word ‘percept’ means Inputs that
an agent receives through sensors at
any instance of time. For example, a
human agent has sense organs for
perceiving percepts and hands, legs,
vocal tract, and so on as actuators.
Similarly, a robotic agent might have
cameras and infrared range finders
For sensing the environment and
various motors as actuators.
● The percept sequence of an agent
shows the history of everything that an
agent has ever perceived and the
percept sequence helps an agent in
choosing an action at any given time.
7. ● An agent’s function speaks its (or His) behavior. The
function of an agent is a result of the mapping of a percept
sequence to an action, and so a function can be called an
abstract mathematical description. By tabulating all
possible percept sequences and their corresponding
actions, we can externally characterize the agent's
behavior.
● An agent program is an internal characterization of an
agent. This program will be a concrete implementation
running on a physical system.
● For Example, The agent function for our cleaning robot is
to clean a room. It perceives its environment through
sensors, determines the dirtiness level, and takes actions
to clean the room efficiently and an agent program might
be a piece of software that runs on the robot's control
system.
8. ● When an agent performs a randomized action, it is considered very silly but it may
be very intelligent. So, if we find that an agent uses some randomization to choose
its action, then we would have to identify the probability of each action by trying
each action many times.
● Before we close this session we should pay attention to the very notion that an
agent is made to be a tool for analyzing systems. But in all areas of Engineering,
an agent makes a non-trivial decision to display an action that it had perceived in
history for a percept sequence. For example, a hand-held calculator shows
“2+2=4”. This result “4” is the result of a history of percept sequence.
________
10. ● An agent is one that does the right thing. To judge whether the task has been done
rightly or not, we capture the sequence of actions that causes the environment to go
through a sequence of states. If the sequence of states is desirable then this notion of
desirability is the performance measure to evaluate the rationality of an agent.
● We don’t need to evaluate the agent’s state, we need to evaluate environment states. If
we evaluate an agent’s states, then the agent can achieve rationality by deluding itself
of its performance as its perfect performance. So, as a general rule, It is better to design
performance measures according to what one actually wants in the environment rather
than, according to how one thinks the agent should behave.
2.1 Rationality
Rational at any given time depends :
• The performance measure that defines the criterion of success.
• The agent’s prior knowledge of the environment.
• The actions that the agent can perform.
• The agent’s percept sequence to date.
11. ● So, 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.
12. 2.2 Omniscience, Learning, and
Autonomy
● An omniscient agent knows the
actual outcome of its actions and
can act accordingly. But in reality,
Omniscience is impossible.
● Rationality maximizes expected
performance while perfection
maximizes actual performance.
● The point is that we expect an
agent to do what turns out to be
the best action after the fact.But
unless we improve agent’s
performance, it will be impossible
to design an agent to fulfill the
specification.
13. ● Doing actions in order to modify
future percepts, sometimes called
information gathering, is an
important part of rationality.
● Exploration of an environment is
another way for information
gathering.
● Some agents may initially have
prior knowledge, but they must
adapt and modify their
understanding based on
experience. So, a rational agent
shouldn't just gather knowledge
but also learn as much as possible
from what it perceives
14. ● If an agent relies on the prior knowledge of its designer rather than on its own
percepts, we can say that the agent lacks autonomy.
● An autonomous agent learns to compensate for its partial or incorrect prior
knowledge. So, a rational agent should be autonomous.
● After a sufficient experience of its environment, the behavior of a rational agent
can become effectively independent of its prior knowledge. In other words, this
independency will make a rational agent autonomous.
_________
16. ● The nature of environment for
an agent is described by
“Specifying the Task
Environment” and “
Properties of Task
Environments”.
3.1 Specifying task
environment
● To specify the task
environment of an agent, we
need to specify PEAS(
Performance measure,
Environment, Actuators, and
Sensors).
17. ● Here’s an instance of specification of the environment of a self-driving car.
● Performance measures for the above example include getting to the correct
destination; minimizing fuel consumption and wear and tear; minimizing the trip
time or cost; minimizing violations of traffic laws and disturbances to other
drivers; maximizing safety and passenger comfort; and maximizing profits.
18. ● The environment includes a variety of roads, ranging from rural lanes and urban
alleys to 12-lane freeways. The roads contain other traffic, pedestrians, stray
animals, road works, police cars, puddles, and potholes. The car may also need to
interact with potential and actual passenger
● The actuators for an automated taxi include those available to a human driver:
control over the engine through the accelerator, control over steering and braking,
and a display screen or voice synthesizer to communicate to the passengers.
● The basic sensors for the taxi will include one or more controllable video cameras
so that it can see the road, a speedometer to control the vehicle properly,
especially on curves, GPS, global positioning system, so that it doesn’t get lost,
and a keyboard or microphone for the passenger to request a destination.
19. ● Softbot or Software agent: a "softbot" refers to a software agent or robot
designed to operate on the Internet.
- Example: a softbot Web site operator designed to scan Internet news sources
and show interesting items to its users, while selling advertising space to
generate revenue.
- The characteristics attributed to this softbot include the need for natural
language processing abilities, the ability to learn user and advertiser
preferences, and the capacity to dynamically adjust plans in response to
changes, such as the connection status of news sources.
- a softbot in this context is an intelligent software agent tailored for web
operations, dealing with the complexity of the online environment and
interacting with both artificial and human agents.
20. 3.2 Properties of task environment
● Fully observable: an agent’s sensors give it access to the complete state of the
environment at each point in time. An environment might be partially observable
because of noisy and inaccurate sensors or because parts of the state are simply
missing from the sensor data. If the agent has no sensors at all then the environment is
Unobservable.
● Single-agent and multiagent: an agent solving a crossword puzzle by itself is clearly
in a single-agent environment, whereas an agent playing chess is in a two-agent
environment
● Chess is a competitive multi agent environment. In the taxi-driving environment, on the
other hand, avoiding collisions maximizes the performance measure of all agents, so it
is a partially cooperative multiagent environment.
● Deterministic vs. stochastic: If the next state of the environment is completely
determined by the current state and the action executed by the agent, then we say the
environment is deterministic; otherwise, it is stochastic. "Stochastic" refers to a process
or phenomenon that involves a random element or probability.
21. ● We say an environment is uncertain if it is not fully observable or not deterministic.
● A non-deterministic environment is one in which actions are characterized by their
possible outcomes, but no probabilities are attached to them. Nondeterministic
environment descriptions are usually associated with performance measures that
require the agent to succeed for all possible outcomes of its actions.
● In an episodic task environment, the agent’s experience is divided into atomic
episodes. In each episode, the agent receives a percept and then performs a single
action. Crucially, the next episode does not depend on the actions taken in previous
episodes.For example, an agent that has to spot defective parts on an assembly
line bases each decision on the current part, regardless of previous decisions;
moreover, the current decision doesn’t affect whether the next part is defective.
● In sequential environments, on the other hand, the current decision could affect all
future decisions.3 Chess and taxi driving are sequential: in both cases, short-term
actions can have long-term consequences.
22. ● If the environment can change while an agent is deliberating, then we say the
environment is dynamic for that agent; otherwise, it is static. Static environments
are easy to deal with because the agent need not keep looking at the world while it
is deciding on an action, nor need it worry about the passage of time. Crossword
puzzles are static.
● If the environment itself does not change with the passage of time but the agent’s
performance score does, then we say the environment is semi-dynamic. Chess,
when played with a clock, is semi-dynamic.
● The state of knowledge of an agent determines whether it is in a known
environment or an unknown environment. In a known environment, the outcomes
(or outcome probabilities if the environment is stochastic) for all actions are given.
If the environment is unknown, the agent will have to learn how it works in order to
make good decisions.
● The hardest cases are partially observable, multiagent, stochastic, sequential,
dynamic, continuous, and unknown.
24. ● One or more agent(s) are placed in a simulated environment and their
behavior is observed over time and their evolution is done according to a given
performance measure. Such experiments are often carried out not for a single
environment but for many environments drawn from an environment class.
● For example, to evaluate a taxi driver in simulated traffic, we would want to run
many simulations with different traffic, lighting, and weather conditions.
● An environment generator for each environment class that selects particular
environments (with certain likelihoods) in which to run the agent.
_______________________
25. How the component of an agent program work ?
● The component of the environment of an agent can be expressed on an axis of
increasing complexity and increasing expressiveness- Atomic, Factored, and
structure.
● In an atomic representation, each state of the world is indivisible. For example, from
going one end to another end of a country, you can break down the route with the name
of cities you will cross while going from one end to another end.The algorithms
‘searching’, game-playing, Hidden Markov Model, and Markov Decision model work on
atomic representation.
● In factored representation, we need to do consider more than just one atomic city.For
example, We need to consider the amount of gas in tank, our current GPS coordinates,
whether or not the oil warning light is working. A factored representation breaks down
each state in attributes or variable, each of which can have some value.
● The areas of AI based on factored representation include constraint satisfaction
algorithm, propositional logic, planning, Bayesian Network, and machine learning
algorithm.
● Sometimes we need things related to each other, not just variables and values. For
example, while driving a car from one end to another their might a situation when at a
turn a cow stops car from moving ahead. What can we do in that situation?
26. ● Structured representation underlie RDB, first-order logic, first order probability model,
knowledge based reasoning, natural language understanding.
● In fact, everything that we human concerns is expressed in objects and their relationship
● As it can be seen, as we move forward from atomic to structured representation, the
expressiveness of components of an environment increases.
● To gain the benefits from expressive representations while avoiding their drawback, an
intelligent system for real world may need to operate all points of the axis simultaneously.
_______________________
28. ● The job of AI is to design an agent program that implements the agent function—
the mapping from percepts to actions. We assume this program will run on some
sort of computing device with physical sensors and actuators—we call this the
architecture:
Architecture Agent
Program
● The difference between the agent program, which takes the current percept as
input, and the agent function, which takes the entire percept history
● Four basic kinds of agent programs:
○ Simple reflex agents
○ Model-based reflex agents
○ Goal-based agents
○ Utility-based agents.
29. ● The simplest kind of agent is the simple reflex agent. This agent selects actions
on the basis of the current percept, ignoring the rest of percept history. For
example, if a self-driving car locates an object in front of it it will decide an action
based on that current situation. The self-driving car decides to initiate breaking
when it finds an object in front. This triggers some established connection in the
agent program to the action “initiate breaking”. we call such a connection or
condition-action rule
30. ● Simple reflex agents do have admirable properties but they turn out to be of limited
intelligence. When they observe the environment partially, they get stuck in an
infinite loop of actions, then the agent can randomize its actions to escape from an
infinite loop.
● The best way to handle partial observability for the agent is to keep track of the part
of the world it cannot see now. That is, the agent should maintain some sort of
internal state that depends on the percept history and thereby reflects at least
some of the unobserved aspects of the current state.
● When the information about the world that the agent cannot see or the information
about how the agent’s actions affect the world or the information about the world
evolves independently of the agent is implemented in a simple boolean circuit or
incomplete scientific theory is called a model of the world. An agent that uses such
a model is called a model-based agent
31.
32. ● Knowing something about the current state of the environment is not always
enough to decide what to do. For example, at a road junction, the taxi can turn left,
turn right, or go straight on. The correct decision depends on where the taxi is
trying to get to. In other words, it can be said now the agent needs some sort of
goal information. Once the agent gets goal information, it combines this information
with the model to choose actions that achieve the goal. Now, the agent is a goal-
based agent.
33. ● Goals alone are not enough to generate high-quality behavior in most
environments. For example, many action sequences will get the taxi to its
destination (thereby achieving the goal) but some are quicker, safer, more reliable,
or cheaper than others. This is achieved by Utility-based agents.
34. ● An agent’s utility function is essentially an internalization of the performance
measure.
● Technically speaking, a rational utility-based agent chooses the action that
maximizes the expected utility of the action outcomes
● We discovered how an agent program selects action with various methods. But
we must also know how the agent program came into being.
● Turin gave a method that proposes to build learning machines and then to teach
them.
● A learning agent can be divided into four conceptual components, as shown
below.
35.
36. ● The learning element is responsible for making improvements and the
performance element is responsible for selecting external actions.
The learning element uses feedback from the critic on how the agent
is doing and determines how the performance element should be
modified to do better in the future.
● The last component of the learning agent is the problem generator. It is
responsible for suggesting actions that will lead to new and informative
experiences.
____________
37.
38. • An agent is something that perceives and acts in an environment. The agent
function for an agent specifies the action taken by the agent in response to any
percept sequence.
• The performance measure evaluates the behavior of the agent in an
environment. A rational agent acts so as to maximize the expected value of the
performance measure, given the percept sequence it has seen so far.
• A task environment specification includes the performance measure, the external
environment, the actuators, and the sensors. In designing an agent, the first step
must always be to specify the task environment as fully as possible.
• Task environments vary along several significant dimensions. They can be fully
or partially observable, single-agent or multiagent, deterministic or stochastic,
episodic or sequential, static or dynamic, discrete or continuous, and known or
unknown.
39. • The agent program implements the agent function. There exists a variety of basic
agent-program designs reflecting the kind of information made explicit and used in
the decision process. The designs vary in efficiency, compactness, and flexibility.
The appropriate design of the agent program depends on the nature of the
environment.
• Simple reflex agents respond directly to percepts, whereas model-based reflex
agents maintain internal state to track aspects of the world that are not evident in
the current percept. Goal-based agents act to achieve their goals, and utility-
based agents try to maximize their own expected “happiness” .
• All agents can improve their performance through learning.
_____________
Editor's Notes
In the book we are going to follow rational agent approach so before women forward we need to know what is rational agent approach?
Agents interact with environments through sensors and actuators
Talk about sensors, actuators, environment, percept, percept sequence.
Talk about sensors, actuators, environment, percept, percept sequence.