A rational agent is an artificial intelligence agent that has clear preferences, models uncertainty, and acts to maximize its performance based on possible actions. Rational agents are designed to make the right decisions using techniques from game theory and decision theory for real-world problems. An AI agent is considered rational if it selects the best possible action in each situation to receive a positive reward, avoiding wrong actions that result in negative rewards. The rationality of an agent depends on factors like its performance measure, prior knowledge of its environment, available actions, and percepts.
Problem solving
Problem formulation
Search Techniques for Artificial Intelligence
Classification of AI searching Strategies
What is Search strategy ?
Defining a Search Problem
State Space Graph versus Search Trees
Graph vs. Tree
Problem Solving by Search
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Intelligent Agent PPT ON SLIDESHARE IN ARTIFICIAL INTELLIGENCEKhushboo Pal
n artificial intelligence, an intelligent agent (IA) is an autonomous entity which acts, directing its activity towards achieving goals (i.e. it is an agent), upon an environment using observation through sensors and consequent actuators (i.e. it is intelligent).An intelligent agent is a program that can make decisions or perform a service based on its environment, user input and experiences. These programs can be used to autonomously gather information on a regular, programmed schedule or when prompted by the user in real time. Intelligent agents may also be referred to as a bot, which is short for robot.Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
In which we see how an agent can find a sequence of actions that achieves its goals, when no single action will do.
The method of solving problem through AI involves the process of defining the search space, deciding start and goal states and then finding the path from start state to goal state through search space.
State space search is a process used in the field of computer science, including artificial intelligence(AI), in which successive configurations or states of an instance are considered, with the goal of finding a goal state with a desired property.
Problem solving
Problem formulation
Search Techniques for Artificial Intelligence
Classification of AI searching Strategies
What is Search strategy ?
Defining a Search Problem
State Space Graph versus Search Trees
Graph vs. Tree
Problem Solving by Search
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Intelligent Agent PPT ON SLIDESHARE IN ARTIFICIAL INTELLIGENCEKhushboo Pal
n artificial intelligence, an intelligent agent (IA) is an autonomous entity which acts, directing its activity towards achieving goals (i.e. it is an agent), upon an environment using observation through sensors and consequent actuators (i.e. it is intelligent).An intelligent agent is a program that can make decisions or perform a service based on its environment, user input and experiences. These programs can be used to autonomously gather information on a regular, programmed schedule or when prompted by the user in real time. Intelligent agents may also be referred to as a bot, which is short for robot.Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
In which we see how an agent can find a sequence of actions that achieves its goals, when no single action will do.
The method of solving problem through AI involves the process of defining the search space, deciding start and goal states and then finding the path from start state to goal state through search space.
State space search is a process used in the field of computer science, including artificial intelligence(AI), in which successive configurations or states of an instance are considered, with the goal of finding a goal state with a desired property.
A Heuristic is a technique to solve a problem faster than classic methods, or to find an approximate solution when classic methods cannot. This is a kind of a shortcut as we often trade one of optimality, completeness, accuracy, or precision for speed. A Heuristic (or a heuristic function) takes a look at search algorithms. At each branching step, it evaluates the available information and makes a decision on which branch to follow.
In the field of artificial intelligence (AI), planning refers to the process of developing a sequence of actions or steps that an intelligent agent should take to achieve a specific goal or solve a particular problem. AI planning is a fundamental component of many AI systems and has applications in various domains, including robotics, autonomous systems, scheduling, logistics, and more. Here are some key aspects of planning in AI:
Definition of Planning: Planning involves defining a problem, specifying the initial state, setting a goal state, and finding a sequence of actions or a plan that transforms the initial state into the desired goal state while adhering to certain constraints.
State-Space Representation: In AI planning, the problem is often represented as a state-space, where each state represents a snapshot of the system, and actions transform one state into another. The goal is to find a path through this state-space from the initial state to the goal state.
Search Algorithms: AI planning typically relies on search algorithms to explore the state-space efficiently. Uninformed search algorithms, such as depth-first search and breadth-first search, can be used, as well as informed search algorithms, like A* search, which incorporates heuristics to guide the search.
Heuristics: Heuristics are used in planning to estimate the cost or distance from a state to the goal. Heuristic functions help inform the search algorithms by providing an estimate of how close a state is to the solution. Good heuristics can significantly improve the efficiency of the search.
Plan Execution: Once a plan is generated, the next step is plan execution, where the agent carries out the actions in the plan to achieve the desired goal. This often requires monitoring the environment to ensure that the actions are executed as planned.
Temporal and Hierarchical Planning: In more complex scenarios, temporal planning deals with actions that have temporal constraints, and hierarchical planning involves creating plans at multiple levels of abstraction, making planning more manageable in complex domains.
Partial and Incremental Planning: Sometimes, it may not be necessary to create a complete plan from scratch. Partial and incremental planning allows agents to adapt and modify existing plans to respond to changing circumstances.
Applications: Planning is used in a wide range of applications, from manufacturing and logistics (e.g., scheduling production and delivery) to robotics (e.g., path planning for robots) and game playing (e.g., chess and video games).
Challenges: Challenges in AI planning include dealing with large search spaces, handling uncertainty, addressing resource constraints, and optimizing plans for efficiency and performance.
AI planning is a critical component in creating intelligent systems that can autonomously make decisions and solve complex problems.
Minmax Algorithm In Artificial Intelligence slidesSamiaAziz4
Mini-max algorithm is a recursive or backtracking algorithm that is used in decision-making and game theory. Mini-Max algorithm uses recursion to search through the game-tree.
Min-Max algorithm is mostly used for game playing in AI. Such as Chess, Checkers, tic-tac-toe, go, and various tow-players game. This Algorithm computes the minimax decision for the current state.
A Heuristic is a technique to solve a problem faster than classic methods, or to find an approximate solution when classic methods cannot. This is a kind of a shortcut as we often trade one of optimality, completeness, accuracy, or precision for speed. A Heuristic (or a heuristic function) takes a look at search algorithms. At each branching step, it evaluates the available information and makes a decision on which branch to follow.
In the field of artificial intelligence (AI), planning refers to the process of developing a sequence of actions or steps that an intelligent agent should take to achieve a specific goal or solve a particular problem. AI planning is a fundamental component of many AI systems and has applications in various domains, including robotics, autonomous systems, scheduling, logistics, and more. Here are some key aspects of planning in AI:
Definition of Planning: Planning involves defining a problem, specifying the initial state, setting a goal state, and finding a sequence of actions or a plan that transforms the initial state into the desired goal state while adhering to certain constraints.
State-Space Representation: In AI planning, the problem is often represented as a state-space, where each state represents a snapshot of the system, and actions transform one state into another. The goal is to find a path through this state-space from the initial state to the goal state.
Search Algorithms: AI planning typically relies on search algorithms to explore the state-space efficiently. Uninformed search algorithms, such as depth-first search and breadth-first search, can be used, as well as informed search algorithms, like A* search, which incorporates heuristics to guide the search.
Heuristics: Heuristics are used in planning to estimate the cost or distance from a state to the goal. Heuristic functions help inform the search algorithms by providing an estimate of how close a state is to the solution. Good heuristics can significantly improve the efficiency of the search.
Plan Execution: Once a plan is generated, the next step is plan execution, where the agent carries out the actions in the plan to achieve the desired goal. This often requires monitoring the environment to ensure that the actions are executed as planned.
Temporal and Hierarchical Planning: In more complex scenarios, temporal planning deals with actions that have temporal constraints, and hierarchical planning involves creating plans at multiple levels of abstraction, making planning more manageable in complex domains.
Partial and Incremental Planning: Sometimes, it may not be necessary to create a complete plan from scratch. Partial and incremental planning allows agents to adapt and modify existing plans to respond to changing circumstances.
Applications: Planning is used in a wide range of applications, from manufacturing and logistics (e.g., scheduling production and delivery) to robotics (e.g., path planning for robots) and game playing (e.g., chess and video games).
Challenges: Challenges in AI planning include dealing with large search spaces, handling uncertainty, addressing resource constraints, and optimizing plans for efficiency and performance.
AI planning is a critical component in creating intelligent systems that can autonomously make decisions and solve complex problems.
Minmax Algorithm In Artificial Intelligence slidesSamiaAziz4
Mini-max algorithm is a recursive or backtracking algorithm that is used in decision-making and game theory. Mini-Max algorithm uses recursion to search through the game-tree.
Min-Max algorithm is mostly used for game playing in AI. Such as Chess, Checkers, tic-tac-toe, go, and various tow-players game. This Algorithm computes the minimax decision for the current state.
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.
Intelligent Agents, A discovery on How A Rational Agent ActsSheetal Jain
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.
Hi there.
This presentation is made by me for a presentation in my university.
If you like my work do download and use please just not try to copy paste the content because if you're learning then learn from your heart.
Have great day!
Yay.... :)
AI Agents, Agents in Artificial IntelligenceKirti Verma
HI guys,
I am starting my very first course on Artificial Intelligence(AI).
if your are interested in the above topic you can follow this course to improve your knowledge.
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Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
2. Rational Agent
A rational agent is an agent which has clear preference, models uncertainty, and
acts in a way to maximize its performance measure with all possible actions.
A rational agent is said to perform the right things. AI is about creating rational
agents to use for game theory and decision theory for various real-world
scenarios.
For an AI agent, the rational action is most important because in AI reinforcement
learning algorithm, for each best possible action, agent gets the positive reward
and for each wrong action, an agent gets a negative reward.
3. Rational Agent
The rationality of an agent is measured by its performance measure. Rationality
can be judged on the basis of following points:
Performance measure which defines the success criterion.
Agent prior knowledge of its environment.
Best possible actions that an agent can perform.
The sequence of percepts.
Rationality differs from Omniscience because an Omniscient agent knows the actual
outcome of its action and act accordingly, which is not possible in reality.
4. A rational agent should depend on the
following factors at any given time:
• Perception: The ability to perceive the current state of the
environment and gather relevant information.
• Actuators: The ability to take actions within the environment to
achieve its goals.
• Performance measure: A way to evaluate the success or failure of
the agent’s actions.
• Information: The agent’s knowledge of its environment, which may
come from past experiences or other sources.
