This document discusses different types of intelligent agents and their architectures. It defines agents as entities that operate in an environment, perceiving it through sensors and acting upon it through effectors to achieve their goals. The document outlines stimulus-response, state-based, deliberative, utility-based, and learning agent architectures. It also discusses concepts like rational agents, bounded rationality, and PEAS (Performance, Environment, Actuators, Sensors) representations for defining agent tasks.
An intelligent agent perceives its environment via sensors and acts upon that environment with its effectors.
A discrete agent receives percepts one at a time, and maps this percept sequence to a sequence of discrete actions.
Properties
Autonomous
Reactive to the environment
Pro-active (goal-directed)
Interacts with other agents
via the environment
Humans
Sensors: Eyes (vision), ears (hearing), skin (touch), tongue (gustation), nose (olfaction), neuromuscular system (proprioception)
Percepts:
At the lowest level – electrical signals from these sensors
After preprocessing – objects in the visual field (location, textures, colors, …), auditory streams (pitch, loudness, direction), …
Effectors: limbs, digits, eyes, tongue, …
Actions: lift a finger, turn left, walk, run, carry an object, …
The Point: percepts and actions need to be carefully defined, possibly at different levels of abstraction
Introduction of agents, Structure(configuration) of Intelligent agent,
Properties of Intelligent Agents
2.2. PEAS Description of Agents
2.3. Types of Agents: Simple Reflexive, Model Based, Goal Based, Utility Based,
Learning agent.
2.4. Types of Environments: Deterministic/Stochastic, Static/Dynamic,
Observable/Semi-observable, Single Agent/Multi Agent
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.
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 discusses intelligent agents and their characteristics. It defines agents as entities that are autonomous, reactive to their environment, and able to exhibit goal-directed and flexible behavior. Intelligent agents are described as perceiving their environment, taking actions that affect it, and reasoning to determine responses. Examples of agents include a human, with senses and limbs, and a robot, with cameras and motors. The document also introduces the PEAS framework for designing agents, which covers an agent's performance measure, environment, actuators, and sensors.
Artificial Intelligence (AI) is the buzzword there days, wherever we go. However some of the fundamentals / foundations required to program AI remains same as in Embedded Systems. The purpose of this talk is to introduce participants what an Artificial System is, how is it different from conventional system programming. It will provide a basic view of AI architecture and introduce audience with technologies / languages / tools. By the end of the talk audience will get basic knowledge of how AI system can be implemented.
An intelligent agent perceives its environment via sensors and acts upon that environment with its effectors.
A discrete agent receives percepts one at a time, and maps this percept sequence to a sequence of discrete actions.
Properties
Autonomous
Reactive to the environment
Pro-active (goal-directed)
Interacts with other agents
via the environment
Humans
Sensors: Eyes (vision), ears (hearing), skin (touch), tongue (gustation), nose (olfaction), neuromuscular system (proprioception)
Percepts:
At the lowest level – electrical signals from these sensors
After preprocessing – objects in the visual field (location, textures, colors, …), auditory streams (pitch, loudness, direction), …
Effectors: limbs, digits, eyes, tongue, …
Actions: lift a finger, turn left, walk, run, carry an object, …
The Point: percepts and actions need to be carefully defined, possibly at different levels of abstraction
Introduction of agents, Structure(configuration) of Intelligent agent,
Properties of Intelligent Agents
2.2. PEAS Description of Agents
2.3. Types of Agents: Simple Reflexive, Model Based, Goal Based, Utility Based,
Learning agent.
2.4. Types of Environments: Deterministic/Stochastic, Static/Dynamic,
Observable/Semi-observable, Single Agent/Multi Agent
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.
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 discusses intelligent agents and their characteristics. It defines agents as entities that are autonomous, reactive to their environment, and able to exhibit goal-directed and flexible behavior. Intelligent agents are described as perceiving their environment, taking actions that affect it, and reasoning to determine responses. Examples of agents include a human, with senses and limbs, and a robot, with cameras and motors. The document also introduces the PEAS framework for designing agents, which covers an agent's performance measure, environment, actuators, and sensors.
Artificial Intelligence (AI) is the buzzword there days, wherever we go. However some of the fundamentals / foundations required to program AI remains same as in Embedded Systems. The purpose of this talk is to introduce participants what an Artificial System is, how is it different from conventional system programming. It will provide a basic view of AI architecture and introduce audience with technologies / languages / tools. By the end of the talk audience will get basic knowledge of how AI system can be implemented.
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.
Artificial Intelligence and Machine Learning.pptxMANIPRADEEPS1
Artificial intelligence and machine learning agents can be categorized based on their architecture, characteristics, and type. The document discusses several types of agents including simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, learning agents, multi-agent systems, and hierarchical agents. It also covers reasoning methods like forward chaining and backward chaining.
Detail about agent with it's types in AI bhubohara
This document discusses different types of agents in artificial intelligence. It defines an agent as anything that can perceive its environment through sensors and act upon the environment through actuators. The document outlines 5 types of agents: 1) Simple reflex agents that act only based on current percepts; 2) Model-based reflex agents that maintain an internal model of the world; 3) Goal-based agents that take actions to reduce distance from a goal; 4) Utility-based agents that choose actions to maximize expected utility; and 5) Learning agents that can improve through learning from experiences.
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.
An intelligent agent is an entity that is situated in an environment, autonomous, and flexible. It perceives its environment through sensors and acts upon the environment through effectors. There are different types of agents including simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. Environments can be fully or partially observable, deterministic or stochastic, static or dynamic, discrete or continuous, and involve a single agent or multiple agents. Examples of environments include chess, poker, backgammon, taxi driving, medical diagnosis, and image analysis.
Intelligent agents are anything that perceives its environment through sensors and acts to achieve goals. They can be described using the PAGE framework of percepts, actions, goals, and environment. Rational agents choose actions that are expected to maximize performance given past experiences. Different agent types include reflex, state-based, goal-based, utility-based, and learning agents.
Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. The goals of AI include replicating human intelligence, solving knowledge-intensive tasks, and performing tasks through an intelligent connection of perception and action. Breadth-first search is an uninformed search algorithm that searches the shallowest nodes in a tree or graph first. It uses a queue data structure and explores all the neighbor nodes at the present level before moving to the next level.
The document defines key concepts in artificial intelligence including intelligent agents, environments, and rational agents. An intelligent agent is anything that can perceive its environment and take actions to achieve its goals. Rational agents aim to maximize their performance measure given their percepts and knowledge. Different types of agents are described including reflex agents, model-based agents, goal-based agents, and utility-based agents. State representations and learning agents are also covered at a high level.
An agent can be anything that perceives its environment and acts upon it. There are three main types of agents: human agents that use senses and limbs, robotic agents that use cameras/sensors and motors, and software agents that use inputs like keystrokes and display outputs. An agent operates in a cycle of perceiving, thinking, and acting. Sensors detect environmental changes and actuators allow the agent to act. Intelligent agents autonomously achieve goals using sensors and actuators. Rational agents perform optimally to maximize their performance measure. The PEAS model defines an agent's performance criteria, environment, actuators, and sensors. Learning agents improve through experience by incorporating a learning element, critic, performance element, and problem
An intelligent agent is an autonomous entity that perceives its environment and takes actions to maximize its chances of successfully achieving its goals. It has sensors to observe the environment and actuators to perform actions. A rational intelligent agent selects actions that are expected to be most useful based on its past experiences and built-in knowledge. Specifying the task environment through PEAS - performance measure, environment, actuators, and sensors - helps define the problem an intelligent agent aims to solve.
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.
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.
The document discusses different types of intelligent agents. It defines agents as anything that can perceive its environment and act upon that environment. It provides examples of human, robotic, and software agents. The document also discusses rational agents and how they should strive to maximize their performance based on their perceptions and available actions. Finally, it outlines different types of environments that agents may operate in, including fully/partially observable, deterministic/stochastic, and single/multi-agent environments.
The document provides an overview of artificial intelligence (AI) and intelligent agents. It defines AI as the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence. An intelligent agent is described as anything that perceives its environment, takes actions autonomously to achieve goals, and may improve its performance through learning or using knowledge. The key components of an intelligent agent are described as its architecture, agent function that maps perceptions to actions, and agent program that implements the function. Different types of agents are discussed including simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents.
An agent is anything that perceives its environment through sensors and acts upon it through actuators. A rational agent aims to maximize its performance measure by selecting actions expected to have the best outcome, given its percepts and built-in knowledge. To design a rational agent, its task environment must be specified using the PEAS framework of Performance measure, Environment, Actuators, and Sensors. There are four main types of agents: simple reflex agents that react solely based on current percepts; model-based reflex agents that also consider past states; goal-based agents that take future goals into account; and utility-based agents that choose actions to maximize expected utility or happiness.
Artificial Intelligence (AI) is a rapidly evolving field that involves creating intelligent machines capable of performing tasks that traditionally require human intelligence. It encompasses machine learning, deep learning, natural language processing, computer vision, and more, each playing a critical role in various AI applications. Machine learning, in particular, enables computers to learn from data and make predictions autonomously, while deep learning has revolutionized complex tasks like image and speech recognition. AI has a profound impact on industries such as healthcare, finance, transportation, and entertainment, offering solutions that enhance efficiency and decision-making. However, as AI continues to advance, there are important discussions about ethical and societal implications, including issues like privacy, bias, and the changing landscape of the job market.
Artificial Intelligence's influence on our lives continues to grow, with AI-powered technologies becoming integral to everyday experiences. For instance, natural language processing enables chatbots to provide customer support, virtual assistants to answer questions, and language translation services to break down communication barriers. Computer vision is behind the development of self-driving cars, facial recognition systems, and security surveillance applications. Robotics, guided by AI, is transforming industries by automating tasks in manufacturing, agriculture, and healthcare. Reinforcement learning is paving the way for autonomous robots and enhancing gaming experiences. The promise of AI is vast, from improving medical diagnosis to making transportation safer and more efficient. However, it also raises concerns about data privacy, algorithmic bias, and the potential for job displacement as automation and AI adoption increase. As the field continues to advance, it's crucial to strike a balance between harnessing the benefits of AI and addressing its ethical and societal challenges.
An agent is an entity that perceives its environment through sensors and acts upon it using actuators. An environment is the external context in which an agent operates. The document discusses different types of agents including human agents, robotic agents, simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. It also describes the key components of an agent as perception, decision-making, action, and knowledge base. Finally, it provides an example of a self-driving car as an agent that must safely navigate road environments using sensors and actuators.
The document defines different approaches to artificial intelligence including:
1. Systems that think like humans through cognitive modeling of human thought processes.
2. Systems that think rationally by following logical rules and principles like Aristotle's laws of thought.
3. Systems that act rationally by perceiving the environment, acting to achieve goals based on beliefs, and being modeled as rational agents.
PRISMOID is a comprehensive 3D structure database for post-translational modifications and mutations with functional impact. It contains over 17,000 PTM sites from nearly 4,000 proteins annotated with 37 different types of PTMs. PRISMOID also annotates disease mutations affecting PTM sites. It collects protein structural features like secondary structure, solvent accessibility, and disorder regions. PRISMOID maps PTM sites from sequence databases to 3D protein structures from the PDB. It aims to provide an interactive resource for visualizing protein structures with PTMs and their associations with disease mutations.
GlyStruct is a machine learning model that uses structural properties of amino acid residues to predict glycated and non-glycated lysine residues with improved 10% performance over existing methods. It extracts features including secondary structure, accessible surface area, and local backbone torsion angles from a dataset of 1753 lysine sites using the SPIDER2 toolbox. A support vector machine classifier is trained on a 104-dimensional feature vector constructed from a 13 amino acid segment window to predict glycation.
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.
Artificial Intelligence and Machine Learning.pptxMANIPRADEEPS1
Artificial intelligence and machine learning agents can be categorized based on their architecture, characteristics, and type. The document discusses several types of agents including simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, learning agents, multi-agent systems, and hierarchical agents. It also covers reasoning methods like forward chaining and backward chaining.
Detail about agent with it's types in AI bhubohara
This document discusses different types of agents in artificial intelligence. It defines an agent as anything that can perceive its environment through sensors and act upon the environment through actuators. The document outlines 5 types of agents: 1) Simple reflex agents that act only based on current percepts; 2) Model-based reflex agents that maintain an internal model of the world; 3) Goal-based agents that take actions to reduce distance from a goal; 4) Utility-based agents that choose actions to maximize expected utility; and 5) Learning agents that can improve through learning from experiences.
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.
An intelligent agent is an entity that is situated in an environment, autonomous, and flexible. It perceives its environment through sensors and acts upon the environment through effectors. There are different types of agents including simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. Environments can be fully or partially observable, deterministic or stochastic, static or dynamic, discrete or continuous, and involve a single agent or multiple agents. Examples of environments include chess, poker, backgammon, taxi driving, medical diagnosis, and image analysis.
Intelligent agents are anything that perceives its environment through sensors and acts to achieve goals. They can be described using the PAGE framework of percepts, actions, goals, and environment. Rational agents choose actions that are expected to maximize performance given past experiences. Different agent types include reflex, state-based, goal-based, utility-based, and learning agents.
Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. The goals of AI include replicating human intelligence, solving knowledge-intensive tasks, and performing tasks through an intelligent connection of perception and action. Breadth-first search is an uninformed search algorithm that searches the shallowest nodes in a tree or graph first. It uses a queue data structure and explores all the neighbor nodes at the present level before moving to the next level.
The document defines key concepts in artificial intelligence including intelligent agents, environments, and rational agents. An intelligent agent is anything that can perceive its environment and take actions to achieve its goals. Rational agents aim to maximize their performance measure given their percepts and knowledge. Different types of agents are described including reflex agents, model-based agents, goal-based agents, and utility-based agents. State representations and learning agents are also covered at a high level.
An agent can be anything that perceives its environment and acts upon it. There are three main types of agents: human agents that use senses and limbs, robotic agents that use cameras/sensors and motors, and software agents that use inputs like keystrokes and display outputs. An agent operates in a cycle of perceiving, thinking, and acting. Sensors detect environmental changes and actuators allow the agent to act. Intelligent agents autonomously achieve goals using sensors and actuators. Rational agents perform optimally to maximize their performance measure. The PEAS model defines an agent's performance criteria, environment, actuators, and sensors. Learning agents improve through experience by incorporating a learning element, critic, performance element, and problem
An intelligent agent is an autonomous entity that perceives its environment and takes actions to maximize its chances of successfully achieving its goals. It has sensors to observe the environment and actuators to perform actions. A rational intelligent agent selects actions that are expected to be most useful based on its past experiences and built-in knowledge. Specifying the task environment through PEAS - performance measure, environment, actuators, and sensors - helps define the problem an intelligent agent aims to solve.
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.
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.
The document discusses different types of intelligent agents. It defines agents as anything that can perceive its environment and act upon that environment. It provides examples of human, robotic, and software agents. The document also discusses rational agents and how they should strive to maximize their performance based on their perceptions and available actions. Finally, it outlines different types of environments that agents may operate in, including fully/partially observable, deterministic/stochastic, and single/multi-agent environments.
The document provides an overview of artificial intelligence (AI) and intelligent agents. It defines AI as the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence. An intelligent agent is described as anything that perceives its environment, takes actions autonomously to achieve goals, and may improve its performance through learning or using knowledge. The key components of an intelligent agent are described as its architecture, agent function that maps perceptions to actions, and agent program that implements the function. Different types of agents are discussed including simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents.
An agent is anything that perceives its environment through sensors and acts upon it through actuators. A rational agent aims to maximize its performance measure by selecting actions expected to have the best outcome, given its percepts and built-in knowledge. To design a rational agent, its task environment must be specified using the PEAS framework of Performance measure, Environment, Actuators, and Sensors. There are four main types of agents: simple reflex agents that react solely based on current percepts; model-based reflex agents that also consider past states; goal-based agents that take future goals into account; and utility-based agents that choose actions to maximize expected utility or happiness.
Artificial Intelligence (AI) is a rapidly evolving field that involves creating intelligent machines capable of performing tasks that traditionally require human intelligence. It encompasses machine learning, deep learning, natural language processing, computer vision, and more, each playing a critical role in various AI applications. Machine learning, in particular, enables computers to learn from data and make predictions autonomously, while deep learning has revolutionized complex tasks like image and speech recognition. AI has a profound impact on industries such as healthcare, finance, transportation, and entertainment, offering solutions that enhance efficiency and decision-making. However, as AI continues to advance, there are important discussions about ethical and societal implications, including issues like privacy, bias, and the changing landscape of the job market.
Artificial Intelligence's influence on our lives continues to grow, with AI-powered technologies becoming integral to everyday experiences. For instance, natural language processing enables chatbots to provide customer support, virtual assistants to answer questions, and language translation services to break down communication barriers. Computer vision is behind the development of self-driving cars, facial recognition systems, and security surveillance applications. Robotics, guided by AI, is transforming industries by automating tasks in manufacturing, agriculture, and healthcare. Reinforcement learning is paving the way for autonomous robots and enhancing gaming experiences. The promise of AI is vast, from improving medical diagnosis to making transportation safer and more efficient. However, it also raises concerns about data privacy, algorithmic bias, and the potential for job displacement as automation and AI adoption increase. As the field continues to advance, it's crucial to strike a balance between harnessing the benefits of AI and addressing its ethical and societal challenges.
An agent is an entity that perceives its environment through sensors and acts upon it using actuators. An environment is the external context in which an agent operates. The document discusses different types of agents including human agents, robotic agents, simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. It also describes the key components of an agent as perception, decision-making, action, and knowledge base. Finally, it provides an example of a self-driving car as an agent that must safely navigate road environments using sensors and actuators.
The document defines different approaches to artificial intelligence including:
1. Systems that think like humans through cognitive modeling of human thought processes.
2. Systems that think rationally by following logical rules and principles like Aristotle's laws of thought.
3. Systems that act rationally by perceiving the environment, acting to achieve goals based on beliefs, and being modeled as rational agents.
PRISMOID is a comprehensive 3D structure database for post-translational modifications and mutations with functional impact. It contains over 17,000 PTM sites from nearly 4,000 proteins annotated with 37 different types of PTMs. PRISMOID also annotates disease mutations affecting PTM sites. It collects protein structural features like secondary structure, solvent accessibility, and disorder regions. PRISMOID maps PTM sites from sequence databases to 3D protein structures from the PDB. It aims to provide an interactive resource for visualizing protein structures with PTMs and their associations with disease mutations.
GlyStruct is a machine learning model that uses structural properties of amino acid residues to predict glycated and non-glycated lysine residues with improved 10% performance over existing methods. It extracts features including secondary structure, accessible surface area, and local backbone torsion angles from a dataset of 1753 lysine sites using the SPIDER2 toolbox. A support vector machine classifier is trained on a 104-dimensional feature vector constructed from a 13 amino acid segment window to predict glycation.
This document summarizes recent research on COVID-19. It discusses the structure of coronaviruses and how they interact with the ACE2 receptor to infect cells. It also summarizes research using protein structure prediction, molecular modeling, and next generation sequencing to understand the virus and identify potential drug targets. Post-translational glycosylation of the spike protein is discussed and how it may help the virus evade immune detection.
This presentation discusses using a Long Short Term Memory (LSTM) neural network model to predict global COVID-19 cases. It provides background on COVID-19 and reviews literature applying machine learning to predict disease spread. The methodology collects real-time case data, performs exploratory analysis, and trains an LSTM on 90% of normalized data. The model predicts daily cases for the held-out 10% and is evaluated on root mean square error. While the deep learning model captures some patterns, predictions are difficult given the pandemic's volatility. Social distancing remains important for health.
Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks can be used for sequence modeling tasks like predicting the next word. RNNs apply the same function to each element of a sequence but struggle with long-term dependencies. LSTMs address this with a gated cell that can maintain information over many time steps by optionally adding, removing, or updating cell state. LSTMs are better for tasks like language modeling since they can remember inputs from much earlier in the sequence. RNNs and LSTMs have applications in areas like music generation, machine translation, and predictive modeling.
Characterization and identification of lysine succinylation sites basedSubash Chandra Pakhrin
This document summarizes a research paper that uses deep learning methods to characterize and identify lysine succinylation sites in proteins. The researchers extracted four types of sequence-based features from protein data and used them to train convolutional neural networks (CNNs) for binary classification of succinylation sites versus non-sites. They evaluated ten-fold cross-validation of CNN models trained on different attribute combinations. The best performing model used position-specific scoring matrices and achieved an accuracy of 88%. However, the paper did not consider additional physicochemical properties that could improve predictions.
Convolutional neural networks (CNNs) are a type of neural network used in image recognition and processing. CNNs use convolutional layers that apply filters to input volumes to extract features at different spatial locations. Backpropagation is used to train CNNs by propagating errors backwards. CNNs have been successfully applied to tasks like ImageNet classification, object detection, and image captioning. Convolutional layers are the core building blocks of CNNs, applying filters to input volumes to produce activation maps as output.
Convolutional neural networks (CNNs) are a type of neural network used in image recognition and processing. CNNs use convolutional layers that apply filters to input volumes to extract features at different spatial locations. Backpropagation is used to train CNNs by propagating errors backwards. CNNs have been successfully applied to large-scale image classification tasks using datasets like ImageNet, with AlexNet achieving breakthrough results in 2012. CNNs employ several convolutional layers interspersed with activation functions to process input volumes while avoiding excessive spatial shrinking.
This document summarizes three AI lab experiments:
1) A monkey and banana problem solved using Prolog code to represent states and moves to get the monkey from the floor to the banana hanging from the roof.
2) A water jug problem to get exactly 2 liters of water in a 4 liter jug using a pump and two jugs of 4 and 3 liters. States are represented as jug volumes and moves involve filling, pouring, and emptying jugs.
3) A farmer, wolf, goat, and cabbage river crossing puzzle where the farmer must transport all items across the river in a boat that holds himself and one other item, avoiding unsafe states like leaving the wolf and goat alone.
The monkey is on the floor by the door, a block is on the floor by the window, and a banana is hanging from the roof in the middle of the room. The problem presented is how the monkey can get the banana.
The document discusses two modern techniques for determining system requirements: Joint Application Design (JAD) and prototyping. JAD involves collaborative workshops between users, analysts, and other stakeholders to jointly design systems. It leads to shorter development times and greater user satisfaction. Prototyping allows quickly building preliminary versions of a system to gather more requirements from user testing and feedback. Both techniques can effectively gather requirements while reducing analysis time compared to traditional methods.
This document discusses the process of identifying and selecting information systems development projects. It begins by outlining the three primary activities of project identification, classification and ranking, and selection. It then provides details on each step, including how potential projects are identified, common criteria for evaluating projects, and factors to consider when selecting projects. The deliverables are identified as a schedule of selected IS development projects. The outcomes include ensuring careful consideration was given to project selection and understanding how each project could help the organization.
This document provides an overview of intelligent agents. It defines key concepts such as agents, environments, rational agents, and bounded rationality. It discusses different types of agent architectures including reactive agents, state-based agents, deliberative agents, utility-based agents, and learning agents. The document also describes different types of environments based on their observability, determinism, episodicity, dynamism, continuity, and whether they involve single or multiple agents. Overall, the document aims to familiarize readers with intelligent agents and their components and characteristics.
The document describes various search algorithms used to solve pathfinding problems represented as graphs. It discusses uninformed searches like breadth-first search (BFS) and depth-first search (DFS) as well as methods for evaluating search performance in terms of completeness, time complexity, space complexity, and optimality. BFS uses a queue to expand shallow nodes first, guaranteeing an optimal solution but with exponential time and space costs. DFS uses a stack to expand deep nodes first, providing better space efficiency but not guaranteeing an optimal solution.
The document discusses various informed search algorithms including A*, greedy search, and uniform cost search. It provides instructional objectives for learning about heuristic functions, designing heuristics for problems, and comparing heuristic functions. Key aspects of A* search are summarized, including that it uses an admissible heuristic function to find optimal solutions, and conditions like admissibility and consistency that guarantee its optimality.
Hill climbing is a local search algorithm that starts with a random solution and iteratively makes small changes to improve the solution. It terminates when no further improvements can be made. Hill climbing can get stuck at local optima rather than finding the global optimum. Simulated annealing is similar to hill climbing but allows occasional "downhill moves" that worsen the solution based on a probability function involving the change in solution quality and temperature parameter. The temperature is gradually decreased, reducing the probability of downhill moves over time. This helps simulated annealing avoid local optima and find better solutions than hill climbing.
Two player games involve two players alternating turns trying to maximize or minimize the outcome of a game. The MiniMax algorithm is commonly used to choose the best move for a player in two player games by recursively evaluating all possible future moves. Alpha-beta pruning improves upon MiniMax by pruning branches that cannot influence the final outcome, allowing deeper search of the game tree within the same time limit.
The document provides an introduction to state space search problems and algorithms. It discusses key concepts like the state space representation, initial and goal states, actions/operators that transform states, and different search strategies. Specific examples covered include the vacuums world problem, towers of Hanoi, water jugs problem, and the 8 queens puzzle. The document also introduces production systems and how they can be used to represent state space search problems.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
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Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
2. Instructional Objective
• Define an agent
• Define an intelligent agent
• Define a Rational agent
• Explain Bounded rationality
• Discuss different types of environment
• Explain different agent architectures
2AI, Subash Chandra Pakhrin
3. Instructional Objective
On completion of this lesson the student will be able to
• Understand what an agent is and how an agent
interacts with the environment.
• Given a problem situation, the student should be able
to
– Identify the percepts available to the agent and
– the actions that the agent can execute.
• Understand the performance measures used to
evaluate an agent
• Understand the definition of a rational agent
• Understand the concept of bounded rationality
3AI, Subash Chandra Pakhrin
4. Instructional Objective
• On completion of this lesson the student will
– Be familiar with
• Different agent architectures
• Stimulus response agents
• State based agents
• Deliberative / goal – directed agents
• Utility based agents
• Learning agents
– Be able to analyze a problem situation and be able to
• Identify the characteristics of the environment
• Recommend the architecture of the desired agent
4AI, Subash Chandra Pakhrin
6. Agents
• Operate in an environment
• Perceives its environment through sensors
• Acts upon its environment through
actuators/effectors
• Have goals
6AI, Subash Chandra Pakhrin
7. Sensors and effectors
• An agent perceives its environment through
sensors
– The complete set of inputs at a given time is called a
percept
– The current percept, or a sequence of percepts can
influence the actions of an agent
• It can change the environment through effectors
– An operation involving an actuator is called an action
– Actions can be grouped into action sequences
7AI, Subash Chandra Pakhrin
8. Agents
• Have sensors, actuators
• Have goals
• Implement mapping from
percept sequence to
actions
• Performance measure to
evaluate agents
• Autonomous agent decide
autonomously which
action to take in the
current situation to
maximize progress towards
its goals.
8AI, Subash Chandra Pakhrin
9. Performance
• Behavior and performance of IAs in terms of
agent function
– Perception history (sequence) to Action Mapping
– Ideal Mapping: specifies which actions an agent to
take at any point in time
• Performance measure: a subjective measure
to characterize how successful an agent is
(e.g., speed, power usage, accuracy, money,
etc)
9AI, Subash Chandra Pakhrin
10. Examples of Agent
• Humans
– Eyes, ears, skin, taste buds, etc. for sensors
• Robots
– Camera, infrared, bumper, etc. for sensors
– Grippers, wheels, lights, speakers, etc. for
actuators
• Software agent (soft bots)
– Functions as sensors
– Functions as actuators
10AI, Subash Chandra Pakhrin
12. Types of Agents: Robots
https://www.youtube.com/watch?v=8t8fyiiQVZ0
The AIBO Entertainment
Robot is a totally new
kind of robot-
autonomous, sensitive to
his environment, and able
to learn and mature like a
living creature. Since each
AIBO experiences his
world differently, each
develops his own unique
personality – different
from any other AIBO in
the world!
Aibo (SONY)
12AI, Subash Chandra Pakhrin
14. Agents
• Fundamental faculties of intelligence
– Acting
– Sensing
– Understanding, reasoning, learning
• In order to act you must sense. Blind actions is
not a characterization of intelligence.
• Robotics: sensing and acting, understanding
not necessary.
• Sensing needs understanding to be useful.
14AI, Subash Chandra Pakhrin
15. Intelligent Agents
• Intelligent Agents
– Must sense
– Must act
– Must be autonomous (to some extent),
– Must be rational.
15AI, Subash Chandra Pakhrin
16. Rational Agent
• AI is about building rational agents
• An agent is something that perceives and acts.
• A rational agent always does the right thing.
– What are the functionalities (goals) ?
– What are the components ?
– How do we build them ?
16AI, Subash Chandra Pakhrin
17. Rationality
• Perfect Rationality
– Assumes that the rational agent knows all and will
take the action that maximizes his/her utility.
– Equivalent to demanding that the agent is Omniscient.
– Human beings do not satisfy this definition of
rationality.
• Bounded Rationality Herbert Simon, 1972 (CMU)
– Because of the limitations of the human kind, humans
must use approximate methods to handle many tasks.
17AI, Subash Chandra Pakhrin
18. Rationality
• Rational Action: The action that maximizes the
expected value of the performance measure
given the percept sequence to date
– Rational = Best ?
• Yes, to the best of its knowledge
– Rational = Optimal ?
• Yes, to the best of its abilities
• And its constraints
18AI, Subash Chandra Pakhrin
19. Omniscience
• A rational agent is not omniscient
– It doesn’t know the actual outcome of its actions
– It may not know certain aspects of its
environment.
• Rationality must take into account the
limitations of the agent
– Percept sequence, background knowledge,
feasible actions
– Deal with the expected outcome of actions
19AI, Subash Chandra Pakhrin
20. Bounded Rationality
• Evolution did not give rise to optimal agents,
but to agents which are in some senses locally
optimal at best.
• In 1957, Simon proposed the notion of
Bounded Rationality:
that property of an agent that behaves in a
manner that is nearly optimal with respect to
its goals as its resources will allow.
20AI, Subash Chandra Pakhrin
21. Agent Environment
• Environments in which agents operate can be
defined in different ways.
It is helpful to view the following definitions as
referring to the way the environment appears
from the point of view of the agent itself.
21AI, Subash Chandra Pakhrin
22. Environment: Observability
• Fully observable
– All of the environment relevant to the action being
considered is observable
– Such environments are convenient, since the agent is
freed from the task of keeping track of the change in
the environment.
• Partially observable
– The relevant features of the environment are only
partially observable
• Example:
– Fully obs: Chess; Partially obs: Poker
22AI, Subash Chandra Pakhrin
23. Environment: Determinism
• Deterministic: The next state of the environment
is completely described by the current state and
the agent’s action. Image analysis
• Stochastic: If an element of interference or
uncertainty occurs then the environment is
stochastic. Note that a deterministic yet partially
observable environment will appear to be
stochastic to the agent. Ludo
• Strategic: environment state wholly determined
by the preceding state and the actions of multiple
agents is called strategic. Chess
23AI, Subash Chandra Pakhrin
24. Environment: Episodicity
• Episodic / Sequential
– An episodic environment means that subsequent
episodes do not depend on what actions occurred
in pervious episodes.
– In a sequential environment, the agent engages in
a series of connected episodes.
24AI, Subash Chandra Pakhrin
25. Environment: Dynamism
• Static Environment: does not change from one
state to the next while the agent is considering its
course of action. The only changes to the
environment as those caused by the agent itself
• Dynamic Environment: Changes over time
independent of the actions of the agent – and
thus if an agent does not respond in a timely
manner, this counts as a choice to do nothing
– Interactive tutor
25AI, Subash Chandra Pakhrin
26. Environments: Continuity
• Discrete / Continuous
– If the number of distinct percepts and actions is
limited, the environment is discrete, otherwise it
is continuous.
26AI, Subash Chandra Pakhrin
27. Environments: other agents
• Single agent/ Multi-agent
– If the environment contains other intelligent
agents, the agent needs to be concerned about
strategic, game – theoretic aspects of the
environment (for either cooperative or
competitive agents)
– Most engineering environments don’t have multi-
agent properties, whereas most social and
economic systems get their complexity from the
interactions of (more or less) rational agents.
27AI, Subash Chandra Pakhrin
28. Complex Environments
• Complexity of the environment includes
– Knowledge rich: enormous amount of information that the
environment contains and
– Input rich: the enormous amount of input the
environment can send to an agent. T
• The agent must have a way of manage this complexity.
Often such considerations lead to the development of
– Sensing strategies and
– Attentional mechanisms
• So that the agent may more readily focus its efforts in
such rich environments.
28AI, Subash Chandra Pakhrin
29. Table based agent
• Information comes from sensors – percepts
• Look it up !
• Triggers actions through the effectors
• In table based agent the mapping from percepts to
actions is stored in the form of table
Reactive agents
No notion of history, the current state is the sensors see it
right now.
Percepts Actions
29AI, Subash Chandra Pakhrin
30. Table based agent
• a table is simple way to specify a mapping from
percepts to actions
– Tables may become very large
– All work done by the designer
– No autonomy, all actions are predetermined
– Learning might take a very long time
• Mapping is implicitly defined by a program
– Rule based
– Neural networks
– algorithms
30AI, Subash Chandra Pakhrin
32. Percept based agent
• Information comes from sensors – percepts
• Changes the agents current state of the world
• Triggers action through the effectors
Reactive agents / Stimulus – response agents
Agent takes some action without any deliberation
No notion of history, the current state is as the
sensors see it right now.
32AI, Subash Chandra Pakhrin
33. Subsumption Architecture
• Rodney Brooks, 1986
• Sensory inputs – action (lower animals)
• Brooks – follow the evolutionary path and build
simple agents for complex worlds.
• Features
– No explicit knowledge representation
– Distributed behavior (not centralized)
– Response to stimuli is reflexive
– Bottom up design – complex behaviors fashioned from
the combination of simpler underlying ones.
– Inexpensive individual agents
33AI, Subash Chandra Pakhrin
34. Subsumption Architecture
• Subsumption Architecture built in layers.
• Time scale of evolution – 5 billion years (cells)
– First humans – 2.5 million years
– Symbols – 5000 years
• Different layers of behavior
• Higher layers can override lower layers.
• Each activity consists of a Finite State Machine
(FSM).
34AI, Subash Chandra Pakhrin
35. Mobile Robot Example
• Layer 0: Avoid Obstacles
– Sonar: generate sonar scan
– Collide: send HALT message
to forward
– Feel force: signal sent to
run-away, turn
• Layer 1: Wander Behavior
– Generates a random
heading
– Avoid reads repulsive force,
generates new heading,
feeds to turn and forward
35AI, Subash Chandra Pakhrin
36. Mobile Robot Example
• Layer 2: Exploration
behavior
– Whenlook notices idle
time and looks for an
interesting place.
– Pathplan sends new
direction to avoid.
– Integrate monitors
path and sends them
to the path plan.
36AI, Subash Chandra Pakhrin
37. Percept based Agent
• Efficient
• No internal representation for reasoning,
interference.
• No strategic planning, learning.
• Percept-based agents are not good for
multiple, opposing, goals.
37AI, Subash Chandra Pakhrin
39. Simple Reflex Agent (E.g. Chess)
• They choose actions only based on the current
percept.
• Their environment is completely observable.
• Condition-Action Rule – It is a rule that maps a
state (condition) to an action
Vacuum cleaner: Condition
if dirty then clean
if it is clean then stop
AI, Subash Chandra Pakhrin 39
41. Model Based Reflex Agents
• It doesn’t know about the whole world
• It has the knowledge of the partial world
• It keeps a small model in which the agent can
predict and control the world / perform the
action.
• There is a predefined model which we keep on
the basis of history and we predict what
action we should take now.
AI, Subash Chandra Pakhrin 41
42. Updating the state requires the
information about -
• How the world evolves.
• How the agent’s actions affect the world.
AI, Subash Chandra Pakhrin 42
43. State based Agent
• Information comes from sensors-percepts
• Changes the agents current state of the world
• Based on state of the world and knowledge
(memory), it triggers actions through the
effectors
• In order to this the agent does some
deliberation
43AI, Subash Chandra Pakhrin
44. Goal-based Agent
• Information comes from sensors-percepts
• Changes the agents current state of the world
• Based on state of the world and knowledge (memory)
and goals/intentions, it chooses actions and does them
through the effectors.
• Agent’s actions will depend upon its goal.
• Goal formulation based on the current situation is a
way of solving many problems and search is a universal
problem solving mechanism in AI.
• The sequence of steps required to solve a problem is
not known a priori and must be determined by a
systematic exploration of the alternatives.
44AI, Subash Chandra Pakhrin
45. Goal-based Agent
• Knowing something about current state of
environment is not enough to decide what to
do
• Agent also needs some sort of goal
information
AI, Subash Chandra Pakhrin 45
47. Utility based agent
• A more general framework
• Different preferences for different goals
• A utility function maps a state or a sequence
of states to a real valued utility.
• The agent acts so as to maximize expected
utility.
47AI, Subash Chandra Pakhrin
49. Learning Agent
• Learning allows an agent to operate in initially
unknown environments
• The learning element modifies the
performance element
• Learning is required for true autonomy
49AI, Subash Chandra Pakhrin
51. PEAS Representation
• Task Environment
– Problems to which rational agent are solutions
– In task environment there are problems and they
are solved by rational agent
AI, Subash Chandra Pakhrin 51
52. To specify Task environment, we need
• P – Performance measures (what output are
you expecting from an agent)
• E – Environment
• A – Actuators
• S - Sensors
AI, Subash Chandra Pakhrin 52
53. Automated Taxi Driver Agent
• Performance Measures:
– Getting to correct
Destination
– Less cost
– High safety
• Environment:
– Variety of roads
– Traffic
– Different Types of passenger
AI, Subash Chandra Pakhrin 53
• Actuators:
– Accelerators
– Steering & Brakes
• Sensors:
– Camera
– GPS
– IR Sensors
55. Summary
• An agent perceives and acts in an
environment, has an architecture, and is
implemented by an agent program.
• An ideal agent always chooses the action
which maximizes its expected performance,
given its percepts sequence so far.
• An autonomous agent uses its own experience
rather than built-in knowledge of the
environment by the designer.
55AI, Subash Chandra Pakhrin
56. Summary
• An agent program maps from percept to action and
updates its internal state.
– Reflex agents respond immediately to percepts.
– Goal-based agents act in order to achieve their goal(s).
– Utility-based agents maximize their own utility function.
• Representing Knowledge is important for successful
agent design.
• The most challenging environments are partially
observable, stochastic, sequential, dynamic, and
continuous, and contain multiple intelligent agents.
56AI, Subash Chandra Pakhrin
57. Questions
1. Define an agent.
2. What is a rational agent ?
3. What is bounded rationality ?
4. What is an autonomous agent ?
5. Describe the salient features of an agent.
57AI, Subash Chandra Pakhrin
58. Questions
6. Find out about the Mars rover.
a. What are the percepts for this agent ?
b. Characterize the operating environment.
c. What are the actions the agent an take ?
d. How can one evaluate the performance of the agent
?
e. What sort of agent architecture do you think is most
suitable for this agent ?
7. Answer the same questions as above for an
Internet shopping agent.
58AI, Subash Chandra Pakhrin
Editor's Notes
Subsumption: A statement that is assumed to be true and from which a conclusion can be drawn
Rodney Brooks argument was the lower animals behave largely in a reactive manner, they have very little sense of deliberation, so most of the actions are reactive actions.
His argument was that in the time scale of evolution reactive behavior came much earlier than deliberative behavior.
And he has been able to show that simple reactive behavior, a number of such components having simple reactive behavior can achieve a surprising degree of intelligence.