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
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 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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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 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
This document discusses intelligent agents and their environments. It defines an agent as anything that perceives its environment and acts upon it. Rational agents are those that select actions expected to maximize their performance based on perceptions. An agent's task environment consists of its performance measure, environment, actuators, and sensors. Environment types include fully/partially observable, deterministic/stochastic, and single/multi-agent. Four basic agent types are described: simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. Learning agents use feedback to improve performance over time. The document provides examples of agents and discusses their design considerations based on task environment properties.
There are five main types of AI agents:
1. Simple reflex agents take actions based solely on current percepts without considering history.
2. Model-based reflex agents maintain an internal state based on percept history to act in partially observable environments.
3. Goal-based agents consider goals to choose actions that achieve desirable situations.
4. Utility-based agents act to maximize success based on a utility function measuring how well goals are achieved.
5. Learning agents can improve their performance over time by learning from experiences through components like a learning element, critic, and problem generator.
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 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.
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
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.
This document provides an overview of different types of agents and their environments in artificial intelligence. It describes 6 types of agents: 1) Table-lookup agents that use a percept/action table. 2) Simple reflex agents that select actions based only on the current percept. 3) Model-based reflex agents that have internal state to track past world states. 4) Goal-based agents that consider future events to achieve goals using problem solving and search. 5) Utility-based agents that use a utility function to optimize between goals and likelihoods of success. 6) Learning agents that adapt and improve over time based on experiences. Each type increases in complexity and capabilities compared to the previous.
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 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.
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 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.
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.
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.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
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.
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 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
This document discusses intelligent agents and their environments. It defines an agent as anything that perceives its environment and acts upon it. Rational agents are those that select actions expected to maximize their performance based on perceptions. An agent's task environment consists of its performance measure, environment, actuators, and sensors. Environment types include fully/partially observable, deterministic/stochastic, and single/multi-agent. Four basic agent types are described: simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. Learning agents use feedback to improve performance over time. The document provides examples of agents and discusses their design considerations based on task environment properties.
There are five main types of AI agents:
1. Simple reflex agents take actions based solely on current percepts without considering history.
2. Model-based reflex agents maintain an internal state based on percept history to act in partially observable environments.
3. Goal-based agents consider goals to choose actions that achieve desirable situations.
4. Utility-based agents act to maximize success based on a utility function measuring how well goals are achieved.
5. Learning agents can improve their performance over time by learning from experiences through components like a learning element, critic, and problem generator.
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 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.
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
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.
This document provides an overview of different types of agents and their environments in artificial intelligence. It describes 6 types of agents: 1) Table-lookup agents that use a percept/action table. 2) Simple reflex agents that select actions based only on the current percept. 3) Model-based reflex agents that have internal state to track past world states. 4) Goal-based agents that consider future events to achieve goals using problem solving and search. 5) Utility-based agents that use a utility function to optimize between goals and likelihoods of success. 6) Learning agents that adapt and improve over time based on experiences. Each type increases in complexity and capabilities compared to the previous.
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 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.
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 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.
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.
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.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Assessment and Planning in Educational technology.pptxKavitha Krishnan
In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
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.
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.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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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
2. Today’s class
• What’s an agent?
– Definition of an agent
– Rationality and autonomy
– Types of agents
– Properties of environments
• Lisp
3. How do you design an intelligent agent?
• 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
4. What do you mean,
sensors/percepts and effectors/actions?
• 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
5. A more specific example:
Automated taxi driving system
• Percepts: Video, sonar, speedometer, odometer, engine sensors,
keyboard input, microphone, GPS, …
• Actions: Steer, accelerate, brake, horn, speak/display, …
• Goals: Maintain safety, reach destination, maximize profits (fuel, tire
wear), obey laws, provide passenger comfort, …
• Environment: Streets, freeways, traffic, pedestrians, weather,
customers, …
• Different aspects of driving may require
different types of agent programs!
6. Rationality
• An ideal rational agent should, for each possible percept
sequence, do whatever actions will maximize its expected
performance measure based on
(1) the percept sequence, and
(2) its built-in and acquired knowledge.
• Rationality includes information gathering, not “rational
ignorance.” (If you don’t know something, find out!)
• Rationality Need a performance measure to say how well a
task has been achieved.
• Types of performance measures: false alarm (false positive)
and false dismissal (false negative) rates, speed, resources
required, effect on environment, etc.
7. Autonomy
• A system is autonomous to the extent that its own
behavior is determined by its own experience.
• Therefore, a system is not autonomous if it is
guided by its designer according to a priori
decisions.
• To survive, agents must have:
–Enough built-in knowledge to survive.
–The ability to learn.
8. Some agent types
• (0) Table-driven agents
– use a percept sequence/action table in memory to find the next action. They
are implemented by a (large) lookup table.
• (1) Simple reflex agents
– are based on condition-action rules, implemented with an appropriate
production system. They are stateless devices which do not have memory of
past world states.
• (2) Agents with memory
– have internal state, which is used to keep track of past states of the world.
• (3) Agents with goals
– are agents that, in addition to state information, have goal information that
describes desirable situations. Agents of this kind take future events into
consideration.
• (4) Utility-based agents
– base their decisions on classic axiomatic utility theory in order to act
rationally.
10. (0) Table-driven agents
• Table lookup of percept-action pairs mapping from every
possible perceived state to the optimal action for that state
• Problems
– Too big to generate and to store (Chess has about 10120
states, for example)
– No knowledge of non-perceptual parts of the current
state
– Not adaptive to changes in the environment; requires
entire table to be updated if changes occur
– Looping: Can’t make actions conditional on previous
actions/states
11. (1) Simple reflex agents
• Rule-based reasoning to map from percepts to optimal
action; each rule handles a collection of perceived states
• Problems
– Still usually too big to generate and to store
– Still no knowledge of non-perceptual parts of state
– Still not adaptive to changes in the environment; requires
collection of rules to be updated if changes occur
– Still can’t make actions conditional on previous state
13. (2) Agents with memory
• Encode “internal state” of the world to remember the past as
contained in earlier percepts.
• Needed because sensors do not usually give the entire state
of the world at each input, so perception of the environment
is captured over time. “State” is used to encode different
"world states" that generate the same immediate percept.
• Requires ability to represent change in the world; one
possibility is to represent just the latest state, but then can’t
reason about hypothetical courses of action.
• Example: Rodney Brooks’s Subsumption Architecture.
14. (2) An example:
Brooks’s Subsumption Architecture
• Main idea: build complex, intelligent robots by
decomposing behaviors into a hierarchy of skills, each
completely defining a complete percept-action cycle for one
very specific task.
• Examples: avoiding contact, wandering, exploring,
recognizing doorways, etc.
• Each behavior is modeled by a finite-state machine with a
few states (though each state may correspond to a complex
function or module).
• Behaviors are loosely coupled, asynchronous interactions.
16. (3) Goal-based agents
• Choose actions so as to achieve a (given or computed) goal.
• A goal is a description of a desirable situation.
• Keeping track of the current state is often not enough
need to add goals to decide which situations are good
• Deliberative instead of reactive.
• May have to consider long sequences of possible actions
before deciding if goal is achieved – involves consideration
of the future, “what will happen if I do...?”
18. (4) Utility-based agents
• When there are multiple possible alternatives, how to decide
which one is best?
• A goal specifies a crude distinction between a happy and
unhappy state, but often need a more general performance
measure that describes “degree of happiness.”
• Utility function U: State Reals indicating a measure of
success or happiness when at a given state.
• Allows decisions comparing choice between conflicting
goals, and choice between likelihood of success and
importance of goal (if achievement is uncertain).
19. Properties of Environments
• Fully observable/Partially observable.
– If an agent’s sensors give it access to the complete state of the
environment needed to choose an action, the environment is fully
observable.
– Such environments are convenient, since the agent is freed from the
task of keeping track of the changes in the environment.
• Deterministic/Stochastic.
– An environment is deterministic if the next state of the environment is
completely determined by the current state of the environment and the
action of the agent; in a stochastic environment, there are multiple,
unpredictable outcomes
– In a fully observable, deterministic environment, the agent need not
deal with uncertainty.
20. Properties of Environments II
• Episodic/Sequential.
– An episodic environment means that subsequent episodes do not depend
on what actions occurred in previous episodes.
– In a sequential environment, the agent engages in a series of connected
episodes.
– Such environments do not require the agent to plan ahead.
• Static/Dynamic.
– A static environment does not change while the agent is thinking.
– The passage of time as an agent deliberates is irrelevant.
– The agent doesn’t need to observe the world during deliberation.
21. Properties of Environments III
• Discrete/Continuous.
– If the number of distinct percepts and actions is limited, the
environment is discrete, otherwise it is continuous.
• 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.
27. Characteristics of environments
Fully
observable?
Deterministic? Episodic? Static? Discrete? Single
agent?
Solitaire No Yes Yes Yes Yes Yes
Backgammon Yes No No Yes Yes No
Driving No No No No No No
Internet
shopping
No No No No Yes No
Medical
diagnosis
No No No No No Yes
→ Lots of real-world domains fall into the hardest case!
28. 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 percept sequence so far.
• An autonomous agent uses its own experience rather than built-in
knowledge of the environment by the designer.
• 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.