well I think we are seeing the most disruptive force in history here that is smarter than the smartest human you may have heard of Siri Alexa or Cortana the popular virtual assistance that can help you with tasks like setting reminders finding information and playing music but did you know that there's a new wave of AI assistant on the rise known as AI agents these AI agents go beyond the capabilities of virtual assistance able to make complex decisions engage in natural conversations and learn from experience in this video we'll explore what AI agents are how they
work and what they mean for the future of AI I believe that it will surprise us on the upside there the technology will surprise us with how much it can do we've got to find out and see but I'm very optimistic and I agree with you what a contribution would that be how many people here have used chat GPT or an AI think I don't think people realize that Alexa and Google home and apple home are about to use this for real for real what are AI agents AI agents are a wide range of entities
designed to interact with and navigate their surroundings whether they're software algorithms or physical machines these agents can sense and understand their environment using sensors and code this capability allows them to collect data which is then processed using algorithms or models this processing step is crucial because it turns raw sensory inputs into meaningful information that helps in decision-making the core of AI agents lies in their capacity to act upon this processed information to achieve specific goals this can involve various actions from simple responses Guided by predefined rules to complex Maneuvers based on intricate computations in their
most advanced forms AI agents possess autonomy and adaptability allowing them to learn and develop over time the versatility of AI agents is highlighted by their classification into different types based on their underlying architecture and behavior you probably must have seen tons of them on the internet but in the next few minutes I will be breaking down these explanations in the simplest form starting from the simple reflex agents simple reflex agents operate by reacting to the current perception without considering history these agents use a condition action rule given a condition or state they map it to
a specific action if the condition is recognized the corresponding action is executed otherwise it's not this type of agent Works effectively only in fully observable environments a typical example of a simple reflex agent is a simple spam email filter this filter operates on a straightforward principle it perceives incoming email messages and using predefined rules decides whether to classify an email as spam or not importantly it doesn't analyze historical data about the sender's Behavior or previous emails it responds only to the immediate perception of each email the rules guiding its actions are clear if specific keywords
commonly associated with Spam like buy now free discount appear in the email subject it marks the email as spam likewise if the sender's email address matches a known list of spammers it's marked as spam conversely emails from recognized reputable sources are classified as not spam this spam filter is categorized as a simple reflex agent because it makes decisions solely based on the current percept the content and sender of each incoming email without using past email history or demonstrating the ability to learn and adapt over time number two model-based reflex agents a model-based agent operates by
applying rules that correspond to its current context distinguishing itself through its ability to navigate environments with limited visibility this agent relies on an internal model of how the world operates continually adjusting this model based on incoming sensory data thus reflecting the cumulative history of these observations this agent has a framework that handles the parts of the environment it cannot directly see helping it deal with conditions where it can only see part of what's happening to effectively updated State the agent needs to grasp two main things how the environment changes on its own and how the
agent's actions affect the environment understanding these Dynamics helps the model-based agent make smart choices even when it doesn't have all the information for instance consider an AI agent controlling a character in a strategy game set in a vast procedurally generated world the agent's objective is to locate and acquire rare resources scattered across the landscape number three goal-based agents these agents are specifically crafted to efficiently achieve predefined objectives this approach fills the agent with the capability to assess different courses of action and choose the one most likely to result in reaching the desired goal State what
distinguishes these agents is their Adept decision-making prowess enabling them to meticulously plan and execute steps toward achieving their goals typically they employ techniques such as search algorithms and strategic planning to navigate toward their objectives notably the behavior of goal oriented agents can be easily adjusted to accommodate changing environments a prime example of a goal oriented agent is alphago a computer program designed to excel in the game of Go to win alphago achieves this by evaluating potential moves based on the current board State previous plays and the opponent's strategies it then calculates the likelihood of winning
or losing for each possible move selecting the move deem most likely to lead to Victory number four utility-based agents utility-based agents are designed to achieve specific outcomes it is engineered to optimize a particular utility which could be maximizing Financial gains or reducing energy usage unlike goal oriented agents utility-driven agents do not have a fixed objective but instead identify the best solution based on a predefined utility Criterion in situations where multiple Alternatives exist utility based agents determine the most favorable option based on their preference or utility for each state sometimes achieving the desired goal alone is
not enough we may prefer a faster safer or more costeffective route to a destination considering the agent's satisfaction utility quantifies the agent's happiness and due to the inherent uncertainty in the world utility-based agents choose actions that maximize expected utility a smart building controller is an AI agent that can make decisions on optimizing energy usage in a building based on various factors these factors include user preferences for Comfort cost and environmental impact as well as building characteristics and energy market prices the controller evaluates the cost and benefits of various actions such as heating or cooling lighting
and power management based on a predefined utility function it then chooses the optimal course of action to maximize the expected utility taking into account the risk and uncertainty associated with each decision number five learning agent a learning agent is like a student who gets better over time by learning from past experiences when it starts it has some basic knowledge already kind of like knowing the alphabet but as it encounters different situations and learns from them it gets smarter and can handle new things on its own it's like how you get better at a game the
more you play it the learning agent gets better at its tasks as it gathers more experience stock trading Bots are just a perfect example of learning agents these AI systems learn and adapt based on Market data and historical trading patterns they are programmed to constantly monitor the market and identify potential opportunities for trading they make decisions based on a combination of technical analysis fundamental analysis and their proprietary algorithms as they gain more experience and learn from their successes and failures these Bots become increasingly skilled at identifying profitable trades some Bots are even able to adjust
their trading strategies in real time based on New Market data number six multi-agent systems Mas a multi-agent system Mas consists of numerous agents collaborating towards a common goal these agents possess varying degrees of autonomy and can perceive their environment make decisions and and act toward achieving the collective goal multi-agent system has diverse applications including Transportation Systems Robotics and social networks aiming to improve efficiency reduce costs and enhance flexibility in complex systems the classification of multi-agent systems depends on factors such as shared or Divergent goals among agents their Cooperative or competitive nature and their homogeneity or
heterogeneity all agents exhibit similar cap capabilities goals and behaviors in a homogeneous multi-agent system contrastly a heterogeneous multi-agent system involves agents with diverse capabilities objectives and behaviors which can complicate coordination but enhance adaptability and system resilience Cooperative multi-agent systems involve agents collaborating to achieve a collective goal while a competitive multi-agent system entails agents striving independently toward individual objectives multi- agent system implementations often integrate both cooperative and competitive behaviors requiring agents to balance individual interests with group objectives it leverages techniques like Game Theory machine learning and agent-based modeling Game Theory analyzes strategic interactions and predicts agent
Behavior machine learning trains agents to improve decision-making over time agent-based modeling imitates complex systems and studies agent interactions effectively number seven hierarchical agent hierarchical agents are structured in a hierarchical arrangement with higher level agents supervising lower level agents the levels within this hierarchy can differ based on the complexity of the system these hierarchical agents are beneficial across diverse applications such as robotics manufacturing and transportation they Excel notably in tasks that demand the coordination and prioritization of multiple activities a perfect example of how this agent could work can can be found in the autonomous car control
system in this scenario the hierarchical agent would consist of multiple levels of control at the highest level there might be a strategic planner that decides the route and overall driving strategy based on inputs such as the destination and traffic conditions at a mid-level there could be a tactical controller that interprets the highlevel plan and makes decisions about Lane changing merging or overtaking based on real-time sensor data lastly at the lowest level there would be a low-level controller responsible for executing specific driving actions like accelerating braking and steering based on the instructions received from the Tactical
controller this hierarchical setup allows the autonomous car to efficiently navigate complex environments by integrating highlevel planning with low-level motor control ensuring safe and efficient driving the future of AI agents the future of AI agents is incredibly exciting as we've seen there are many different types of AI agents each with its unique capabilities in the future we can expect to see more advanced AI agents that combine the features of multiple types for example we might see AI agents that are both learning and goal-based able to learn from experience and use this knowledge to achieve goals or
we might see AI agents that are both utility-based and reactive able to make decisions based on their utility function while also responding to the environment this will open up new possibilities for AI to assist humans in a variety of ways from personal assistance to complex decision-making tools the sky is the limit when it comes to the potential of AI in the future if you have made it this far let us know what you think in the comments section below for more interesting topics make sure you watch the recommended video that you see on the screen
right now thanks for watching
