Environment Types In Artificial Intelligence – It’s 4:00 PM and the day before Thanksgiving. You’re parked on Wall Street, waiting for the next rider to arrive. You watch the roads become crowded – pedestrians, cars, buses, and everyone – as everyone runs to wherever they need to be, as quickly as possible.
You personally want to beat the traffic before it’s too late, but there is time for this last trip.
Environment Types In Artificial Intelligence
Soon, someone will knock on your car door, and you make sure they look close to their profile photo on the app. Say you are going to the airport. You let him in.
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For some reason, you have a bad feeling about this. “What time is your flight?” he asks carefully.
We have all been in a situation where the deadline was fast approaching, and we had to choose between the bad and the bad. Or in other, technical terms, we had to expand our application within the given constraints.
, the most obvious. Simply put, since the passenger’s flight takes an hour, it would be better if we arrive before that time.
How can we solve this problem? Perhaps we could drive faster to save time, using the overpass whenever possible. But this can be dangerous and we plan to stay within the speed limits. Maybe we can find a shortcut using GPS. But even with Google Maps and Waze, the shortest route isn’t always the fastest; therefore we have degrees of uncertainty.
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So where do we draw the line between maximizing profits (say, depending on the length and duration of the trip) while improving customer satisfaction (so we can rely on five star ratings)?
It may seem like we are overcomplicating the problem, but we as humans face decisions like these every day. Usually a good place to start is at
Likewise, designing the ideal artificial intelligence must take into account the conditions that may affect its journey from the initial location to the target location. We call this process work.
A job, on the other hand, has specific definitions and properties. Stuart Russell and Peter Norvig, in their seminal book on artificial intelligence, call this the PEAS definition (Performance, Environment, Actuators, Sensors).
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We have achieved the given goal. In the story above, we want to complete the trip, be on time, drive legally, stay safe, make a profit, and get good points. All of these factors measure how well we’ve done our job from Wall Street to Newark.
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Of an external event that may affect our performance in achieving the target level. For example, while driving to the airport, we need to be aware of other vehicles on the road, pedestrians at intersections, different roads and traffic signals, etc. We are not alone in the world, and what others do can affect us; and what we do can affect them.
On the environment. For example, a car has a horn to warn others, blinkers to inform about turning, a steering wheel to turn, and pedals to control speed.
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Environmental indicators. This could be the proximity sensor that beeps when we are too close to a car or other entity, the lane departure sensor that prevents us from drifting, and our navigation display that allows us to see car information (like oil temperature, next maintenance, what radio. are we there, etc.).
The biggest group? Well, as far as the outside world is concerned, there are many things that can happen, and many things that we have to plan for.
Information that we can take from a certain time. For example, while driving in the city, we can see the cars in front of us, behind us, and on both sides (minus the blind spots, of course). We can also measure how fast others are driving and whether or not they are willing to change lanes, using the speed limit and flashing signals, respectively. However, we may not see what is happening two miles ahead and several blocks to the right.
But perhaps we do not need to know this information, since it is not related to our immediate environment. However, if we are actually driving two miles ahead and a few blocks to the right, we may be interested in information about traffic jams, road closures , and so on.
Artificial Intelligence — Agents And Environments
If we have all the information we need to know, then our environment is fully visible. This type of environment is beneficial for artificial intelligence as it does not need to keep track of any foreign species; it only takes external signals for reflexive action. On the other hand, access to some, but not all, relevant information at any given time creates an observable environment. In this case, artificial intelligence may need to keep track of history to predict future decisions.
An agent is any autonomous entity (person, computer program, robot, animal, self-driving car, etc.) that can perceive its environment (through sensors) and produce a response (through actuators ). This answer does not need to be correct either
One agent can be taken alone. For example, if you play solitaire, there is no second player to compete with, while if you sit down at the poker table, others have already contributed to the outcome of the game.
Poker in particular is a highly competitive field. Everyone at the table wants to maximize their utility, or “win”, at the expense of others.
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To some extent, yes, but not completely; there is a contract (your driver’s license) that tells you to avoid collisions, follow traffic laws, blink and drive within speed limits to help keep everyone on the road safe, including and you. Violation of these rules serves the competition, and can increase temporary use (for example, cutting in front of the exit pile to save some time), but at the risk of “getting a ticket” or some kind of punishment. Therefore, the paths are somewhat competitive, but also somewhat cooperative.
Now let’s think about driving in a general way: can the behaviorist be considered a single-agent environment rather than a multi-agent one?
Yes, it is possible. If, on the perfect road, we assume that people are accustomed to driving safely, then all other vehicles can be viewed as things that we must avoid. This is like saying that our car is the only one on the road, and we need to go through moving obstacles to get to our destination.
In a fully cooperative environment of many agents, this is a reasonable assumption, and we can treat it as a single agent. But introduce competition and it fails to catch on.
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It happens. The laws of physics provide an example of this: all other things being equal, if you were to drop an apple, it would fall to the center of the earth. This is just gravity at work.
In the case of an AI chess agent, we can imagine it in the first stage with no moves made. If our agent has the first move and decides to push the pawn forward, yes
On the other hand, if the agent decides to move the pawn forward, and if it sometimes ends up one space ahead, sometimes two spaces ahead, and sometimes ling and even where it started, we now have a certain degree of uncertainty as to what will happen. This randomness is opposed to a stochastic, or non-selective, environment.
For example, with a chess player, each new move depends on what happened before. Or, in other words, different actions can have different results. Using your queen to take your opponent’s pawn can bring short-term help, but it can also put your queen at risk in the next move. This is a sequential environment.
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An episodic environment, on the other hand, is where each state is independent of the other. If a police officer with a radar gun is checking a one-way road for speeding drivers, the speed of the previous car, all other things being equal, has nothing to do with the speed of the next one. As if every car that passes in front of the policeman is separated,
The difference between a chain of events and a sequence is conditional probability, where the probability of separate events depends on each other, given or not.
A stable environment is one that does not change when the agent makes a decision. For example, in a game of chess, your opponent cannot make a move while it is your turn. It’s as if the passage of time doesn’t matter: a move made in five seconds isn’t necessarily better than one made in five minutes (assuming we’re playing chess without a clock, of course).
The dynamic environment, on the other hand, changes when the agent plans. While we are driving, all the other cars around us are also moving; some change directions, some speed up, some slow down, and so on. Every decision we make on the road can be affected by the sudden movement of other vehicles.
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Note the similarities between a dynamic environment and a multi-agent environment. The key difference is
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