Intelligent Agents
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Video Script
Differentiation here between strong and weak AI starts to lead to the idea of intelligent agents. Intelligent agents are entities that can perceive things about its environment through sensors and act upon that environment with effectors, hopefully in a way that can be perceived by the actual agent. And so what do you think are some examples of these intelligent agents that you’ve seen in your lives recently? I know for me, I have things like Amazon Alexa or smartwatches. Basically, anything that has a sensor and acts upon its own and its environment is going to be in some form of intelligent agent. Now a lot of these are going to be categorized as weak AI, right? So it’s very good at one thing. A simple example is a thermostat, or even a Roomba. Roombas are really good at vacuuming your floor, but not so great at opening your door for you, or cooking your food or telling you what temperature it is in your house.
Each robotic agent or intelligent agent is going to have four primary functions. The ability to perceive things from its environment. So what it can sense, although granted, an agent isn’t going to be able to sense everything about its environment. Sensors are really what it can understand. So what it can sense, what it can understand, right, or what it can understand is only what it senses it knows about. Actions- so what it can do on its environment. And really, an agent is only going to be able to know a subset of all possible actions, it’s not going to be able to know all the things that are possible, right? Depending on the kind of AI and use of that particular agent. Some of those actions or environments become extremely complex, even in the game, something like checkers. A simple game overall, but it becomes extremely complex if you’re trying to go through all possible board configurations. The last major function of an agent are its effectors. So how it can do things. Really, and sometimes it may not be capable of doing enough in its environment, but these are the things that it’s actually able to do in its environment, or how it can do those things in its environment.
Now, this also brings us into discussion of how an AI should act, right? A weak AI, strong AI, whatever you’re trying to talk about here. But this brings us into a discussion on how an agent or an AI agent should be able to act. Now a rational agent, which is what we perceive as intelligent is concerned with doing the right thing, given what it believes, from what it perceives. Given the information that it can gather from its environment, and all of the information that previously knows, it should be concerned about doing the right thing with that information, the act upon that information. But as you can guess it may not know everything, but it will try to do the best it can. Now, we can also measure the utility of an agent by measuring its maximum success. So how good is that? That is what is the right thing? And how do we figure out how well it did that thing? So to understand the effectiveness of artificial agents or intelligence, we have to ask ourselves questions about our beliefs, the uncertainty of knowledge, and how its represented and how we can reason and learn from it. And by studying our own rational behaviors, that is why we as humans do the things that we do, and we can hopefully better understand what goes on into the decision making process and how we can build better rational agents. But in that sense as well, right? If we’re trying to build perfect AI, our human bias gets into the AI that we build. And so one AI, this is why different robotic vacuums behave in sometimes entirely differently, because different humans are building different models of vacuums, or thermostats, or AAA whatever kind of AI you’re looking at. And that is based off of that particular person’s experiences, research and knowledge. And that bias is going to lead into different kinds of utility, different measurements of success, different actions, and different rational behavior.
A couple of very simple examples here of categories of agents that we may see in weak AI, something like a simple reflex agent, which just simply does an action based off of the things that collects or senses from its environment. This is something as simple as a automatic door so you walk into Walmart, and the door opens for you. Right, awesome magic door that opens for you. There is no one that has to open the door for you. But that is a basic AI right? It is something that can perceive from its environment and act upon it. Now it doesn’t really know what its action does, AAA It does know that the door is open, but it doesn’t know what that actually means for the environment. It doesn’t know that it’s letting humans into the room, and it doesn’t know that it’s, you know, letting energy out, for example, as a basic automatic door. And we could do something a little bit more advanced like a learning agent, which is going to be able to learn from its actions and do better the next time, some form of critic or utility is going to be involved here. Something like a smart thermostat in your home. So a nest thermostat or eco be whatever smart thermostat you may be aware of, but those smart thermostats are going to try to learn your behaviors- when are you out of your house?, the temperature of the environment outside of your house, how comfortable you like to have your house, whether like it colder or warmer, what room you’re in, in the house. So it can make that room more comfortable than the rest of the house. So you save on energy. And those sorts of things are going to start learning your behavior or other types of behavior so it can better its particular results, right? It’s so a lot of these AI are basing their decision based off of the utility. The utility, the measurement of success, that it’s actually been programmed to to look at.