Chapter 2 Exercises

Below are the exercises from Chapter 2 I’ve chosen to work.

2.1) Suppose that the performance measure is concerned with just the first T time steps of the environment and ignores everything thereafter. Show that a rational agent’s action may depend not just on the state of the environment but also on the time step it has reached.

2.1) A rational agent’s actions vary in regards to the environment. An action may or may not affect the environment, but if it does then we need to update the environment. One action may also lead to different paths, so we need to know what the final result will be in regards to every action, not just up until T time steps. An agent can only know what to do based on what it knows, if it stops after T time steps then there is a chance it will have not reached its goal, unless the goal in question has been designed around T time steps.

2.2) Let us examine the rationality of various vacuum-cleaner agent functions.
A) Show that the simple vacuum-cleaner agent function described in Figure 2.3 is indeed rational under the assumptions listed on page 38.
B) Describe a rational agent function for the case in which each movement costs one point. Does the corresponding agent program require internal state?
C) Discuss possible agent designs for the cases in which clean squares can become dirty and the geography of the environment is unknown. Does it make sense for the agent to learn from its experience in these cases? If so, what should it learn? If not, why not?

2.2)
The assumptions on page 38 state that:
-The performance measure awards one point for each clean square at each time step, over a “lifetime” of 1000 time steps.
-The “geography” of the environment is known a priori (Figure 2.2) but the dirt distribution and the initial location of the agent are not. Clean squares stay clean and sucking cleans the current square. The Left and Right actions move the agent left and right except when this would take the agent outside the environment, in which case the agent remains where it is.
-The only available actions are Left , Right, and Suck.
-The agent correctly perceives its location and whether that location contains dirt.

A) The simple vacuum cleaner agent is indeed rational under these assumptions because it accounts for all variables. The map is known, there is a sensor for clean or dirty, what to do in that space, and the agent will never go out of bounds.

B) If each move costs one point then an internal state is required to keep track of starting points (if any) and the subtraction of those points. Unless of course the score starts at 0 and goes negative for each move. But if cleaning a space awards one point, then an optimal goal would be to try and get a score of 0 or higher.

C) If clean spaces can become dirty again then obviously a vacuum should clean it again. But the vacuum should bot be constantly running, this would waste electricity. The vacuum should map its surroundings and objects so it knows that the environment looks like for each additional time. By learning how large the area is it can determine how many times a day it must run to keep every space clean. Upon starting up it should assume every space is dirty and start a path around to clean them all, making sure to get every space. The more the vacuum does this the more it will know its surroundings and how often to clean.

2.3) For each of the following assertions, say whether it is true or false and support your answer with examples or counterexamples where appropriate.
A) An agent that senses only partial information about the state cannot be perfectly rational.
B) There exist task environments in which no pure reflex agent can behave rationally.
C) There exists a task environment in which every agent is rational.
D) The input to an agent program is the same as the input to the agent function.
E) Every agent function is implementable by some program/machine combination.
F) Suppose an agent selects its action uniformly at random from the set of possible actions. There exists a deterministic task environment in which this agent is rational.
G) It is possible for a given agent to be perfectly rational in two distinct task environments.
H) Every agent is rational in an unobservable environment.
I) A perfectly rational poker-playing agent never loses.

2.3)

2.4) For each of the following activities, give a PEAS description of the task environment and characterize it in terms of the properties listed in Section 2.3.2.
A) Playing soccer.
B) Exploring the subsurface oceans of Titan.
C) Shopping for used AI books on the Internet.
D) Playing a tennis match.
E) Practicing tennis against a wall.
F) Performing a high jump.
G) Knitting a sweater.
H) Bidding on an item at an auction.

2.4)

2.5) Define in your own words the following terms: agent, agent function, agent program, rationality, autonomy, reflex agent, model-based agent, goal-based agent, utility-based agent, learning agent.

2.5)
Agent- A system with at least some form of intelligence.
Agent Function- What at agent is suppose to do, its purpose.
Agent Program- An internal absolute implementation of code.
Rationality- What the agent knows about the environment and a self judgement on how it performed.
Autonomy- The ability to act on its own. Knowing where it is, what it has to do, etc.
Reflex Agent- Responding to percepts in the environment.
Model Based Agent- Has knowledge of the workings of the world.
Goal Based Agent- Has knowledge of the goal and decides what actions to take in order to reach it.
Utility Based Agent- Determines the best way to reach the goal.
Learning Agent- Analyzes information to make improvements.

2.6) This exercise explores the differences between agent functions and agent programs.
A) Can there be more than one agent program that implements a given agent function? Give an example, or show why one is not possible.
B) Are there agent functions that cannot be implemented by any agent program?
C) Given a fixed machine architecture, does each agent program implement exactly one agent function?
D) Given an architecture with n bits of storage, how many different possible agent programs are there?
E) Suppose we keep the agent program fixed but speed up the machine by a factor of two. Does that change the agent function?

2.6)
A) Yes, there can be more than one agent program implementing an agent function. As stated above, the function is the purpose and the program is the code for its implementation. If a function has multiple options then there must be more than one program.
B) There exist agent functions that cannot be implemented by any agent programs. For example, if an agent function was to count to find the square root of a negative number. There is no way to solve that.
C) Yes, each agent program will implement exactly one agent function. For example, a precept has multiple reactions, but each reaction is different according the situation it’s in.
D) There are 2^n possible agent programs.
E) Speeding up the machine does not change the agent function because the environment is static.

2.7) Write pseudocode agent programs for the goal-based and utility-based agents.

2.7)
Goal-Based Pseudocode

set tickets_unsold to 50
set tickets_sold to 0

sell tickets for show
decrease tickets_unsold for each ticket_sold
increase tickets_sold for every decrease in tickets_unsold

stop when tickets_unsold equals zero

Utility-Based Pseudocode

set starting_location to (0,0)
set ending_location to (50,50)
create a fifty by fifty grid (even numbers are rows, odd numbers are columns)
set time to 0

randomly generate two numbers between 1 and 25 (the first is rows and the second is columns)
this determines how many roads contain traffic which increases time to ending_location

a normal block takes 1 minute to travel
a traffic block takes 5 minutes

start at (0,0) and arrive at (50,50) is the shortest amount of time

compute which path is the shortest and has the least amount of traffic

2.8) Implement a performance-measuring environment simulator for the vacuum-cleaner world depicted in Figure 2.2 and specified on page 38. Your implementation should be modular so that the sensors, actuators, and environment characteristics (size, shape, dirt placement, etc.) can be changed easily. (Note: for some choices of programming language and operating system there are already implementations in the online code repository.)

2.8)

2.9) Implement a simple reflex agent for the vacuum environment in Exercise 2.8. Run the environment with this agent for all possible initial dirt configurations and agent locations. Record the performance score for each configuration and the overall average score.

2.9)

2.10) Consider a modified version of the vacuum environment in Exercise 2.8, in which the agent is penalized one point for each movement.
A) Can a simple reflex agent be perfectly rational for this environment? Explain.
B) What about a reflex agent with state? Design such an agent.
C) How do your answers to a and b change if the agent’s percepts give it the clean/dirty status of every square in the environment?

2.10)
A) A simple reflex agent cannot be perfectly rational in this environment because the agent never stops and its score will continue downward. It also has no idea whether there are even any unclean spaces before moving.
B) A reflex agent with a state is possible, as long as it keep track of the environment, otherwise it will keep moving from space to space. But the reflex agent performs the same action in similar situations, so entering a dirty space and moving is fine, but after moving from a clean space it will continue to move forever. So as long as the agent has memory of squares and the environment it is possible to work. There needs to be a line of code that states “after all squares are clean, stop).
C) If the agent knows whether a square is dirty or clean it has the option to take no action which prevents the score from decreasing. The agent should only clean dirty squares and if it has to travel to a dirty space, it should take the shortest route.

Chapter 1 Exercises

I’ve chosen the following problems from Chapter 1 to work on.

1.1)
Define in your own words:
(A) Intelligence
(B) Artificial intelligence
(C) Agent
(D) Rationality
(E) Logical reasoning

1.1)
A) Intelligence is the the ability to solve problems, no matter how miniscule or extraordinary they are. Not only should we be able to solve these problems, but also find improvements to our solutions and continue to expand our knowledge. Being intelligent is one thing, but being able to expand our Intelligence is far more valuable.

B) Artificial Intelligence is a piece of machinery, programming, code, etc that was built by humans with the specific task to solve a problem or many problems. But as I’ve stated before, solving problems is one thing, expanding that problem solving knowledge is another. Artificial Intelligence should keep records of its attempts to solve a problem so that it can learn from it’s progress and its mistakes.

C) Agents are things that perform actions. They are given instructions and are expected to follow them; but there’s more to it than just following those actions. Agents must also be able to operate on their own, change their actions depending on their surroundings, maintain themselves over an elongated period of time, and develop their own goals to pursue. 

D) Rationality is doing what is expected of you or what is acceptable to do. When a problem arises there are specific ways that one would solve it; these are filed under Rationality. For example: Your car runs out of gas while driving on the highway and you pull over to solve the problem. A rational action would be to call AAA or a tow service. An irrational action would be to try an siphon gas from a nearby parked police cruiser.

E) Logical Reasoning is the ability to perceive a problem logically and know which solution is best to solve it. 

1.3) Are reflex actions (such as flinching from a hot stove) rational? Are they intelligent?

1.3)
Reflex actions are second nature to humans, our brains try and keep us healthy so we live longer. This includes minimizing damage to our bodies. And even though we flinch without thinking, I do believe that those actions are rational because it’s beneficial to minimize the amount of time being burned. However, I do not believe flinching to be intelligent, being that it’s second nature and people do it without thinking. Intelligence requires at least some amount of thought.

1.4) Suppose we extend Evans’ ANALOGY program so that it can score 200 on a standard IQ test. Would we then have a program more intelligent than a human? Explain.

1.4) If we have a program that can score a 200 on a standard IQ test it would have the same intelligence as a human. I looked up the scores for an IQ test and the max score is 140 and over. It is possible for a human to score a 200, but it is very unlikely. The same can be said for the program, it can score a 200 on the IQ test, just as the human can. If we had a program that can always score a 200 then I believe it would be more intelligent than a human.

1.6) How could introspection—reporting on one’s inner thoughts—be inaccurate? Could I be wrong about what I’m thinking? Discuss.

1.6) Introspection can be inaccurate in many ways. One of the main reasons is that everyone thinks in different ways, so the way a program perceives one person;s thoughts may be different from the way it perceives another person’s thought, or even the same person just at a different point in time. The way we think changes over time so it’s hard to keep an accurate report on our thoughts.

1.7) To what extent are the following computer systems instances of artificial intelligence:
• Supermarket bar code scanners.
• Web search engines.
• Voice-activated telephone menus.
• Internet routing algorithms that respond dynamically to the state of the network.

1.7) The following items contain at least some artificial intelligence.

Supermarket Bar Code Scanners are tied into a database with records for each item in a specific store. The scanner itself just reads the code and fins the corresponding item in the database and comes up with the correct price. The scanner itself has to be able to recognize that there is a bar code present, scan it, and display the information to the cashier and the customer.

Web Search Engines are connected to the every piece of information stored on the internet. Think of it like an all knowing entity. You can type anything in and if it exists on the web, it will find it for you. The intelligence required isn’t that high though. It seems like a daunting task for a human, but a piece of code that searches the web for any text, image, file, etc containing the corresponding text is fairly simple. It’s just matching what you typed to what’s out there, but on a much larger scale. There are options when using search engines, but those only refine the search; such as excluding items from a certain date, only retrieving peer reviewed articles, or only finding images that contain pictures of Iron Man.

Voice-Activated Telephone Menus are in a way, similar to Web Search Engines. The phone is programmed to hear a voice, listen for key words, and perform an action based on those words. For example, I take out my phone and say “Galaxy, call home.” My phone picks up that I’m addressing it by saying Galaxy, it knows to open the phone app when I say call, and it knows which contact to call when I saw home. If I had multiple numbers for home it would ask me to clarify which number I want to call.

Internet routing algorithms that respond dynamically to the state of the network respond on their own to what’s happening in their environment. If there is too much traffic, it can decided whether or not to open up more space. Internet Routing Algorithms know what ports are accessible and which ones are not. Yes it is programmed to do so but it reacts on its own. There’s no one scanning a box of cereal, searching the web for Iron Man, or someone telling there phone to call home.

1.9) Why would evolution tend to result in systems that act rationally? What goals are such systems designed to achieve?

1.9) Acting rationally is beneficial to everyone. Evolution is survival of the fittest, only the strong (and intelligent) survive. Humans (for the most part) tend to act rationally, and they designed their systems to do the same. We want proper results as quickly as possible. Hunt in a pack not alone, solve this Rubik Cube by not repeating any moves. Though the goals may be different, the way we solve them is the same, in the most rational way.

If there is a solution to a problem then it is most likely rational. If there are multiple solutions to a problem then some may be more rational than others.

1.10) Is AI a science, or is it engineering? Or neither or both? Explain.

1.10) Artificial Intelligence is most certainly a science. But it would be nothing with engineering. Computer Scientists need somewhere to place their programs, such as computers, servers, robots, cars, etc. But without engineers they would have no outlet to test their Artificial Intelligence on.  Science and Engineering go hand in hand, they both benefit each other. While the engineers build the machines, the scientists are writing code for their AI.

1.11) “Surely computers cannot be intelligent—they can do only what their programmers tell them.” Is the latter statement true, and does it imply the former?

1.11) The latter statement is true, in a sense. Yes computers only do what they’re told, but they also learn from what they do. After a certain period of time they know what works and what doesn’t. You can look at two children the same way. Child A is brought up in a good home, enrolled in school, taught right from wrong. Child B is raised in a less than favorable neighborhood, not enrolled in school, and does not have much parental guidance. Child A has a completely different view of the world from Child B. Children can only do what their parents (programmers) tell them to do. But does that mean Child B will never succeed in anything or that Child A will always do great things? The answer is no, There are outside factors that affect children (programs). The program performs its tasks and takes in knowledge and learns as it goes.

1.12) “Surely animals cannot be intelligent—they can do only what their genes tell them.” Is the latter statement true, and does it imply the former?

1.12) The latter statement is blatantly false. Mostly because I don’t believe we’ve even begun to really understand animals. Yes we can watch them, study their behavior and habits, but that doesn’t mean we know why they do the things they do. Yes, there are certain animals that are smarter than others. For example, a dolphin is smarter than a sloth. Dolphins can learn commands and remember people they interact with frequently, while sloths sometimes mistake their arms for a branch and fall out of trees. Just as intelligence varies from person to person, it varies from animal to animal, not only between different species, but also within. There could be two dolphins of the exact same species and one is more intelligent than the other.

1.13) “Surely animals, humans, and computers cannot be intelligent—they can do only what their constituent atoms are told to do by the laws of physics.” Is the latter statement true, and does it imply the former?

1.13) The latter statement is again false. If this were true then humans would have never invented anything, most of all the airplane. The very first humans had nothing, they were on equal ground with most animals. But over time they developed tools, medicine, shelter, transportation, weapons, etc. I want to specifically focus on the invention of the airplane. By the laws of physics, humans cannot fly. But the Wright Brothers tirelessly worked on a machine that would allow humans to soar in the sky like birds. And now, over 100 years later, a human can fly around the world mere hours. Now this is quite a feat, but animals have accomplished an even more impressive feat. They have survived for as long, if not longer than humans with only evolution, They have not built mighty sky scrapers, airplanes, computers, anything. They simply survive by using their natural instincts, which to me is very impressive.

1.14) Examine the AI literature to discover whether the following tasks can currently be solved by computers:
A) Playing a decent game of table tennis (Ping-Pong).
B) Driving in the center of Cairo, Egypt.
C) Driving in Victorville, California.
D) Buying a week’s worth of groceries at the market.
E) Buying a week’s worth of groceries on the Web.
F) Playing a decent game of bridge at a competitive level.
G) Discovering and proving new mathematical theorems.
H) Writing an intentionally funny story.
I) Giving competent legal advice in a specialized area of law.
J) Translating spoken English into spoken Swedish in real time.
K)Performing a complex surgical operation.

For the currently infeasible tasks, try to find out what the difficulties are and predict when, if ever, they will be overcome.

1.14)
A) Playing a decent game of table tennis (Ping-Pong) – This is solvable by computers. A robot arm can be fitted with a ping pong paddle and motion sensors to move to wherever the ball is.

B) Driving in the center of Cairo, Egypt – This is solvable by computers. Google is working on a self-driving car. It requires a ton of motion sensors in order to respond to its surroundings. One issue would be refilling the gas tank.

C) Driving in Victorville, California -This is solvable by computers. Google is working on a self-driving car. It requires a ton of motion sensors in order to respond to its surroundings. One issue would be refilling the gas tank.

D) Buying a week’s worth of groceries at the market – This would be a challenge for computers right now. The computer would have to know what it (or you) wants, it would have to be able to identify foods without bar codes such as apples.

E) Buying a week’s worth of groceries on the Web – This is solvable by computers. All you need to do is tell your computer what you want, what size (if any), how many, etc. You would also need your payment information accessible to your computer so it can complete the transaction for you. Aside from picking up your groceries, everything is done for you.

F) Playing a decent game of bridge at a competitive level – This is solvable by computers. There are already computers that can play chess at a competitive level, and since bridge isn’t as complicated as chess it shouldn’t be too hard for a computer.

G) Discovering and proving new mathematical theorems – This would be a challenge for computers right now. Computers can solve mathematical theorems, but discovering them is a whole different story. A computer would have to be self aware to discover anything.

H) Writing an intentionally funny story -This would be a challenge for computers right now. Computers don’t know the concept of comedy. You can input jokes into a computer, but it won’t know how to write a funny story with new material.

I) Giving competent legal advice in a specialized area of law – This would be a challenge for computers right now. They would need to know every aspect of the case and that specific area of the law. It’s easier for a computer to give quantitative advice than qualitative advice.

J) Translating spoken English into spoken Swedish in real time – This is solvable by computers.

K)Performing a complex surgical operation – This would be a challenge for computers right now.