- June 2022
- May 2022
- December 2021
- November 2021
- October 2021
- September 2021
- August 2021
- July 2021
- May 2021
- April 2021
- March 2021
- February 2021
- January 2021
- December 2020
- November 2020
- October 2020
- September 2020
- August 2020
- March 2020
- February 2020
- January 2020
- December 2019
- November 2019
- October 2019
- September 2019
- August 2019
- July 2019
- June 2019
- May 2019
- April 2019
- March 2019
- February 2019
- January 2019
- December 2018
- November 2018
- October 2018
- September 2018
- August 2018
- July 2018
- June 2018
- May 2018
- April 2018
- March 2018
- February 2018
- January 2018
- December 2017
- November 2017
- October 2017
- September 2017
- August 2017
- July 2017
- June 2017
- May 2017
- April 2017
- March 2017
- February 2017
- January 2017
- December 2016
- November 2016
- October 2016
- September 2016
- August 2016
- July 2016
- June 2016
- May 2016
- April 2016
- March 2016
- February 2016
- January 2016
- December 2015
- November 2015
- October 2015
- September 2015
- August 2015
- July 2015
- June 2015
- May 2015
- April 2015
- March 2015
- February 2015
- January 2015
- December 2014
- November 2014
- October 2014
- September 2014
- August 2014
- July 2014
- June 2014
- Algebra and Number Theory
- Appendix- Algebra and Number Theory
- Appendix- Automata and Language Theory
- Appendix- Games and Puzzles
- Appendix- Mathematical Programming
- Appendix- Network Design
- Appendix- Program Optimization
- Appendix- Sets and Partitions
- Appendix-Graph Theory
- Appendix: Sequencing and Scheduling
- Appendix: Storage and Retrieval
- Chapter 3 Exercises
- Core Problems
- Problems not in appendix
Tag Archives: 3-sat
The other major reduction my student, Dan Thornton, was working on was a reduction for the old NES Pokemon game.
This is Dan’s last reduction for his independent study class. He’s going to be applying to grad schools this year- if you’re running a grad program, you should accept him!
(Over to Dan)
Here we prove that a generalized version of the classic Pokemon Red/Blue video game is NP-complete. Generalized Pokemon(hereafter referred to as ) asks the following question: Given a party of Pokemon and a map of trainers, can you get from the start position to the end position without having all of your Pokemon defeated in battle and continue your journey of catching them all?
This is based off of a paper by Aloupis, Demaine, Guo, and Viglietta on classical Nintendo games.
The goal of any Pokemon game is progress through the world collecting all Pokemon to become the very best Pokemon trainer there ever was. For more background see this link. We will focus on a subproblem where there is a path filled with enemy trainers from a start point to some end point. Solving and determining if we can get to the finish from the start turns out to be NP-complete.
In our construction of , there are two types of enemy trainers we will encounter, Hard trainers which we will always lose against, and Easy trainers which we will always win against. The details of this construction will be described below.
Here we reduce from 3 SAT by building certain constructs or gadgets in that can be used to model clauses, variables, variable assignment, and satisfiability. Then inductively using these component pieces we may build an instance of that is logically equivalent to one from . Below is the framework that we will map components of on to.
In the above figure all solid lines are single use paths, all dashed lines have no traversal limit.
The idea is that our trainer starts at the Start location and proceeds to a variable gadget, they then have an exclusive choice between two paths. One path reflects the assignment of true, the other of false. This choice will take us to a series of paths that will let us proceed to each clause the variable assignment satisfies and unlock it. Then we move on to the next variable. Eventually after proceeding through all of the variable clauses, we arrive at the Check in phase. Here we try to pass through all of the clauses, only the clauses we previously satisfied with the correct variable assignment may be passed through. Now if all clauses are satisfied, then we are able to pass through every one and arrive at the Finish state.
As an example in the above figure the colored edges give use a path that will satisfy all the clauses and pass to the Finish. The red edges show us assigning the value of true to then proceeding to the first and second clause which are satisfied by this assignment of . The blue edges show an assignment for and the green one for . It is important to note that we are unable to move backwards except on dashed edges, this prevents us from going back and attempting to correct our variable assignment.
To show equivalence we break any instance up into to following: . Here each clause is of the form:
and each is a literal of the form .
The construction of the GP instance is built out of several component pieces, each described below.
Our trainer has a single Pokemon, a Ghastly that knows only a single move– Self-Destruct, a move that when used causes the user to faint but deals massive damage to a single enemy Pokemon. If it is ever our trainer’s turn during a battle we are forced to use a move.
By the above, if we ever use a move, we lose due to us only having a single suicidal move and a single Pokemon. When we lose we are unable to move and will be unable to get to the Finish.
Hard trainers have two Snorelaxs, both of which are slower than our Ghastly, so when we go into battle against them we are forced to use self-destruct and lose. Even if we cause a single of the opponent’s Snorelax to faint we still lose the battle as they have another.
Easy trainers all have a single Electrode that only knows self-destruct and has more speed than our Ghastly, so they will always go first and destroy themselves. Now normally self destruct would also deal massive damage to our Ghastly but, because Ghastly is a ghost type Pokemon, and self-destruct is a normal type move, it will not effect our Ghastly. Thus the opposing trainer will run out of Pokemon before us, and we win.
So, we have a situation where we may encounter any number of weak trainers and win, but a battle with a strong trainer guarantees a loss.
Field of Vision
Enemy trainers have a “field of vision” of some fixed length. In the above Figure, Player A’s field of vision is given by all blue squares. Once a player enters a trainer’s field of view they will be unable to move, the enemy trainer who can see them will approach and the trainer will be forced to battle. Other enemy trainers can block a trainer’s field of view, for example the striped blue box above is no longer in trainer A’s field of view due to trainer B.
Alternatively, we may choose to force an enemy trainer to battle our trainer if we stand in any of the orange squares.
Finally, once an enemy trainer has been defeated they will remain on the square they were defeated on indefinitely, so if we challenged and beat trainer A with our trainer standing on any of the orange squares, then trainer A would stay on the exact square he is on even if our trainer moved into his field of vision (any of the solid blue squares).
To distinguish the two types of trainers hard trainers will have a blue field of vision, whereas easy trainers will have a red one.
Variable assignment may be modeled using the construction in Figure 3. Here the player enters through a. The player may either choose to move to battle the enemy trainer while standing on square x or y. If our trainer battles the enemy trainer on square x then the enemy trainer will block off the c exit, and once defeated will stay there indefinitely. Alternatively if our trainer battles on square y then the defeated enemy trainer will remain where he is indefinitely and we must exit through c. So this construction therefore presents the player with an exclusive choice between two possible paths b or c.
Single Use path
In order to prevent a player from re-tracing his or her steps to access previous paths we may use the construction below. Here if we assume a player enters from a and will exit through b.
Notice that to pass from a to b our player must pass through both squares marked x and y. Notice that once we pass through y trainer C will have moved forwards to battle our trainer at y. This now means that trainer B has line of sight to x. Now because we must go through x square to get from a to b or b to a any passage through this gadget will be impossible.
Also notice that if we enter from b then we must pass through y before x and so by the same argument as above this is impossible.
Trainer A simply serves as a barrier from letting us battle trainer C from an adjacent square.
So this gadget is a single use unidirectional path.
Below we illustrate a clause, each of the weak trainers is a literal.
If the player at any time enters through any of the three literal paths from above, then they may battle a weak trainer. If a player battles any of the weak trainers then later on when the clause gadget is entered through check in then the weak trainer the player already battled will not approach the player, and will instead continue to block hard trainer B’s field of vision so that we may pass through to check out. Hard trainer A is there just to prevent re-entering the literal clauses after entering this gadget through check in.
The crossover gadget shown below is present to deal with the issue of planarity, as there is no device in the original Pokemon games for passing “over” other portions. Then if we view the paths between gadgets as edges of a graph similar to our framework in the first figure, then without this gadget we are restricted to building planar graphs.
This gadget lets two paths cross without letting a player switch the path they are on. So if you enter from a player is forced to leave through and similarly for and . Each path is single use, but using the ‘s path, has no effect on our use of the ‘s path and vice versa. As a result we may assume we can build a single use path between any two points.
Here we reduce from an instance of . We are given a formula with the form defined in section 4. Now we build into an instance of using the reduction framework in Figure 1 as well as our components. We also impose the restriction that in every variable gadget path corresponds to assigning a literal the value and corresponds to .
If we have a variable assignment that satisfies then by definition there is a literal in every clause such that satisfies . Then in our constructed instance of we must be able to visit the literal section of every clause gadget at least once prior to arriving at the check-in phase. So it follows that we are able to battle at least 1 of the easy trainers in each clause gadget before check-in. Therefore it must be possible for us to reach the finish.
If it is possible for our trainer to get from start to finish, then again by the framework in Figure 1 we must be able to battle an easy trainer in each of our clause gadgets. To reach these clause gadgets the trainer had to make an exclusive decision in each variable gadget. This choice corresponds to a literal assignment. So the path our trainer takes through the variable gadgets, and then through the clause gadgets to the finish gives us an assignment of literals such that every clause is satisfied. Obviously this corresponds to an assignment that satisfies .
(Back to me)
Difficulty: 9. This isn’t that hard to understand, but there are lots of details that are needed- having to position the trainers in exactly the right position for it all to work.
I told Daniel when he gave me his Monotone Satisfiability reduction that the actual problem mentioned in G&J was Monotone 3-Satisfiability. So he went off and did that reduction too.
Monotone 3 SAT. This is a more restrictive case of Monotone SAT
Given an formula of clauses where each clause in contains all negated or non-negated variables, and each clause contains at most variables. Does there exist an assignment of the variables so that is satisfied?
the following assignment satisfies :
And the following is in Monotone 3SAT form:
are both unsatisfiable.
In the following reduction we are given an instance of 3SAT,
. Here each clause is of the form:
and each is a literal of the form .
We use the following construction to build an instance of Monotone 3 SAT out of the above instance of 3SAT :
In each clause we have at most one literal, that is not of the same parity as the rest of the literals in the clause. For every such literal, we may preform the following substitution:
this yields a modified clause .
Now we must be able to guarantee that and are mapped to opposite truth values, so we introduce the new clause:
and conjunct it onto our old formula producing a new formula .
so we preform the substitution
Now repeating this procedure will result in a new formula: .
We claim logical equivalence between the and This is semantically intuitive as the clause requires all substituted literal in to take the value opposite of this was the stipulation for the substitution initially. It is also verifiable by truth table construction for:
If there exists a truth assignment that satisfies , then we may extent this truth assignment to produce which will satisfy
by letting for all and letting for all .
Obviously if is satisfiable must be by the above construction of . So by the above claim we have that will satisfy .
Continuing from the above, if we have a truth assignment that satisfies , then by the claim above it also must satisfy . And is a sub-formula of so any truth assignment that satisfies must also satisfy .
(Back to me)
Difficulty: 4, since it’s a little harder than the regular Monotone Sat one.