I think I first played the first Spyro the Dragon game when I was about seven years old. I’m not sure what attracted me to the game. It might have been the idea of collecting bright, shiny gems, the rather gentle learning curve (unlike the Crash Bandicoot, Contra or Metal Slug games that I remember having access to on the PS1 at that time) or perhaps the exploration required to complete a 100% clear of the game.

More recently, a collection of all three games in remastered form was released on PC. I never actually got round to playing Spyro 3, for some reason, and the first two games were pretty enjoyable, so I purchased it. It was available on Steam for £34.99, though I searched elsewhere and managed to procure it for just £13.10.

In Spyro 1, the titular purple dragon has flame breath, is able to charge at enemies with his horns, and can jump and glide around (though he cannot fly). Levels in Spyro 1 generally feature a mostly linear path from a level’s starting point to its exit, with enemies and obstacles along the path; figuring out how to traverse this path can be interesting, in that one usually needs to understand how enemy attacks work, and/or how Spyro can navigate relevant obstacles, typically through judicious platforming.

However, reaching each level’s exit is rarely the end goal. Typically, levels contain a number of gems (and sometimes dragon eggs as well), and collecting these requires exploration beyond the main route. Although I certainly wasn’t familiar with the abstract concept at that time, when I first played these levels as a seven-year-old I would usually perform a depth-first search. I would head down an interesting path until it reached a dead end or looped back to somewhere I’d already explored, and then continue to a new path that hadn’t been searched yet.

Depth first search is naturally a reasonable way to explore a game world. Since players can’t teleport freely, breadth first search is generally not an option. Iterative deepening-style approaches could make sense if paths are uni-directional or if there are frequently very long routes that don’t need to be explored on the way from one area of interest to another, as it is usually possible to exit and re-enter levels from the beginning. However, this isn’t generally true in Spyro level designs. Thus, for Spyro I don’t think this core process has changed very much. What keeps Spyro interesting for me tends to not be about overcoming the obstacles on a given path (with some exceptions), but instead about identifying legitimate paths that can be plausibly explored. A simple example is in one of the areas in World 1, Town Square; there is an optional area accessed by gliding down a staircase and around a wall. This eluded me for a long time when I was seven, as clearing most of the levels (in the sense of reaching the exit, not 100% completion) can be done with gliding in mostly straight lines.

Typically, the game will give you hints that such hidden areas exist. Most obviously, each level has a number of gems to collect, and this number is known to the player – so shortfalls typically indicate that there are still hidden areas to be found (provided the player has meticulously collected everything in the areas they have already explored). It is also fairly common for some gems to be visible from one of the main paths, even if it is not made obvious how to reach them. With the Town Square example, one of the dragons in the area does actually give you a big hint on what to do, though I wasn’t aware of this as I skipped cutscenes then.

I usually had to resort to walkthroughs or online guides to find these answers in the past. I’m pretty confident it was quite different from the way I’d approach these problems now. I think I mostly just tried a ton of different approaches and saw what worked. However, I now use deductive reasoning more heavily – perhaps experience with participating in logic puzzle contests has helped. Instead of simply exploring paths that come to light and trying things that look encouraging, I tend to apply a perhaps more principled approach to identifying suitable routes to explore once the obvious methods have been tried. I would consider the usual platforming techniques that may be appropriate and test them against the level setup (e.g. Is there a high point I could glide from? Could there be some non-obvious platforms that are actually safe to land on? If there is/are supercharge ramps that increase Spyro’s running and jumping speed, can I use these? Can I combine them?). I’ve been able to solve quite a number of levels that I wasn’t able to in the past.

Some of this reminds me of a book I read earlier this year, Problem Solving 101: A Simple Book For Smart People by Ken Watanabe. The techniques introduced there for making decisions on how to approach problems are applicable to clearing a Spyro level – for various root causes/problems what one should do varies, and it is probably more expedient to think through what needs to be considered in a principled way than scurry off aggressively pursuing whatever solution comes to mind. I don’t think I’ve ever been faced with a level sufficiently difficult to need drawing a full-blown logic tree out, but even mapping out such a tree in my mind helps a lot.

The Touhou Project is a series of “bullet hell” shoot-em-up games. In these games, the player controls a character within a 2D plane and needs to dodge large quantities of bullets. These games tend to be fairly difficult, testing players’ reflexes and in some cases logic as well (for example, many patterns are aimed at or relative to the player’s position; misdirecting such patterns can be useful).

I wouldn’t say my reflexes are very good. Nonetheless, good progress can be made using careful resource management; players are given tools in the form of lives (extra chances after getting hit) and bombs (single-use abilities that clear the screen and deal damage to enemies). The eleventh installment of the series is called Subterranean Animism (SA), and I’m choosing to look at it for this post because it is widely regarded as the hardest game in the series to clear on normal difficulty. For most of these games (on normal), I can just sit down, play the game, dodge bullets and win. There were two exceptions – the fifteenth entry Legacy of Lunatic Kingdom (but even then that only took about five attempts), and SA. SA required a nontrivial amount of planning – I had to learn some of the specific patterns, and also chose a shot type I wouldn’t normally pick.

I generally play Touhou games on normal with an aim to complete the game on a single credit; this is called a “1 Credit Clear” or 1CC. I’m generally somewhere in between difficulties; I’m fairly comfortable with Normal in most cases, but Hard is hard (the game also has an even harder Lunatic mode). I’ve successfully completed 1CCs of most of the games in the series on Normal, and a few of the easier ones (7, 8, 10) on Hard. SA was the toughest game in the series for me to 1CC; it is also the last one I did, at least from installments 6 through 16.

Resource Management

Touhou games usually start the player with two spare lives; this is true in SA as well. However, the bomb mechanic is different from other games, which give the character a fixed number of bombs per life. In SA, players sacrifice some of their shot power when using a bomb. A character’s shot power usually ranges from 0.00 to 4.00; this is increased by collecting powerups when fighting stages or bosses. Firing off a bomb costs 1.00 shot power (and cannot be done if one is below 1.00). This can be frustrating, as some patterns become more than proportionally harder if the player’s shot power is low. When a character is hit, she (the games feature an all-female cast) will drop powerup items such that shot power will be reset to at least 2.25 (higher if shot power was at least 3.00). There is an exception – if it is the character’s last life, a full powerup item will drop that sets shot power to maximum.

The game also has mechanics for earning additional lives. In SA, boss enemies have a staged health-bar with multiple patterns; if the player defeats a pattern within the time limit and is not hit, a life fragment will drop; five life fragments result in an extra life. Bombs are allowed.

A Touhou game is divided into six stages; typically stages 1 through 3 are mostly a non-event for me. That said, for SA, the boss of Stage 3 has a few fairly nasty attacks. Most of my aforementioned “blind” or casual 1CCs involve racking up large stocks of lives and bombs on these stages, and then utilising these aggressively in the later stages. We can see this on SA, as well as on what is often regarded as one of the easier entries in the series, Imperishable Night (IN); the first death on SA is at the end of stage 4 while that on IN is at the end of stage 5. That said, I’m actually already failing to dodge patterns as early as stage 3 or 4. It’s important to be willing to use bombs to deal with difficult patterns, as they are much easier to recover (by subsequently picking up powerup items) in SA. This becomes even more important in other games like IN, where bombs that are unused when a player is hit just go away.

Character Selection

Touhou games usually give the player a choice of multiple player characters, and sometimes for each character different weaponry. Typically, different characters have different movement speed and possibly some other advantages or disadvantages, like having a smaller hit-box or extra bombs. In terms of weaponry, players may select from different normal shots and bombs, which usually have balanced trade-offs. For example, one may pick a homing shot which does less damage but can hit enemies anywhere on the screen, or a shot that only shoots straight ahead but does more damage.

Earlier, I mentioned being able to sit down and just play the game; in most cases this involves the main character of the series, called Reimu, who usually (and in SA) has relatively slower movement and a small hit box. I also normally use her homing shots for a first playthrough, even though I usually prefer straight shots after I get more comfortable with the game. These don’t quite exist in SA.

Apart from slightly different shooting and movement mechanics, many Touhou games also feature a medium to late stage boss (often on Stage 4) which adapts her patterns to the player’s character selection. This is on full display in SA as well; the Stage 4 (out of 6) boss has a relatively easy warm-up battle that is static, before reading the player character’s mind and creating patterns from that (which differ depending on the character and shot type).

Most of the time, the different variants of patterns the boss uses are quite balanced. However, this isn’t the case in SA and thus influenced my selection. Although I find ReimuA (with straight shots) is best equipped to handle the Stage 5 and 6 bosses, the Stage 4 fight one has if one makes this choice is extremely difficult, I’d say possibly even harder than the later bosses. Pictured above is Double Black Death Butterfly; it isn’t apparent from the picture, but some of the butterfly bullets are rotating inwards (and so one needs to dodge bullets possibly coming from behind as well). I thus picked ReimuB (which has a weakly homing shot, a relatively easy Stage 4 fight and a special ability to gather powerups from anywhere on the screen) for my 1CC.

Learning the Patterns

Of course, even with careful resource management it’s unlikely that one can perform a 1CC if one’s dodging skills are too far below the bar. While some of the patterns are random and/or based mainly on reflexes, others have a certain trick to them that makes the pattern a lot easier once figured out. With experience, one can figure out common elements and ways to deal with them (for example, a stream of bullets fired at the character’s position; move slowly, but to change directions make a sudden quick movement to open up a gap in the stream) – this drives most of the “sight-read” clears.

In a sense, good resource management is less critical (consider that one can completely ignore the resource system if one can reliably dodge every single pattern in the game) if one can dodge the patterns. That said, it’s actually possible to clear one these games even if one is quite poor at dodging them, if one makes good use of the resources one has.

Many modern video games feature an achievements system. Achievements are typically awarded for completing specific tasks; typically achieving all of these requires doing substantially more than just what is required to finish the game. In many cases, it’s not even possible to complete all of the achievements on one playthrough.

In a previous post, I wrote about Slay the Spire, an RPG where combat is driven by a deckbuilding mechanic. The game also features an achievements system. I finished getting 100% completion on these achievements this week – this took quite a long time. If I wanted to start from the beginning and accomplish these as quickly as possible, I would need to plan carefully. The main issue here is that achievements are not independent. For example, there are chains of achievements where the final achievement subsumes all previous ones (e.g. “have 99 block, have 999 block” or “unlock ascension mode, finish ascension 10, finish ascension 20”). Furthermore, some achievements can be achieved using the same strategy (e.g. “play 25 cards in a single turn” and “defeat a boss before it takes a turn”, which can both be achieved by building an infinite combo). Other achievements can often be significant handicaps (e.g. “beat the game with a deck that has no uncommons or rares”, “beat the game with only 1 relic”) and I thus think it’s a lot harder to combine these with other achievements.

The game is played in distinct runs, and a majority of the achievements involve performing specific feats on individual runs. We can model this by identifying strategies that unlock one or more achievements. If we try to use the fewest number of distinct approaches, this becomes what’s called a set cover problem. There are achievements and strategies with each being non-empty and a subset of . We also require that , otherwise not all achievements are achievable. The goal is to identify some set of strategies we need to deploy such that . Trivially, picking everything is possible, but it’s very unlikely that that’s going to be minimal.

This problem is known to be NP-hard; the decision version (does there exist a solution using strategies or fewer?) is NP-complete. Clearly, we can obtain a solution that is worst-case exponential in by iterating through each possible subset of , checking if it satisfies the criteria and remembering the best solution we’ve seen so far. There’s a little dynamic programming trick we can do to memoise partial achievement states (e.g. I can get to the state where I have the first 8 achievements and no others using two runs with one strategy but three with another; I’ll always pick the first). This achieves a solution exponential in but linear in , which is an improvement. We can also try to pre-process the input for and a little, by eliminating any achievements that are subsumed by harder achievements, and by eliminating any strategies that are strictly dominated by others.

If this is unacceptable, we can either try to use approximation algorithms (for example, a standard ‘greedy algorithm’ which picks the subset with the most outstanding items doesn’t always fare too poorly). We can also attempt to leverage constraint solving systems; these don’t deal with the exponentiality of the problem directly, but they often have good heuristics to deal with this.

That said, this isn’t a very good model, as some strategies are riskier to implement than others. For example, it may be theoretically possible to combine “beat the game with one relic” and “beat the game without uncommon or rare cards”, but that seems unlikely. For example, if each of these individually has a 1% chance of being completed but attempting them together has just a 0.1% chance, doing the achievements separately will take an expected 200 runs while the combined approach has an expectation of 1000.

A natural extension to this is to consider the weighted version of the problem. We change the formulation slightly, so each strategy has a weight and we seek to minimise the total weight of the strategies we pick. If we assign a strategy a given probability of success , since runs are (mostly) independent, the expected number of runs it takes to attain a successful outcome would be following standard geometric distribution results. The dynamic programming based algorithm still works if we keep track of weights instead of number of sets.

This is better, but there are still a few issues with the model. Firstly, partial successes haven’t been considered in this model; we might try a risky strategy for three achievements and fail, but still complete two of them. We could try to account for these by figuring out some kind of state transition between achievement states – it wouldn’t be a set cover problem any more. We would be building some kind of a discrete-time Markov chain on-the-fly.

Modelling this as a set cover problem also seems to imply that our algorithm is offline; that is, it formulates a strategy and then executes it. However, Slay the Spire has a significant luck element, especially if one is trying for specific achievements, and thus an offline algorithm doesn’t work so well. For example, one achievement requires “play 10 shivs on a single turn” and another “have an enemy reach 99 poison”; the cards and relics that support these are quite different, and depending on what cards are offered as the game progresses, one achievement or the other could become easy or nigh-impossible. If both achievements remain to be unlocked, it seems suboptimal to commit early on.

I had a weekend break in Zurich to meet up with a friend who works there. We often play board games when we meet, so I brought Hanabi and FUSE (very compact games, suitable for travelling – especially since I almost always travel HLO) but my friend was thinking of getting something different. We thus headed down to a game shop, and picked up The Game: Extreme (Board Game Geek).

The rules are fairly simple. There is a deck of 98 cards – one copy each of the integers from 2 to 99, inclusive. Players will have some number of cards in their hand (eight for a solo game, seven for 2 players), and play them to the table; the game ends in a loss if a player must play, but is unable to (or if all cards are played, which is a win). It is a cooperative game; players all win or lose together. The rulebook also mentions a half-win if players finish with ten cards or fewer remaining.

There are four communal piles that players can play cards to. On two of these, cards played should be numerically increasing; on the other two, they should be numerically decreasing. However, one may also go against the direction of a pile – this can only be done if the jump is exactly 10. Thus, for example, if an increasing pile has a 57 on top, one may play a 59 (increasing) or a 47 (ten less), but may not play a 49 (not increasing, and not ten less). Of course, if a player has both the 59 and 49, the player may play the 59, after which the 49 becomes legal.

One can probably write some search-based algorithm to figure out what the best possible outcome is for a given hand and individual pile, or even set of piles given some fitness function. There’s also a straightforward dynamic programming optimisation, and for certain classes of fitness functions there might be better principles one can leverage.

Players must typically play two cards on their turn, though they may play more (for example, if they have a chain of cards that involves several reverse jumps) while there are still cards in the deck; once that is emptied, they only need to play one. Once they have played all of their cards, they draw back to their hand size and play passes to the next player.

The Extreme version adds special effects to some of the number cards. These may either restrict the current player’s turn (requiring that they stop immediately, play exactly three cards, or immediately cover up the card that was played) or affect all players while they are exposed (requiring players keep quiet, play all the cards in their turn to the same pile, not do reverse jumps or only draw 1 card at the end of their turn). Apart from the stop card, all of these make the rules for playing more restrictive (and thus I’d say this is somewhat harder than the base game).

The solo game doesn’t generally seem too difficult as one can plan and execute sequences of reverse jumps without interference from others. The keep-quiet special effect no longer really matters, and the stop cards actually become positive as they allow for drawing cards back up earlier. I’m not sure how one might formulate an algorithm for determining optimal moves, but I can generally see the following principles:

• Play the minimum number of cards needed per turn, and then draw back up. This lets you see more options, which is useful. Normally in a multiplayer game you may want to play more than the minimum to ensure that no one interferes with your plan, but in single player no one will anyway.
• Plan and aggressively use reverse jumps. These often get thwarted in multiplayer games (you might draw a card that allows a reverse jump, but someone might need to play on the relevant pile before it gets to your turn).

Playing with more than one player gets trickier, as communication between players is permitted but precise numerical information is not. I would say things that imply precise numerical information (e.g. “I can jump this pile”) are also bad. There are edge cases which are probably acceptable (e.g. “it makes no difference to me; which of these would you prefer I play to?” with an up-65 and down-67 suggesting I hold the 66). Still, talk of “small/large jumps” and actively saying “stop” after interesting cards are played is very useful.

Cooperating well involves looking ahead to other players’ turns as well; it’s not always optimal to, on each player’s turn, individually minimise “damage” to the piles. There have been times where being forced to jump a pile by 30 or so, I’ve encouraged my teammate to play more cards there since it would be destroyed on my turn anyway. We only played with two players, but I imagine the dynamics get more interesting with larger groups.

The Game: Extreme is a very light game; while there is some minor logic involved it’s not particularly complex. It’s fairly fun and witnessing successful chains can be enjoyable; the special effect cards can also cause annoying obstacles (while blocking reverse jumps is probably the most damaging, the keep-quiet one is interesting as it changes the game for a while). It’s certainly a fun thing to play while passing time and discussing other things, and hard enough to provide a good challenge without being frustrating.

Alphabear 2 is a word game where players are given a two-dimensional grid of tiles. Some of these tiles show letters, along with timers; others are concealed and must first be exposed by using an adjacent letter in a word. Players use letter tiles (anywhere on the grid) to form a word. When a player makes a word, he/she scores points equivalent to the sum of the timers on the tiles that are used. The timers on all tiles that aren’t used then count down by 1; tiles that reach zero become unusable stone blocks.

Usually, this means that you want to form long words that incorporate tiles that have shorter timers; for example, there is a ten letter word on the above board (PEDESTALED), but it doesn’t use the F. After playing this word, the timers on the remaining letters will count down by 1; the P will now have a timer of 2, the first E 10, and so on.

However, the game does have various mechanics that mean this isn’t always the best strategy. For example, one needs to ensure that there are actually enough letter tiles to work with, and that the distribution of letter tiles allows reasonable words to be formed. The game also has players choose bears for each mission which have special abilities such as awarding additional points for certain words. Actually, in hindsight FEASTED might have been a better choice than DEADLIFTS, as I was using a bear that gives a point bonus for words ending in ED.

In addition to scoring points by making words, players also receive bonus points for the largest fully cleared rectangular area. The game identifies this for you. I’m not sure how this is actually implemented, though there are large enough boards (15 by 15?) that it’s probably not the naive algorithm of generating and testing all rectangles; where is the width of the board that would be . There is a DP-based approach where one can keep track of the number of stone blocks that slashes this to which would probably be enough. With more clever approaches it’s possible to knock the time complexity down to – and that’s probably optimal as any less would involve not actually reading some parts of the grid.

Most of the time, this isn’t something to worry about too much. I’m able to clear most levels with no stones at all. However, if we need to take a stone, ideally it should be as far away from the center of the board as possible (note that only the largest rectangular area counts).

Defining optimal play in general is difficult, owing to the spatial reasoning required by the largest clear area bonus, uncertainty of tiles that are yet to be revealed and sheer breadth of options when faced with many open tiles.

Before starting a round, the player has to select bears to use. Optimal decisions here largely depend on the specific challenge being played. A bear that scores bonus points for six-letter words isn’t particularly useful on a tight board where one has only a few open tiles at a time; some bears add time to timed boards, which isn’t useful if the board has no time limit. Furthermore, players select up to three bears each round; these bears can have useful synergies. In particular, I like a combination of “summon three Zs” and “score triple points for words including JQXZ (not stacking, unfortunately)”; “summon ING” or “summon ED” goes quite well with these two as well. I’ve used these to complete a mission deemed “impossible” given the strength of my bears at the time.

We can perhaps begin by focusing more narrowly on optimal play of an Alphabear endgame (where all tiles have been revealed), ignoring special abilities bears may have for now and assuming that the board has a solution. Here is an example:

From this point on, there is no more uncertainty as to what letters we have to use. Interestingly, this board shows that a greedy algorithm where one tries to play the longest possible words doesn’t work. One could begin WORKSHOPPED, leaving RAII, but this is actually an unwinnable state. There are several two-turn solutions, like ROADWORKS + HIPPIE, or WORSHIPPED + KORAI. (Obviously, this is very high-level play; I think I played SKIPPER with this board. That leaves RODIAHWO, for which I’d probably have played something like RADIO + HOW, though HAIRDO + WO is better.)

Given a multiset of pairs of letters and timers , we want to return a suitably ordered list of words , that satisfies the following properties:

• legitimacy: every word is included in the game’s dictionary.
• resourcing: words do not use tiles that aren’t in .
• timing: for each pair , is used in a word before or on position .

The above set of properties is not correctly specified without a refinement to deal with duplicate letters. There are several ways to deal with this; assuming low cardinality, one approach could be to recognise all duplicate instances of letters as separate letters, and suitably expand the dictionary to allow this. Our list is then , and the dictionary recognises both and .

I don’t have a good way of finding an optimal solution; I suspect the problem is actually NP-complete; it feels like SAT or 3SAT may be reducible to this problem, though I haven’t worked through the details. Nonetheless, there are several search heuristics we can try. Clearly, on a given turn we must use everything which now has a timer of 1. Also, although this board is an example of why you don’t want to be greedy, in general it probably makes sense to do some kind of ordered backtracking, where one expands the most promising nodes first. I can imagine an A* search could actually work well, though finding an admissible heuristic could be hard.

There’s a fourth property we want as well:

• optimality: the score produced by the words is maximal.

Going back to our sample board, WORSHIPPED and KORAI will score one more point than ROADWORKS and HIPPIE, because there is one fewer timer tick. In general, we want to minimise the number of timer ticks by playing longer words upfront where possible, as this reduces the number of timers that are still ticking. Once one has devised a feasible solution, it may make sense to perform some iterative optimisation by seeing if it’s possible to bring letters forward. Of course, that approach may get us stuck in a local optimum. I could see some kind of iterative hill-climbing algorithm from the top-n admissible solutions (as found by our ordered backtracking) yielding decent results.

Unfortunately my vocabulary and anagram skills are far from good enough to handle the required complexity, especially under time pressure. Most of the time, successfully playing every letter on the board is enough to complete the mission’s objectives. Interestingly, it looks like the game wasn’t really programmed to deal properly with successfully using every letter but failing the mission. The game claims that there’ll be an additional reward on the reward screen when identifying the board as clear; yet, there is no reward screen as the mission was failed.