Russell Sherwood

Dead Man's Attack

Russell Sherwood  Wednesday, December 13, 2017

Most CC players have heard of Dead Man’s Defence, the unsavoury technique used to put off the result of the game (generally a loss for the person using DMD). Here I want to discuss it’s cousin, Dead Man’s Attack!

 

Picture the situation. You are playing a game, which is into the early endgame. Engine Analysis shows a nice +1 core in your favour but neither you nor the engine knows how to make progress.  What then tends to happen is that the moves played shuffle the pieces around without making any visible progress and after a little while longer a draw is agreed as its one of the positions the engine does not understand. It tends to be in practice that a Fortress was in place which secured the position.

 

That is what is supposed to happen……in a minority of games we see the “winning” player refusing the give up his “win” and continue shuffling. Now I am on the wrong side of one of these games at the moment and am at move 110! An interesting fact for me is that my opponent will not lose any rating points for a draw which is often the motivation to keep shuffling, hoping your opponent makes a mistake.

 

This piqued my interest, how often does this take place? Using my trusty Chessbase I took a look at the longest ICCF games in the database and the results are interesting……..

 

137 games more than 120 moves long

56 of them since 2012

33 of these turned into draws, leaving 23 as decisive

 

So the message to those playing DMA is quite clear – don’t its chance of success is less than 50%

Horses for Courses

Russell Sherwood  Monday, December 11, 2017

Most CC players tend to be aware that engines don’t play certain types of positions very well and traditionally the King’s Indian Defence has been seen as an area where Engines are close to clueless!

 

Fewer players tend to also know which Openings/Positions engines tend to do well or not so well in but very few have a comprehensive view of this.

 

This does not have to be the case. Some very interesting research has been taking place at  http://www.amateurschach.de/ One that is of interest to ambitious CC players is his project to create a Openings rating list of sorts.

 

There is a great description of what has been done but in simple terms, various engines have been tested and the results then tabulated by ECO Code.  At the Summary level Stockfish/Houdini and Komodo are at the top but if you into individual ECO codes or groups of codes, it shows that all of these engines are perform well/badly in certain positions!  Now this information can be used in a number of ways…..

  • You can make sure your engine/opening combination matches up well (which one you change is your choice!)
  • If you know which engine your opponent uses as their primary analysis tool, then you could steer the game into an area that engine does badly.
  • If you use an analysis technique involving multiple engines, you will know which ones tend to be more accurate in which type of position.

Is this a game changer? No, but when you consider that the difference in performance between the “Big 3” Engines is 15% in certain positions it does appear to be a potential way to gain a nice “edge”

Alpha Go a GoGo

Russell Sherwood  Sunday, December 10, 2017

Alpha Go a GoGo

 

https://arxiv.org/pdf/1712.01815.pdf

 

Earlier this week an interesting paper was published regarding Google Deepmind’s Alpha Go Zero teams research into Chess. Since published this report and various sensationalist articles have sent seismic waves through the Chess community.

The basic claim is that this program learned to play chess in 3 hours to the point that it beat Stockfish 28 Wins  - 72 Draws – 0 Defeats.

Whilst, without a doubt, this approach will eventually become the prevalent approach to computer chess, the reports circulating are a little over the top.

Looking more closely at the details. The 3 hours taken to reach these levels was on hardware so specialised and new that it is only available to Google’s Deepmind team. For us mere mortals this can be thought of as a Supercomputer. So whilst 3 hours is very impressive, if it had been on typical hardware this would have taken much longer.

The conditions of the match were somewhat in Alpha Go Zero’s favour. Firstly whilst Stockfish ran on 64 Cores, it only had 1GB of HashTable available, probably around 1% of what is required. This is the equivalent of putting a Ferrari engine into a Mini!

The second issue is the time control used – 1 minute per move. All of the top Chess engines have very efficient algorithms to manage time and identify when to spend more time on a move or when to spend less – very similar to Human behaviour.

The third difference and probably the most telling, is that StockFish played without the benefit of an opening book. A fair event would have been either an opening book or a Brainfish/Cerebellum combination.

The belief amongst the Computer Chess enthusiasts is that if the conditions above had been “fair” the result would have been much closer.

In addition, for a wider test, the same match should take place with Houdini, Komodo.

So to summarise, this approach is the path of the future but the current “wow factor” is probably premature. For those interested, the Giraffe engine is worth looking at and some projects which can be seen on Github putting AlphaGo Zero’s ideas into practice!

Where there’s a way there’s a will!

Russell Sherwood  Sunday, December 10, 2017

Where there’s a way there’s a will!

 

At first glance, you may think I have got this saying the wrong way round! In typical use, you would be correct but in the world of improvement, this is a more common saying. In Chess (as in other things) we all want to improve and there is a belief among many that simply wanting to improve (so-called positive thinking) is enough to get better on its own. However, the evidence tends to suggest otherwise and that having a clear path to improvement is more important.

 

So how do we get a clear path to improvement In CC?  Much of the advice that does float around is from high rated players who started out highly rated (i.e. their initial rating was very high and stayed there) and is so not necessarily relevant to a typical players journey.

 

Where do we want our advice to come from? Preferably from people who have undergone the journey we wish to undertake, i.e. who have seen their ratings improve over an extended period.  Downloading a few ICCF Rating lists and some manipulation in Excel leads to the list below.  

RUS   Kornev, Aleksey Nikolaevich 12 M 2468
USA   Risquet, Carmelo 12 M 2386
ENG   Stebbings, Anthony J. 12 M 2352
RUS   Agryskina, Nadezda Ivanovna 13 F 2538
RUS   Odrov, Viktor Anatolievich 13 M 2432
GER   Kuntze, Andreas 13 M 2397
ISL   Ásbjörnsson, Ásgeir Páll 13 M 2391
LAT   Kovalenko, Igor 14 M 2429
NED   Hooft, Diederic 't 17 M 2390
USA   Stein, Kurt W. 19 M 2533
IND   Sengupta, Deep 19 M 2515
GER   Lembeck, Karl-Heinz 19 M 2512
NED CCE Jong, Jan Willem de 21 M 2503
GER   Schmidt, Jörg (* 1954) 20 M 2376
POL   Gorzkiewicz, Łukasz 23 M 2473
DEN   Jensen, Tommy 23 M 2416
SRB CCM Petronijević, Zoran 23 M 2398
BEL   Van Assche, Jeroen 24 M 2393
AUT   Komaromi, Gabor 25 M 2401
ENG   Adair, James 26 M 2463
PAK   Idrees, Muhammad 25 M 2368
RUS LGM Churakova, Natalya Evgenyevna 28 F 2438
RUS   Bukarin, Mikhail Yurievich 29 M 2471
INA   Margana, Adhy 28 M 2359
ISL CCM Guðmundsson, Elvar 30 M 2504
NED   Werten, Tony 31 M 2392
ITA   Rombaldoni, Denis 33 M 2401
SUI   Schmid, Pablo 35 M 2525
IND   Dutta, Amit 34 M 2395
RUS   Kudryavtsev, Dmitry Aleksandrovich 34 M 2385
POR CCE Vasconcellos, Renato 35 M 2411
SLO   Coklin, Marko 37 M 2435
RUS LGM Matveeva, Maria Aleksandrovna 39 F 2498
IND   Das, Arghyadip 37 M 2363
CUB   Fernández Martínez, Juan Carlos 47 M 2393
ROU LIM Stanila, Elena 53 F 2389
USA   Kulick, Neil 59 M 2354
CRO CCM Feletar, Darko 79 M 2391
RUS   Matvienko, Vladimir Petrovich 18 M 2404
ENG   Rallabandi, Praveen Kumar 112 M 2398
ESP   Moreto Quintana, Alex 120 M 2437
UKR   Khanas, Valeriy 125 M 2357
SLO CCM Zajšek, Franc 147 M 2372
WLS CCM Yeo, Gareth 153 M 2378
UKR   Mashchenko, Vladimir 30 M 2481
SLO   Pokrivač, Izidor 32 M 2405
RUS   Rybin, Anton Sergeevich 24 M 2480
SWE   Qwarfort, Fredrik 26 M 2448
INA CCE Sitorus, Yosua 238 M 2403
CUB   Pérez Rodríguez, Rubén 38 M 2392
RUS   Mannanov, Rinat Rafikovich 56 M 2396
GER   Marwitz, Ullrich 78 M 2370
ITA IM Piccirilli, Fabrizio 74 M 2390
RUS   Enin, Anatoly Nikolaevich 107 M 2353
CAN CCE MacTilstra, Ian 160 M 2354
ENG IM Soh, Edmund 240 M 2403
WLS IM Claridge, John B. 255 M 2399
CAN   Czerniawski, Andrew 79 M 2436
GER   Ulbig, Stefan 107 M 2392
GER CCM Hartl, Hermann 124 M 2385
PER   Quiñones Borda, Jorge Victor 141 M 2406
USA CCM Koo, Oliver 150 M 2388
GER CCE Meißen, Frank 85 M 2401
CZE CCM Hrubčík, Martin 234 M 2412
GER IM Anderskewitz, Ralf 341 M 2401
UKR IM Koshmak, Iurii 109 M 2451
NOR   Johansen, Anders Sten 467 M 2386
USA CCM Hernandez, Angel 177 M 2401
AUS CCM Roebuck, Derek 110 M 2378
ITA   Galliano, Giovanni 113 M 2428
SWE CCM Strömberg, Håkan 121 M 2392
ENG CCM Carr, Trevor 229 M 2391
RUS SIM Panitevsky, Ivan Anatolevich 180 M 2504
HKG IM Tsang, Hon-ki 168 M 2419
SLO CCM Pirš, Jernej 185 M 2411
ESP IM Sánchez Huerga, Aser 193 M 2445
CZE CCM Binas, Jindřich (*1954) 237 M 2421
GER CCM Bär, Lutz 150 M 2400
GER CCM Lukas, Norbert 280 M 2355
BUL CCM Petkov, Stamat 126 M 2407
GER IM Wenzel, Stefan 177 M 2461
GER   Hesse, Olaf 293 M 2447
ARG   Fernández, Javier Horacio 329 M 2416
AUT CCM Hengl, Christian 236 M 2420
GER   Keskowski, Thilo 201 M 2406
GER LGM Achatz, Kirstin 223 F 2377
USA CCE Biedermann, Kyle 312 M 2384
GER CCM Tiemann, Christoph 162 M 2412
GER CCM Homont, René de 276 M 2353
WLS LGM Sherwood, Helen 452 F 2386
RUS   Razumikhin, Andrey Mikhailovich 364 M 2391
RUS CCM Novikov, Sergey Vasilievich 249 M 2400
RUS CCM Prozorovsky, Vyacheslav Grigorievich 208 M 2404
USA CCM Landes, Eric 222 M 2397
GER CCE Boos, Markus 193 M 2400
CUB CCE Marrero Rodríguez, Alexis 167 M 2416
ITA IM Mauro, Lucio 735 M 2407
AUS CCM Mulligan, Barrie 276 M 2414
CUB CCM Pérez López, Alberto 536 M 2407
ENG CCM Weldon, David J. 227 M 2410
POL   Mostowik, Daniel 773 M 2366
ITA CCE Bellegotti, Giorgio 243 M 2453
LTU   Voveris, Saulius 271 M 2388
ENG CCM Brasier, John 352 M 2428
USA   Zaas, Peter S. 224 M 2369
DEN CCE Konstantinov, Maxim 207 M 2421
ROU CCE Taras, Iulian 546 M 2390
RUS CCM Kozlov, Aleksandr Anatolievich 237 M 2382
POL IM Broniek, Mariusz Maciej 523 M 2439
USA IM Schakel, Corky 782 M 2416
ROU CCE Stanescu, Teodor-Adrian 146 M 2406
GER CCM Scheiba, Manfred 218 M 2403
CZE CCE Dědina, Miroslav 578 M 2354
GER LGM Bolz, Barbara 293 F 2402
GER CCM Zielasko, Andreas 284 M 2390
LTU   Voveris, Gediminas 660 M 2352
ESP IM De Carlos Arregui, Inigo 804 M 2433
ENG CCM Thompson, Brian 953 M 2413
ESP CCM Conde Poderoso, Antonio 597 M 2373
GER IM Löffler, Werner 802 M 2394
GER IM Langer, Raimund 443 M 2406
SUI IM Janisch, Manfred 658 M 2441
CZE SIM Sýkora, Josef 1477 M 2416
POL IM Mirkowski, Piotr 457 M 2412
GER SIM Gromotka, Harry 1456 M 2389
CZE CCM Cvak, Rudolf 1585 M 2363
ENG SIM Rawlings, Alan J. C. 1775 M 2393

 

 

 

 

 

Close examination of the players on the list will bear dividends for the ambitious player. What do these players have in common? All have come through the system and raised their ratings up to 2350/2400+. This group is of more interest to us, than, for example, players rated 2450+ , who often only play against their rating peers and draw,draw,draw!

Practically these players make up around 1% of the total CC player pool. To put into context their performance - over the last 4 years they are scoring around 70% with White and beter than 50% with Black.

 

There are a number of different strategies in play.

 

Now I could give a summary of the learning examination of these players but that would take all the fun out of it for you!  Some pointers to help in your analysis…..

 

  • How many games are they playing/completing?
  • What level are they competing at?
  • What Openings and they playing/not playing?
  • What is their win –loss –draw ratios with black and white?
  • Are there any commonalities in their playing style?
  • Do they take draws? Short or Long?
  • Do you know any of them?

 

Putting some effort in here will allow you to reap rich rewards as it will give you an idea of “the way”, and Where’s there’s a way, there’s a will!

 

 

Humanity's last Stand?

Russell Sherwood  Wednesday, October 25, 2017

In recent years there has been a massive increase in the strength of Chess engines and their influence on Correspondence Chess has been significant, leading a number of critics to observe that CC is Computer Chess, not Correspondence Chess.

 

It Is true that a CC player playing without any computer support, at the higher levels, is putting themselves at a near fatal disadvantage but there is hope for the player wishing to play a human led game at this level.

 

How can this be? Much comes down to two issues:

 

  • Looking at the nature of the how the engine works and its strengths and weaknesses. It’s widely accepted that even the best engines (not necessarily the highest rated) only have the Chess knowledge of a 2200-2300 rated player. The reason they have ratings of (depending on which list you look at!) 3300-3500 is due to their tactical prowess, which is related to their ability to review millions of moves in time it takes a typical human to look at a mere handful.
  • The rise of the “engine jockey”: Players will powerful hardware/software and very little engine knowledge

 

 

So how can we, the poor human, make progress here?

 

The answer is twofold – utilising another engine (even in theory a rather weaker one) to check our moves for tactical holes – a form of error checking and attempting to exploit the engine's lack of knowledge. Now some more astute readers may be thinking that some Engines (Komodo for example) are rather better at positional play than others but whilst the better engines will know, for example, to occupy the open file , they are doing this driven by fairly simple formula’s rather than understanding the implication of the move which may be in 20-30-40 moves time.

 

How do we look to put this into practice?

 

  • Openings. In general engines struggle in openings, left to their own devices, as unless there is a clear way forward the number of options becomes too large a number of variations to be calculated, occasionally leading the engine astray. Looking through CC games is becomes obvious where a player thinks deviates very early in the game, knowing the typical engine will give a poor response. Now as a human to make this work, you need to understand the opening in question really well, especially its strategic aims.  This approach was (and is) used successfully in the King’s Indian Defence, where most engines are fairly clueless, although it must be said that the effectiveness of this approach is diminishing as engine knowledge is increasing in this area.
  • Deep Strategic middle game ideas. Another tactic used by “Engine Killers” is to steer the game towards tactically quiet, but strategically rich middlegames. This tends to be seen by players who utilise the Nf3/g3 type systems. Due to the flexibility of these systems opening knowledge (books) often run out quite quickly and the evaluation of the engine either heads towards 0.0 or is stuck on some random number (this is caused by some positional evaluation factor of the engine which may have no relevance in the position). If in this position you have a clear plan with quite distant objectives, which the engine is looking for tactical and basic positional style moves you opportunities become clear.
  • Engines can, sometimes, fall to a method related to the “slippery slope”. Imagine playing a game, materially is even, but your opponent has some significant positional plusses and you are under pressure. If you could free yourself from the pressure by giving away a pawn, in a non-fatal manner, you probably would, especially if it gave you some opportunities of your own. The engine is unlikely to do this. In this situation, it probably has an evaluation of about -0.4, but to give up the pawn would change this to -0.8 whereas to continue to play in the significantly pressurised would only lead to -0.5 or -0.6. This seems sensible but what then happens is that the engine goes down a slippery slope and 10 or 15 moves later is looking at an evaluation of -1.5 and a lost position!!
  • Endgames have always been a struggle for engines. Tablebases have “covered this up” somewhat but it is not uncommon to see massive misevaluations. Now as a human we can look to play against this directly or in a much deeper way in the middlegame. If we know that in this type of position a win is much more likely with a b pawn rather than a pawn and if his bishop is not on the board a win is  even more likely ,then  our play 20 or 30 moves earlier can steer the game in this direction, way beyond any meaningful horizon the engine may have!

 

So my dear human’s all is not lost and it is possible to make progress against the metal (and digital ) monsters and their poor slaves but only if we (a) Utilise where we are stronger than the engines and (b) use the engines to nullify their own strengths.

 

Should you be successful, don’t look to repeat your success in the exact same line as, like the Borg in Star Trek, your opponents will quickly adapt!

 

As a final thought – there were some games with GM’s + obsolete engines vs Leading edge engines, where the Leading Edge engines prevailed. This is something of a red herring for us as whilst the GM’s are almost certainly much better players than us, their approach was to play in their own style, rather than look to exploit the engines weakness. In addition, the time control’s for this was for OTB, which changes the nature of the game somewhat!

 

Good Luck!

 

 

The Time Machine

Russell Sherwood  Thursday, August 17, 2017

Have you ever got yourself into major time trouble? If this happens, especially in a number of games at once it can be a major headache and for some a cause of stress.

 

So having got into this hole, how do you get out of it? Here are a few ideas:

 

  1. If you have vacation time – use it, whilst you analyse your games. It still surprises me how few players don’t do this.
  2. Know the time controls – what exactly do you have to do? If you are playing in a 10 moves in x Days event, then once you reach each 10th move milestone you will get an injection of time. Remember to consider the so-called “Free Day” as well. This information is at the bottom of the Tournament Summary page.  It is worth noting that not all time controls are of this type – some more exotic experiments don’t have time injections.
  3. Consider your position in each event to play – is it a team event, are you chasing a Norm?  This will aid you in your prioritisation
  4. Consider the position at the board in each game? Where is it on the Win to Loss spectrum of results
  5. You now need to consider your plan in each game:
    1. Team events should, in general, be prioritised.
    2. If the game is “lost”, then resign it – why waste time you don’t have in these games?
    3. If the game is drawn or drawing then consider offering the draw
  6. Having followed step 5 we should have reduced our “pile” a bit for our remaining games we need to consider:
    1. Have I got to move daily?
    2. How much time do I have available (both in physical and processor time)?
    3. What is the game situation?
  7. We now have the grunt work to try and get to the time control. A few things can help:
    1. If the game has a fairly obvious line – go down it, using conditional moves if they are available in the event.
    2. If your response is obvious in a position – make the move (subject to a quick sanity check)

 

Hopefully after a few days of doing this you will have got yourself out of the immediate hole. Now is the time to consider how and why you got there in the first place.

 

I personally follow the 10 day rule – I rarely allow games to go below 10 days on my clock or not make a move for more than 10 days in a game. I probably manage to follow this 95% of the time and it prevents me getting into time trouble too often!

The Universal Soldier?

Russell Sherwood  Sunday, July 30, 2017

The Universal Solder?

If you spend any time analyzing with engines and have any ability yourself It becomes obvious that whilst they play close to perfect chess in some positions, they are also clueless in other positions. A traditional example being the King’s Indian Defense, where most engines struggle. An added dimension to this is that different engines excel and misbehave in different types of openings (and/or positions).

 

Knowing which ones work well (or otherwise) where is a significant potential advantage to the player. Trawling some of the Forums and Webpages can lead to a partial picture, although the problem is that any information gained this way is suspect and could become obsolete in the next version of an engine! If you are wondering why this is so? In simple terms most (stronger) engines are very finely tuned. This tuning means that the engine will play the “best” move in the highest number of positions. An improved version will means the engines plays slightly more positions correctly. The flaw is that, whilst Version 2 might play 94% of positions correctly compared to Version 1 which played 92% of positions in the right manner it does mean core positions – it could well be that 6% of positions more are correct but 4% more are now incorrect. This is general is not an issue, unless, of course, the positions (or more typically themes in positions) are the ones which are important in your opening repertoire.  Of course an interesting conclusion from this is that it would be possible to tune an engine for a specific kind of opening but it would probably be weaker in general!

 

So how can do this ourselves with some degree of certainty? An approach which can deliver dividends is as follows:

 

  1. Examine your own opening repertoire
  2. Identify Key positions in these openings. This can be done from personal knowledge or Opening Books (both Electronic and physical)
  3. Create an EPD file with these positions and the “Best” moves
  4. Run a “Beauty contest” with a number of engines to determine which best “understands” these type of positions.
  5. Use the winner(s) s part of your evaluation strategy in this opening.

 

Now some may wonder why I believe using the EPD approach is the way forward, rather than Engines Tournaments (either between engines or self-play). The simple reason(s) are : (1) EPD testing is faster and (2) Engine v Engine tournaments can suffer from the results being skewed by either the difference between the two engines or the oddities of an individual engine.

 

What is worth saying that Engine v Engine can be useful if you can determine which Engine your opponent uses as their main support and openings/lines could then be selected to utilize this information!

The Specialist

Russell Sherwood  Sunday, July 30, 2017

As I have mentioned a number of times most Chess Engines are not well suited to Correspondence Chess.  In fact many, if not most, Engine Analysis techniques methods are designed to cover some of the gaps left in engine design.

Many Engines can be modified for CC in their Parameter file and some of the sensible choices in this area were covered in a previous article.  There are a number of current engines that do significantly better than most in terms of CC performance. So (in no particular order)

Houdini 5 (and previous versions to a certain extent) – Tactical mode allows a much shallower, deeper search to be applied.

Matefish – a variant which delivers a similar functionality to Houdini’s tactical mode.

Komodo – probably the most configurable engine, “off the shelf”

CorChess  - A Stockfish variant with a number of developments related mainly to a more effective search method

Thinksfish – Another Stockfish variant, with a massive number of adjustable parameters and a special “correspondence mode”

CIFish – a combination of CFish (faster Stockfish variant) and CorChess

All of these are a big step in the right direction for CC players and well worth a place in your engine stable………

There are a number of other improvements which are possible but we will talk about the “Blueprint” another time!

Spot the Difference!

Russell Sherwood  Sunday, July 30, 2017

The Similarity test was created a few years ago to be able to compare engines, mainly to be able to attempt to detect cloning (The process of taking someone else’s engine, making a few minor changes and renaming it as your own, without any acknowledgement of the original. There were many deep, nasty, arguments of the type “this engine is a copy of that engine”. These do not concern us here!)

An important area of generating winning chances is developing moves which are not the First choice or Principal Variation of our main engine. A number of analysis methods do exist which can do this, such as Subtractive or Monte Carlos Analysis but a starting point before all of this is to consider which Engines tend to give “Original” moves.

A note of caution here – “Original” moves are not necessarily “Good” moves but often the suggestion of a different (weaker) engine can help us find the best path forwards from a position. In some cases simply showing the move to an engine will help it find a better line. In other cases much more work is required.

So for the trial to determine originality I consulted the FastGM rating list  http://www.fastgm.de/ on its slowest time control and ran all the engines I had from this list (plus some which I consider to be the best older ones: Gandalf, Junior, Hiarcs…….)

The results are interesting – As you would expect most of the Stockfish variants have a very high score………………………..

So what we are looking at is a heat map – it works by looking down the left-hand column to find the engine we are interested in – we then read across – the similarity in move choice is shown, the higher the score the more similar the moves selected. In general it is better to ignore the numbers and look at the colours – The more red the more similar the choices, the more green the less similar.  The Engines are ordered in a rough ordering of strength – with the ones on the left/at the top being stronger and the ones on the right/bottom being weaker.  The difference between the top and bottom engines is between 300 and 500 elo, depending on which rating list you believe! There were also a few engines which failed to run in the trials – Fizbo and Giraffe. Of these Giraffe is the more interesting as its evaluation is based on a very different method, however the project is currently shelved!

 

We can divide the map into four basic zones:

 

Top Left (The Red Zone)

The strongest engines – many different variants of Stockfish, so it is unsurprising that these engines are fairly similar in outlook. In general engines in this area make poor bedfellows in terms of bringing a different point of view to the table.

 

The Middle (Central Red Zone)

Here we have a lot of engines with similar outlooks. Without going into much detail, a number of these were/are accused of being copies of other engines.

 

The Bottom Corner (Green Zone along the Right and Bottom sides)

These engines tend to come up with different moves from the Top Engines – the downside often being that they’re simply weak rather than original moves and care should be taken in any suggestions.

 

The Interesting Zone (Green-Yellow along the top and down the left hand side)

These are the engines which tend to give a different slant of a position compared to the Top Engines. Particularly interesting are Houdini 4, Boot and Deep Shredder 13 and Andscacs. These engines are not the strongest but are not weak either. Houdini 4 is the oddity of the group as it is “obsolete”, whereas the others are all current.

Anyway If you want a different opinion to HockmodoFish, these engines represent probably the current best mix of originality and playing strength.

 

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Be careful out there!

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