Advances in Computer Games: 14th International Conference, by Aske Plaat, Jaap van den Herik, Walter Kosters

By Aske Plaat, Jaap van den Herik, Walter Kosters

This booklet constitutes the completely refereed post-conference proceedings of the 14th overseas convention on Advances in laptop video games, ACG 2015, held in Leiden, The Netherlands, in July 2015.
The 22 revised complete papers awarded have been conscientiously reviewed and selected from 34 submissions. The papers disguise a variety of topics resembling Monte-Carlo Tree seek and its improvements;  theoretical points and complexity; research of online game features; seek algorithms; and computer learning.

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Additional resources for Advances in Computer Games: 14th International Conference, ACG 2015, Leiden, The Netherlands, July 1-3, 2015, Revised Selected Papers (Lecture Notes in Computer Science)

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Alternatively, node initialization (sometimes referred to as priors [3]) initializes the win and visit counts of nodes at the time of their creation with win values that reflect the strength of the node, again determined by the evaluation function. In all three of our programs we have seen that it is necessary to use one of these techniques. In the case of Amabot, progressive widening is used. In fact, since Amabot possesses such a mature and accurate evaluation function and since Amazons allows so many legal moves, especially in the early parts of the game, we push progressive widening a bit further and do some forward pruning.

Playout, policy) end for return the move with the most playouts exp(policy[move]) z 23 24 T. Cazenave Algorithm 4. playouts[m] + c × log(t) value ← w p p if value > bestV alue then bestV alue ← value bestM ove ← m end if end for play (board, bestM ove) player ← opponent (player) res ← UCT (board, player, policy) update t with res else t ← new entry of board in the transposition table res ← playout (board, player, policy) update t with res end if return res – – – – has been solved up to size 6 × 5 using Job Level Proof Number Search [25].

NMCS has been previously successfully adapted to two-player games in a recent work [5]. PPA is a follow-up to this paper since it is the adaptation of NRPA to two-player games. In the context of GGP we look for general enhancements of UCT that work for many games without game-specific tweakings. This is the case for PPA. In our experiments we use the exact same algorithm for all the games. It is usually more difficult to find a general enhancement than a game specific one. PPA is an online learning algorithm, it starts from scratch for every position and learns a position specific playout policy each time.

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