Our research investigates how two adaptive AI methods, evolutionary transfer learning and TD(lambda), perform in the three-dimensional chess environment Dragonchess. The game challenges players with its unique board structure and computational load, making it an ideal setting to study how adaptive methods can update evaluation heuristics in novel environments. In this work we re-implement the Dragonchess engine, changing it from a PyGame engine to C++. This enables faster gameplay, allowing us to run 10,000 games with confidence intervals and significance tests, rather than a single small tournament. Both adaptive methods outperform all other agents in the round-robin tournament. Our results showed that there is no significant difference in the performance between the evolved and learned evaluations. This research establishes the efficacy of adaptive methods in structurally complex, novel game domains.
Figures & tables
Figure 1: Each layer of the board (Sky, Ground, and Underworld) consists of 8 rows and 12 columns, represented by integer indices from 0 to 287. At any given index of the array, an integer constant is stored, representing piece type and ownership. These constants are positive for Gold, and negative for Scarlet. This is the same structure as the original Dragonchess implementation [ 10 ] .
Figure 2: Screenshot from our Dragonchess engine’s graphical interface built using C++. Dragonchess’ characteristic three-layered board can be seen with the Sky (top), Land (middle), and Underworld (bottom)—with distinct piece types and clear depiction of multi-layer interactions and movements unique to Dragonchess. This is the same GUI as in the original PyGame implementation [ 10 ] .
Agent
W
L
D
Score
Elo
CMA-ES (evolved, original)
2131
615
1254
0.690
1678.4
TD( λ ) self-play (learned)
2314
1088
598
0.653
1653.1
AlphaBeta- d2 (material search)
1932
1380
688
0.569
1595.2
Jackman (handcrafted)
1889
1374
737
0.564
1592.0
Random
47
3856
97
0.024
981.3
Table 1: Depth-matched (AlphaBeta d=2 ) round-robin standings, 1000 games per pair, color-balanced. Elo by Bradley-Terry MLE (draws as 0.5), mean anchored to 1500.
Figure 3: Bar chart visualization of the Bradley-Terry Elo round-robin standings detailed in Table 1 .
A
B
A rate
95% CI
TD( λ )
CMA-ES
0.503
[0.463, 0.544]
TD( λ )
Jackman
0.601 ∗
[0.569, 0.632]
TD( λ )
AlphaBeta
0.560 ∗
[0.528, 0.591]
TD( λ )
Random
0.982 ∗
[0.972, 0.989]
CMA-ES
Jackman
0.743 ∗
[0.706, 0.777]
CMA-ES
AlphaBeta
0.758 ∗
[0.723, 0.790]
Table 2: Head-to-head decisive win rates (draws excluded) with 95% Wilson confidence intervals. Search depth fixed at 2.
Jun 24, 2026·Tianyuan Zhou, Zhizheng Fu, Tianming YangChess
School of Intelligence Science and Technology Nanjing University Suzhou, China · Center for Excellence in Brain Science and Intelligence Technology Institute of Neuroscience Chinese Academy of Sciences Shanghai, China