Rule-based bots follow programmer-written conditions and strategy logic; machine-learning agents use data or experience to fit or adjust their decisions. Neither label guarantees a tournament result, and many bots combine both approaches. To compare them fairly, look beyond win rate: the game rules, maps, opponent set, bot versions, evaluation period, and runtime limits all shape the outcome.
What the labels mean—and where they overlap
Rule-based bots
A rule-based bot maps the game state it observes to actions using explicit conditions, scripts, build orders, heuristics, or strategy parameters. Developers can encode known strategic and tactical knowledge directly, and the resulting logic may be easier to inspect. Its performance depends on the coverage and quality of those rules; brittle logic can fail when a match reaches a situation its developers did not anticipate. Historical competition literature discusses strategies parameterized for future games, and SSCAIT listings include bots described as rule-model based. The StarCraft AI wiki’s historical overview is secondary context; listings are bot descriptions, not architecture audits.
Machine-learning agents
A machine-learning agent uses data or experience to estimate actions, values, or policies. Reinforcement learning is one family of methods, not a synonym for all machine learning. Learning may produce behavior beyond a fixed set of authored responses, but results depend on training conditions, reward design, data, computing resources, and how closely training matches tournament play.
Hybrid bots
The distinction is a spectrum, not a clean division between two kinds of competitor. A bot can use hand-authored strategic structure alongside learned components. SSCAIT’s results page includes a bot that describes a new machine-learning module, while its current list also includes one described as based on a rule model. Those self-descriptions show that both approaches appear in the competition ecosystem; they do not establish independently verified, mutually exclusive categories. SSCAIT results
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What published tournament evidence shows
One frequently useful historical data point comes from the authors of LastOrder. In a 2018 paper, they report an 83% win rate for their deep reinforcement-learning system against the 28-bot set from the AIIDE 2017 StarCraft competition, and say it outperformed 26 of the 28 entrants in that evaluation. The paper studies reinforcement learning for macro-action selection; it does not establish that every decision in the bot was learned. LastOrder paper
That result is evidence that one learning-based approach performed strongly against one historical opponent set. It is not LastOrder’s current ladder win rate, nor a controlled general comparison proving that machine-learning bots outperform rule-based bots. The available competition results do not isolate architecture as the cause of performance. Rankings mix versions, opponents, and conditions, so they cannot by themselves answer that causal question. SSCAIT results
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Why competition rules can change the comparison
SSCAIT: a specific Brood War environment
SSCAIT describes itself as a public ladder and yearly tournament. Its published rules specify 1v1 Melee games in StarCraft: Brood War 1.16.1, with maps selected randomly from its map pool. Full map vision and cheats are forbidden. The rules also describe losses for losing all buildings, crashing, or slowing the game beyond stated frame-time limits. These conditions make reliability and execution speed part of competitive performance, not just implementation details. SSCAIT rules
Under those rules, a game can end after 90 in-game minutes (86,400 frames) or five real-world minutes without a unit dying. The rules assign a timeout result using the in-game kills-plus-razings score. SSCAIT states: “Draw results are no longer possible.” A bot’s ability to finish games within the allowed conditions can therefore affect its record independently of how well its strategy handles a typical match. SSCAIT rules
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AIIDE: check the edition
AIIDE’s historical overview says its competition has recurred since 2010 and characterizes its emphasis as AI rather than coding build orders. The organizer’s page provides edition-specific rules and registration information for 2026. Those current-edition details should not be inferred from the 2017 opponent set used in the LastOrder paper—or from another year’s format. Check the organizer’s page for the edition you plan to enter. AIIDE competition history · AIIDE 2026 organizer page
How to compare two bots fairly
A win-rate comparison is interpretable only when the test conditions are clear. For a useful head-to-head or evaluation, hold the following constant or report any differences:
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- Game and rules: Identify StarCraft: Brood War or StarCraft II, the game version, and the competition rules. Do not merge results across games or formats.
- Maps and races: State the map pool, map selection method, and races tested.
- Opponents: Name the opponent pool and clarify whether it is a fixed historical set or a changing ladder.
- Versions and period: Record each bot version and the dates or evaluation period; bots can change over time.
- Scoring and sample: Report the number of games and scoring method alongside the win percentage.
- Reliability and resources: Distinguish strategic strength from crashes, timeouts, runtime limits, and computation or training costs.
- Architecture: Describe learned and hand-authored components rather than forcing a hybrid into a binary category.
These details help separate several different questions: which bot won under a particular setup, which one adapts better to unfamiliar opponents, which is more robust under runtime constraints, and which approach is easier to inspect or maintain. One leaderboard position cannot answer all of them.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Entering a competition: use its current requirements
SSCAIT’s rules page asks tournament entrants to submit source code and a compiled bot. It lists C++, Java, BWAPI, and some compatible wrappers as supported approaches, and encourages terrain-analysis libraries such as BWTA or similar tools. The page, as accessed, lists supported BWAPI versions and a 32-bit Windows 7 execution environment; treat these as SSCAIT’s published requirements, not universal or timeless requirements for StarCraft bot competitions. Check the organizer’s current rules before preparing an entry. SSCAIT rules
Quick Recap
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