Challenge your understanding of how artificial intelligence approaches planning and resource management within strategy games. This quiz explores concepts like adaptive planning, economic modeling, opponent prediction, and multi-agent coordination in AI-driven gameplay.
In a real-time strategy game, which approach allows an AI to adapt its strategy when an opponent unexpectedly switches to a defensive stance?
Explanation: Dynamic replanning enables AI to adjust its strategy on-the-fly in response to changes, such as an opponent becoming defensive. Fixed action sequences and static tree evaluation are less flexible, following pre-set actions or evaluations and missing sudden shifts. Randomized tactics can introduce variety but do not guarantee appropriate responses to specific opponent behavior.
Which AI reasoning technique helps an agent prioritize building farms over military units when resources are low in a turn-based strategy scenario?
Explanation: Utility-based decision making allows the AI to weigh options and choose actions with the highest overall benefit, such as prioritizing farms for resource regeneration. Blind search explores possibilities without evaluating their value, so it's inefficient here. Serial monotoning is not a standard technique in this context. Heuristic overloading refers to misusing heuristic methods, which can lead to poor choices.
How can an AI in a strategy game improve its planning by simulating and predicting an opponent’s likely next moves after spotting a scouting unit?
Explanation: Opponent modeling refers to the AI’s ability to infer and predict an adversary’s strategy based on observed behavior, like encountering a scouting unit. Coin flipping adds randomness, not planning. Pattern misrecognition implies the AI is analyzing incorrectly. Rule blinding ignores established game rules and patterns, making predictions inaccurate.
In a game where several AI agents must share a limited pool of resources, which method best ensures fair and efficient distribution between them?
Explanation: Multi-agent coordination allows AI agents to negotiate and allocate resources equitably, improving overall team efficiency. Single-path routing pertains to movement, not resource sharing. Greedy grasping lets agents prioritize their own needs, leading to imbalance. Manual balancing involves pre-set divisions, which may not adapt to changing circumstances.
When an AI must plan resource usage without full information about opponent positions, which approach helps manage the uncertainty and still make effective decisions?
Explanation: Probabilistic reasoning enables the AI to make informed guesses and adapt to incomplete data, which is valuable when opponent positions are unknown. Exhaustive enumeration searches all possibilities, which is unfeasible in complex games. Deterministic mapping ignores uncertainty by assuming fixed outcomes. Predictive resolving is not an established technique and is too vague for this context.