the Model Predictive Game
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Related Articles from SNS
Strategizing at Speed: A Learned Model Predictive Game for Multi-Agent Drone Racing
arXiv:2602.06925v2 Announce Type: replace Abstract: Autonomous drone racing pushes the boundaries of high-speed motion planning and multi-agent strategic decision-making. Success in this domain requires drones not only to navigate at their limits but also to anticipate and counteract competitors' actions. In this paper, we study a fundamental question that arises in this domain: how deeply should an agent strategize before taking an action?
Predicting every game of the entire World Cup: All...
Everyone is using artificial intelligence to do, well, everything. With the World Cup starting on June 11, you can't scroll for more than a couple of minutes without hitting another post or video or reel of someone telling you how they used AI to predict the World Cup. So, I decided to use my own supercomputer to predict every game of the 2026 World Cup -- the supercomputer is called "my brain."
AI Level of Detail: Distance-Aware ML Model Precision Selection for Real-Time Human Motion Prediction in Games
Announce Type: new Abstract: Modern game engines spend significant compute animating NPCs with learned motion models. This paper proposes AI Level of Detail (AI LOD), a framework in which machine learning inference precision is adapted based on the distance between each NPC and the player camera. The core idea mirrors classical geometry LOD: substitute a cheaper approximation where the difference is imperceptible.
ChessMimic: Per-Rating Transformer Models for Human Move, Clock, and Outcome Prediction in Online Blitz Chess
arXiv:2606.04473v1 Announce Type: new Abstract: We present ChessMimic, a system of three small encoder-only transformers - for move, thinking-time, and outcome prediction - conditioned on the position, recent move history, player rating, and clock state. We fit a separate instance of each model per 100-Elo rating band, trading parameter efficiency for sharper per-skill calibration. On a held-out month-wide slice of Lichess Rated Blitz games ChessMimic's human move prediction accuracy...
Explaining a probabilistic prediction on the simplex with Shapley compositions
arXiv:2408.01382v3 Announce Type: replace Abstract: Originating in game theory, Shapley values are widely used for explaining a machine learning model's prediction by quantifying the contribution of each feature's value to the prediction. This requires a scalar prediction as in binary classification, whereas a multiclass probabilistic prediction is a discrete probability distribution, living on a multidimensional simplex. In such a multiclass setting the Shapley values are typically computed...
I Put a Datacenter GPU in My Gaming PC for £200
I Put a Datacenter GPU in My Gaming PC for £200 I already had an RTX 4080. Good enough for gaming, not good enough for the models I wanted to run locally. The next step up in GPU land is either spend a fortune on a card with more VRAM, or find another way.
Representational Similarity and Model Behavior in Multi-Agent Interaction
Announce Type: new Abstract: Researchers have shown that neural similarity among humans predicts social closeness and cooperative success, whereas innovation often emerges from interactions among dissimilar individuals. We investigate whether these principles extend to artificial intelligence by examining interactions between large language models. In our experiments, 276 model pairs interact across eight games spanning both cooperation and novelty.
Certificates without Electrons? Theory and Evidence on Impacts from AI-Driven Power Demand
arXiv:2606.00811v1 Announce Type: cross Abstract: Data centers now account for 4.4% of United States electricity demand, yet the grid-level effectiveness of the renewable energy certificates (RECs) and power purchase agreements (PPAs) hyperscalers use to claim carbon neutrality remains unclear. We develop a game-theoretic model in which a data center operator chooses among RECs, PPAs, and behind-the-meter colocation while generators make entry decisions under endogenous financing costs. The...
When Does Predictive Inverse Dynamics Outperform Behavior Cloning?
arXiv:2601.21718v2 Announce Type: replace Abstract: Behavior cloning (BC) is a practical offline imitation learning method, but it often fails when expert demonstrations are limited. Recent works have introduced a class of architectures named predictive inverse dynamics models (PIDM) that combine a future state predictor with an inverse dynamics model. While PIDM often outperforms BC, the reasons behind its benefits remain unclear.
CARVE-Q: Quantum-Proposed, Classically Certified Interactive Driving Repair
arXiv:2606.06531v1 Announce Type: new Abstract: The critical question after a correct driving veto is not only whether a maneuver is unsafe, but whether the blocked interaction admits a lawful, auditable, and responsibility-bounded repair. Prediction and game-theoretic planners can suggest plausible cooperation, yet they do not return a proof that the repair respects hard rules, right-of-way, cost allocation, and ego fallback. We introduce CARVE, Certified Affordable Repair of Vetoed...