3 accepted papers
Large Language Model (LLM) agents are reshaping the game industry, by enabling more intelligent and human-preferable characters. Yet, current game benchmarks fall short of practical needs: they lack evaluations of diverse LLM capabilities across various game genres, studies of agentic modules crucia…
Predicting the future motion of traffic agents is crucial for safe and efficient autonomous driving. To this end, we present PredictionNet, a deep neural network (DNN) that predicts the motion of all surrounding traffic agents together with the ego-vehicle's motion. All predictions are probabilistic…
An autonomous robot team can be employed for continuous and strategic coverage of arbitrary environments for different missions. In this work, we propose an anytime approach for creating multi-robot patrolling policies. Our approach involves a novel extension of Monte Carlo Tree Search (MCTS) to all…