Symbolic regression (SR) has emerged as a pivotal technique for uncovering the intrinsic information within data and enhancing the interpretability of AI models. However, current state-of-the-art (sota) SR methods struggle to perform correct recovery of symbolic expressions from high-noise data. To address this issue, we introduce a novel noise-resilient SR (NRSR) method capable of recovering expressions from high-noise data. Our method leverages a novel reinforcement learning (RL) approach in conjunction with a designed noise-resilient gating module (NGM) to learn symbolic selection policies. The gating module can dynamically filter the meaningless information from high-noise data, thereby demonstrating a high noise-resilient capability for the SR process. And we also design a mixed path entropy (MPE) bonus term in the RL process to increase the exploration capabilities of the policy. Experimental results demonstrate that our method significantly outperforms several popular baselines on benchmarks with high-noise data. Furthermore, our method also can achieve sota performance on benchmarks with clean data, showcasing its robustness and efficacy in SR tasks.

Deep reinforcement learning (DRL) has emerged as a leading technique for designing AI-bots in the gaming industry. However, practical implementation of DRL-trained bots often encounter two significant challenges: improving strength and diversifying strategies to satisfy player expectations. We observe that the strength of AI-bots are intrinsically tied to the diversity of emerged strategies. Considering this relationship, we introduce diversity is strength (DIS), a novel DRL training framework capable of concurrently training multiple types of AI-bots for adversarial games. These bots are interconnected through an elaborated history model pool (HMP) structure, thereby improving their strength and strategy diversity to tackle the aforementioned challenges. We further devise a model evaluation and sampling scheme to form the HMP, identify superior models, and enrich the model strategies. The DIS can generate diverse and reliable strategies without the need for human data. This method is validated by achieving first-place finishes in two AI competitions based on complex adversarial games, including Google Research Football and Olympic Games. Experiments demonstrate that bots trained using DIS attain an excellent performance and plentiful strategies. Specifically, diversity analysis demonstrates that the trained bots possess a wealth of strategies, and ablation studies confirm the beneficial impact of the designed modules on the training process.

In this research, systems, libraries, and databases for modeling different product development life-cycle aspects are distributed at different locations that are linked through the Web. Product modeling libraries and databases at different places, described by class features and instance features, are associated by defining their relations. Product modeling systems at different locations for manipulating different product development life-cycle activities are integrated by using the system components at one location as the components at other locations.