本文提出了一种结合归纳逻辑编程与强化学习的新方法，以提升训练性能与可解释性。我们利用从含噪示例中归纳学习answer set program（回答集程序）的方式，在每一批经验数据中学习一组逻辑规则，作为智能体策略的可解释近似。随后我们基于所学规则执行回答集推理，以指导学习智能体在下一批次的探索，无需低效的奖励塑形，且通过软偏置保持最优性。整个流程在强化学习算法的在线执行过程中完成。我们将该方法集成到Q-learning算法中，在Pac-Man（吃豆人）场景的两张复杂度递增的地图上初步验证了方法的有效性。该方法显著提升了智能体获得的折扣回报，即便在训练的最初几个批次中效果也很明显。此外，归纳学习不会增加Q-learning所需的计算时间，且所学规则能快速收敛为智能体策略的解释。
英文摘要：
This paper presents a novel approach combining inductive logic programming with reinforcement learning to improve training performance and explainability. We exploit inductive learning of answer set programs from noisy examples to learn a set of logical rules representing an explainable approximation of the agent policy at each batch of experience. We then perform answer set reasoning on the learned rules to guide the exploration of the learning agent at the next batch, without requiring inefficient reward shaping and preserving optimality with soft bias. The entire procedure is conducted during the online execution of the reinforcement learning algorithm. We preliminarily validate the efficacy of our approach by integrating it into the Q-learning algorithm for the Pac-Man scenario in two maps of increasing complexity. Our methodology produces a significant boost in the discounted return achieved by the agent, even in the first batches of training. Moreover, inductive learning does not compromise the computational time required by Q-learning and learned rules quickly converge to an explanation of the agent policy.
Answer Probing-Guided Search for Diverse Solution Exploration of LLMs
University of Science and Technology of China（中国科学技术大学）；Zhongguancun Academy（中关村学院）；Alibaba Group（阿里巴巴集团）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）
SPADER: Step-wise Peer Advantage with Diversity-Aware Exploration Rewards for Multi-Answer Question Answering
State Key Lab of CAD&CG, Zhejiang University（浙江大学CAD与CG国家重点实验室）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；School of Software Technology, Zhejiang University（浙江大学软件技术学院）
Explaining Neural Networks in Preference Learning: a Post-hoc Inductive Logic Programming Approach
Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, Italy（意大利拉奎拉大学信息工程、计算机科学与数学系）；Department of Life, Health and Environmental Sciences, University of L'Aquila, Italy（意大利拉奎拉大学生命、健康与环境科学系）；Department of Electrical Engineering and Information Technologies, University of Naples, Italy（意大利那不勒斯大学电气工程与信息技术系）；Ethos Group, Department of Human Sciences, University of Verona, Italy（意大利维罗纳大学人文科学系Ethos研究组）
Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning
Artificial Intelligence Research Center, Chang Gung University（长庚大学人工智能研究中心）；Graduate Institute of Oral Biology, National Taiwan University（国立台湾大学口腔生物学研究所）；National Taiwan University of Science and Technology（国立台湾科技大学）
Best-Effort Inductive Logic Programming via Fine-grained Cost-based Hypothesis Generation
Technische Universität Wien（维也纳技术大学）；Marmara University（马尔马拉大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Sabanci University（萨班哲大学）
本文介绍了参加第一届归纳逻辑程序设计（ILP）竞赛的计算机系统。它基于Answer Set Programming（ASP）。假设空间生成的ASP编码：给定一组表示模式偏差的事实和一组成本配置参数，该编码的每个答案集表示一个规则，该规则被认为是用于找到需要给定示例的假设。与使用规则体的长度作为规则复杂性度量的最新方法相比，我们的方法允许对假设候选规则的形状进行更细粒度的规范。系统迭代地增加规则成本限制，从而增加搜索空间，直到找到合适的假设。该系统搜索一个假设，需要一个单一的例子，利用ASP编码从XHAIL中使用的编码派生。我们用ILP竞赛的开发和测试集进行实验。为了比较，我们还适应了ILASP系统处理竞争的情况下。实验结果表明，假设搜索空间的代价参数是在有限的资源范围内找到竞争实例的假设的重要因素。
MIRAGE: Evaluating and Explaining Inductive Reasoning Process in Language Models
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Reasoning Can Hurt the Inductive Abilities of Large Language Models
School of Information Sciences University of Illinois at Urbana-Champaign（信息科学学院伊利诺伊大学厄巴纳-香槟分校）；Computer Science and Engineering HKUST（计算机科学与工程香港科技大学）；Research Scientist, Intel Labs（英特尔实验室研究员）；School of Information Sciences University of Illinois Urbana-Champaign（信息科学学院伊利诺伊大学厄巴纳-香槟分校）
