在分层操作模型中整合行动、规划和学习

Integrating Deep Reinforcement and Supervised Learning to Expedite Indoor Mapping

The challenge of mapping indoor environments is addressed. Typical heuristic algorithms for solving the motion planning problem are frontier-based methods, that are especially effective when the environment is completely unknown. However, in cases where prior statistical data on the environment's architectonic features is available, such algorithms can be far from optimal. Furthermore, their calculation time may increase substantially as more areas are exposed. In this paper we propose two means by which to overcome these shortcomings. One is the use of deep reinforcement learning to train the…

Purposeful and Operation-based Cognitive System for AGI

This paper proposes a new cognitive model, acting as the main component of an AGI agent. The model is introduced in its mature state, and as an extension of previous models, DENN, and especially AKREM, by including operational models (frames/classes) and will. In addition, it is mainly based on the duality principle in every known intelligent aspect, such as exhibiting both top-down and bottom-up model learning, generalization verse specialization, and more. Furthermore, a holistic approach is advocated for AGI designing and cognition under constraints or efficiency is proposed, in the form of…

智能人机协作制造：通过仿真驱动的知识图谱与大语言模型协作提升仓库规划

Intelligent Human-Machine Partnership for Manufacturing: Enhancing Warehouse Planning through Simulation-Driven Knowledge Graphs and LLM Collaboration

本研究提出通过仿真驱动的知识图谱与大语言模型协作，提升制造业仓库规划的智能化水平，实现人机高效协作以优化运营决策。

将符号RL规划整合到基于BDI的自主无人机框架中：系统集成与SIL验证

Integrating Symbolic RL Planning into a BDI-based Autonomous UAV Framework: System Integration and SIL Validation

本研究提出AMAD-SRL框架，整合符号RL与BDI方法，通过SIL验证提升无人机任务效率75%。

风险调整的智能红利：一种量化框架，用于衡量人工智能投资回报，整合ISO 42001和监管暴露

The Risk-Adjusted Intelligence Dividend: A Quantitative Framework for Measuring AI Return on Investment Integrating ISO 42001 and Regulatory Exposure

本文提出一种量化框架，用于衡量AI投资回报，整合ISO 42001和监管暴露，以更准确评估AI项目风险与收益。

将运动学和环境背景集成到深度反向强化学习中以预测越野车辆轨迹

Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories

从第三人称的角度预测移动代理的运动是许多机器人应用的重要组成部分，如自主导航和跟踪。通过对其他智能体的准确运动预测，机器人可以计划更智能的行为来实现指定的目标，而不是以纯粹的反应方式行事。以前的工作解决运动预测，要么只过滤运动学，或使用手工设计和学习的环境表示。我们没有将运动学背景和环境背景分开，而是提出了一种新的方法，将两者整合到反向强化学习（IRL）框架中进行轨迹预测。而不是指数增加的状态-空间复杂度与运动学，我们提出了一个两阶段的神经网络架构，考虑运动和环境一起恢复的奖励函数。第一阶段网络使用低级LiDAR统计数据学习环境的特征表示，第二阶段网络将这些学习的特征与运动学数据相结合。我们收集了超过30公里的越野驾驶数据，并通过实验验证了我们的方法可以有效地提取有用的环境和运动学特征。我们生成车辆未来轨迹分布的准确预测，编码复杂的行为，例如道路交叉口的多模态分布，甚至根据车辆的速度在同一交叉口显示不同的预测。