Tagged articles

Control

6 articles · Page 1 of 1

Jul 4, 2026 · Artificial Intelligence

How Ontology‑Driven Architecture Enables Controllable AI Agents

The article analyzes the limitations of current Agent‑centric AI solutions and proposes an ontology‑driven “Harness Engineering” framework that embeds business rules directly into the semantic layer, providing architecture constraints, context engineering, and feedback loops to achieve safe, auditable, and business‑controllable agent execution.

AI AgentControlKnora

0 likes · 18 min read

How Ontology‑Driven Architecture Enables Controllable AI Agents

Data Party THU

Jun 29, 2026 · Artificial Intelligence

Ensuring Safety in Real-World Reinforcement Learning: Tsinghua’s Safe Exploration Equilibrium Mechanism

The article reviews a Tsinghua University paper published in IEEE TPAMI 2026 that introduces a Safe Exploration Equilibrium (SEE) framework for real‑world reinforcement learning, proving convergence to a safety equilibrium, detailing a two‑step algorithm, and validating it on three classic control tasks with zero constraint violations and rapid region expansion.

ControlEquilibriumReal-World RL

0 likes · 8 min read

Ensuring Safety in Real-World Reinforcement Learning: Tsinghua’s Safe Exploration Equilibrium Mechanism

Didi Tech

Jan 27, 2021 · Artificial Intelligence

Addressing Uncertainty in Autonomous Driving: Data‑Driven Control Module Strategies

The article proposes a three‑layer, data‑driven framework—problem analysis using massive fleet data, iterative deep‑learning algorithm development with fallback and explainable‑AI safeguards, and systematic validation via simulation and real‑world tests—to mitigate perception, prediction, and control uncertainties and advance trustworthy autonomous‑driving control systems.

ControlData-DrivenPerception

0 likes · 12 min read

Addressing Uncertainty in Autonomous Driving: Data‑Driven Control Module Strategies

DataFunTalk

Feb 27, 2020 · Artificial Intelligence

Technical Challenges in Planning and Control for Autonomous Heavy Trucks

The article reviews the complex system model of autonomous heavy trucks, outlines traditional and modern planning and control methods—including rule‑based FSM, POMDP, learning‑based and optimization techniques—highlights safety, efficiency, fuel‑economy, and dynamic modeling challenges specific to heavy‑truck and trailer configurations, and shares practical attempts such as lane‑changing, merging, and trailer‑aware trajectory planning.

ControlPOMDPPlanning

0 likes · 13 min read

Technical Challenges in Planning and Control for Autonomous Heavy Trucks

DataFunTalk

Nov 8, 2019 · Artificial Intelligence

Balancing Safety and Comfort in Autonomous Driving: Planning and Control Optimization

This article explores how autonomous driving systems can simultaneously ensure safety and passenger comfort by optimizing planning and control modules, defining safety and comfort metrics, formulating constraints and cost functions, and employing models such as the bicycle model for lateral and longitudinal control.

ControlPlanningSafety

0 likes · 12 min read

Balancing Safety and Comfort in Autonomous Driving: Planning and Control Optimization

Tianxing Digital Tech User Experience

Dec 14, 2018 · Product Management

Boost User Experience by Preserving the Sense of Control

The article explains why users feel motion sickness when they lack control, outlines six practical methods—eliminating uncertainty, providing timely feedback, avoiding learned helplessness, tracking processes, creating ritualistic cues, and offering alternative control—to preserve users' sense of control and enhance overall product experience.

ControlProduct designUX

0 likes · 7 min read

Boost User Experience by Preserving the Sense of Control