Gain insights into path cost, solving time, and memory allocation, revealing algorithmic behaviors in diverse grid settings.

Explore the conclusions drawn from an evaluation of heuristic search algorithms in pathfinding tasks, focusing on solving time, memory consumption and path cost

Gain insights into path cost, solving time, and memory allocation, revealing algorithmic behaviors in diverse grid settings.

Explore the performance of heuristic search algorithms like D*, D* Lite, LPA*, LRTA*, RTAA*, and ARA* in different grid environments.

This section discusses the state-of-the-art in path planning algorithms for autonomous agents, exploring real-time and incremental search approaches.

This paper presents a comprehensive evaluation of heuristic search algorithms in autonomous systems and robotics, focusing on pathfinding.

Explore how multi-agent systems evolved through key computing trends like ubiquity, intelligence, and interconnection, shaping the future of automation.

Explore the future of warfare, where autonomous systems like robot dogs, drones, and unmanned ground vehicles work alongside enhanced human soldiers.

Explore how human-machine systems influence competition, cooperation, contagion, and decision-making, revealing patterns in collective social outcomes.

High-frequency trading algorithms enhance market efficiency but may amplify volatility and reduce diversity, risking financial stability.

Advancing human-machine research with agent-based models, ethical experiments, and a sociological perspective before AI complexity outpaces understanding.

AI design must enhance trust, adapt to cultural contexts, and embrace complexity for resilient, ethical, and efficient human-machine systems.

Machines enhance human systems with efficiency and persistence but can also trigger instability, confusion, and polarization in social interactions.

Exploring human-machine social systems to address challenges like misinformation, market crashes, and road safety for a better, connected future.

Explore the dynamics of human-machine interactions, highlighting behavioral patterns, cognitive biases, and the evolving social roles of autonomous machines.

AI policy must prioritize diversity, resilience, and human-machine co-evolution to prevent systemic risks and adapt to cultural and societal changes.