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About 4,643 papers for “Pathfinding”

Pathfinding

Ian Millington, John Funge · 2018

Cited by 0

Pathfinding

Nayland Blake

Three small children; Published: [London]; Issued by the National War Savings Committee, Eyre and Spottiswoode

Cited by 0

Axon pathfinding

Strochlic, L., Weinl, C., Piper, M. et al. · Queensland's institutional digital repository (The University of Queensland) · 2010

Cited by 0

Cooperative Pathfinding

David Silver · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · 2005

Cooperative Pathfinding is a multi-agent path planning problem where agents must find non-colliding routes to separate destinations, given full information about the routes of other agents. This paper presents three new algorithms for efficiently solving this problem, suitable for use in Real-Time Strategy games and other real-time environments. The algorithms are decoupled approaches that break down the problem into a series of single-agent searches. Cooperative A* (CA*) searches space-time for a non-colliding route. Hierarchical Cooperative A* (HCA*) uses an abstract heuristic to boost performance. Finally, Windowed Hierarchical Cooperative A* (WHCA*) limits the space-time search depth to a dynamic window, spreading computation over the duration of the route. The algorithms are applied to a series of challenging, maze-like environments, and compared to A* with Local Repair (the current video-games industry standard). The results show that the new algorithms, especially WHCA*, are robust and efficient solutions to the Cooperative Pathfinding problem, finding more successful routes and following better paths than Local Repair A*

Cited by 656Open access

Ontological Pathfinding

Yang Chen, S. Goldberg, Daisy Zhe Wang et al. · 2016

Recent years have seen a drastic rise in the construction of web-scale knowledge bases (e.g., Freebase, YAGO, DBPedia). These knowledge bases store structured information about real-world people, places, organizations, etc. However, due to limitations of human knowledge and information extraction algorithms, these knowledge bases are still far from complete. In this paper, we study the problem of mining first-order inference rules to facilitate knowledge expansion. We propose the Ontological Pathfinding algorithm (OP) that scales to web-scale knowledge bases via a series of parallelization and optimization techniques: a relational knowledge base model to apply inference rules in batches, a new rule mining algorithm that parallelizes the join queries, a novel partitioning algorithm to break the mining tasks into smaller independent sub-tasks, and a pruning strategy to eliminate unsound and resource-consuming rules before applying them. Combining these techniques, we develop the first rule mining system that scales to Freebase, the largest public knowledge base with 112 million entities and 388 million facts. We mine 36,625 inference rules in 34 hours; no existing approach achieves this scale.

Cited by 47Open access

Axonal Pathfinding

Mortimer, D., Goodhill, G. J. · Queensland's institutional digital repository (The University of Queensland) · 2009

Cited by 0

pathfinding and

Vasanta Subramanian, Benedict Crabtree, K. Ravi Acharya · 2007

Human angiogenin is a neuroprotective factor and amyotrophic lateral sclerosis associated angiogenin variants affect neurite extension/

Cited by 0

Smart Pathfinding

AVERSA, DAVIDE, VASSOS, STAVROS · IRIS Research product catalog (Sapienza University of Rome) · 2016

Summary of the work on "Smart Pathfinding". Two different ways to expand videogame path planning with deliberative capabilities such as solving limited planning problems or taking into account the lack of knowledge on the state of the map.

Cited by 0

Emotional Pathfinding

Toby Donaldson, Andrew Park, I‐Ling Lin · Lecture notes in computer science · 2004

Cited by 2

AI and Pathfinding

Sheikh Sohel Moon · Apress eBooks · 2025

AI (artificial intelligence) and pathfinding are integral components of modern games, enabling non-player characters (NPCs) to navigate, react, and interact with the game world in intelligent and dynamic ways. From basic movement algorithms to complex decision-making processes, AI systems breathe life into games by creating challenging enemies, helpful companions, and engaging environments.

Cited by 0

Pathfinding in Games

Adi Botea, Bruno Bouzy, Michael Buro et al. · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2013

Commercial games can be an excellent testbed to artificial intelligence (AI) research, being a middle ground between synthetic, highly abstracted academic benchmarks, and more intricate problems from real life. Among the many AI techniques and problems relevant to games, such as learning, planning, and natural language processing, pathfinding stands out as one of the most common applications of AI research to games. In this document we survey recent work in pathfinding in games. Then we identify some challenges and potential directions for future work. This chapter summarizes the discussions held in the pathfinding workgroup.

Cited by 17Open access

Biased Cost Pathfinding

Alborz Geramifard, Pirooz Chubak, Vadim Bulitko · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · 2006

In this paper we introduce the Biased Cost Pathfinding (BCP) algorithm as a simple yet effective meta-algorithm that can be fused with any single-agent search method in order to make it usable in multi-agent environments. In particular, we focus on pathfinding problems common in real-time strategy games where units can have different functions and mission priorities. We evaluate BCP paired with the A* algorithm in several game-like scenarios. Performance improvement of up to 90% is demonstrated with respect to several metrics.

Cited by 30Open access