Tugas 3 Kecerdasan Buatan - Solving Problems by Searching? (Uninformed Search & Informed Search)

Описание: Video ini dibuat untuk tujuan edukasi berdasarkan buku Artificial Intelligence: A Modern Approach (Russell & Norvig) dan beberapa publikasi ilmiah yang disebutkan dalam referensi.

Jika terdapat kekeliruan dalam penyampaian istilah, interpretasi diagram, atau penyederhanaan konsep, hal tersebut tidak bermaksud menyalahi isi sumber aslinya.
Koreksi dan masukan dari penonton sangat dihargai.

Berikut jurnal paper yang dipakai:

1. Uninformed
Yoon, D., Jeong, M. & Oh, S. WAVE: designing a heuristics-based three-way breadth-first search on GPUs. J Supercomput 79, 6889–6917 (2023). https://doi.org/10.1007/s11227-022-04934-1

Das, S., Dereniowski, D. & Uznański, P. Energy Constrained Depth First Search. Algorithmica 86, 3759–3782 (2024). https://doi.org/10.1007/s00453-024-01275-8

Ferenczi, A., & Bǎdicǎ, C. (2024). Optimization of IOTA Tangle Cumulative Weight Calculation Using Depth-First and Iterative Deepening Search Algorithms. Vietnam. J. Comput. Sci., 11, 301-321.

Alshammrei, S., Boubaker, S., & Kolsi, L. (2022). Improved Dijkstra Algorithm for Mobile Robot Path Planning and Obstacle Avoidance. Computers, Materials & Continua.

M., Ravi, & Bulo, Y. (2023). An Investigation of the Optimum Power Allocation Technique in the MIMO-NOMA Network with the Deep Neural Network and Depth Limited Search Algorithm. IETE Journal of Research, 70, 1438 - 1448.

Yamín, D., Medaglia, A. L., & Prakash, A. A. (2022). Exact bidirectional algorithm for the least expected travel-time path problem on stochastic and time-dependent networks. Computers & Operations Research, 141, 105671.

2. Informed
Fickert, M., & Hoffmann, J. (2022). Online Relaxation Refinement for Satisficing Planning: On Partial Delete Relaxation, Complete Hill-Climbing, and Novelty Pruning. J. Artif. Intell. Res., 73, 67-115.

Haoxin, L., & Yonghui, Z. (2022). ASL-DWA: An Improved A-star Algorithm for Indoor Cleaning Robots [J]. IEEE Access, 10, 99498-99515.

Lim, D., & Jo, J. (2022). Path planning with the derivative of heuristic angle based on the GBFS algorithm. Frontiers in Robotics and AI, 9, 958930.

Neshat, M., Pourahmad, A. A., & Rohani, Z. (2020). Improving the cooperation of fuzzy simplified memory A* search and particle swarm optimisation for path planning. International Journal of Swarm Intelligence, 5(1), 1-21.

Li, P., Li, Y., & Dai, X. (2024). VNS-BA*: An Improved Bidirectional A* Path Planning Algorithm Based on Variable Neighborhood Search. Sensors, 24(21), 6929.