Exploring Types of Planning Techniques in Artificial Intelligence
In the ever-evolving world of Artificial Intelligence (AI), understanding the types of Planning Techniques in AI is fundamental. This article aims to deepen our understanding of these advanced mechanisms that give life to the realm of bits and bytes.
Planning Techniques in AI bare the sequences of actions that intelligent systems utilise to accomplish a specific objective. Navigating the tumultuous stream of constant variables, they are the driving force of various intellectual revolutions1. For anyone seeking to understand the different types of planning techniques in AI, this guide is a perfect starting point.
Diverse Types of Planning Techniques in Artificial Intelligence
Creating AI is akin to establishing culinary masterpieces, where a variety of planning techniques are integral ingredients. This article will shed light on five distinctive methods: Heuristic-Based Search, Decision Trees, State Space Search, Plan Space Planning, and Hierarchical Planning, all of which are vital types of planning techniques in AI3.
Heuristic-Based Search & Decision Trees in AI Planning
No exploration of types of planning techniques in AI would be complete without mention of the Heuristic-Based Search. Fondly referred to as the ‘Sleeping Beauty’ amongst the techniques, it employs forward-chaining methods4. Alongside, we have Decision Trees, the comprehensive map of potential choices in the AI planning process, accompanied by their corresponding results5.
State Space Search, Plan Space Planning & Hierarchical Planning
As we delve deeper into the different types of planning techniques in AI, State Space Searches aid in the investigation of potential states of a problem6. Plan Space Planning, on the other hand, represents a unique discipline within this constellation of methods. Finally, Hierarchical Planning refines complex plans into a hierarchy of manageable sub-plans7.
Adaptability of Planning Techniques in AI and Their Impact
Unique to their kind, the types of planning techniques in AI exhibit chameleon-like adaptability. Executive musicians of their domain, they harmonize with the shifting rhythm of AI applications, thus enhancing the individuality of Artificial Intelligence itself8.
As the realm of Artificial Intelligence grows, so do these planning techniques in AI. Their constant innovation contributes to AI’s evolution and cements them as the solid pillars supporting the intricate structure of AI9.
References
1. Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Malaysia; Pearson Education Limited.
2. Ghallab, M., Nau, D., & Traverso, P. (2004). Automated Planning: Theory and Practice. San Francisco; Elsevier.
3. Georgeff, M. P., & Lansky, A. L. (1987). Reactive reasoning and planning. In Proceedings of the sixth national conference on Artificial intelligence – Volume 2 (pp. 677–682).
4. Pearl, J. (1984). Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley.
5. Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning, 1(1), 81–106.
6. Bonet, B., & Geffner, H. (2001). Planning as heuristic search. Artificial Intelligence, 129(1-2), 5-33.
7. Erol, K., Hendler, J., & Nau, D. S. (1994). HTN Planning: Complexity and Expressivity. In Proceedings of the Twelfth National Conference on Artificial Intelligence.
8. Laird, J. E., & van Lent, M. (2001). Human-level AI’s killer application: Interactive computer games. AI magazine, 22(2), 15-15.
9. Langley, P. (2011). The Changing Science of Machine Learning. Machine Learning, 82(3), 275–279.
