Hierarchical Planning In Artificial Intelligence Ppt

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Hierarchical problem-solving has been used to lower the computational cost of planning since artificial intelligence was first developed. The widely acknowledged principle behind hierarchical problem-solving is to prioritize problems based on their relevance, starting with the most pressing issues. Its fundamental benefit comes from a considerably smaller search space that may be obtained while looking for a strategy by emphasizing some actions while temporarily disregarding others.


Hierarchical planning in artificial intelligence (AI) is a planning approach that involves organizing tasks and actions into multiple levels of abstraction or hierarchy, where higher-level tasks are decomposed into a sequence of lower-level tasks. It provides a way to efficiently reason and plan in complex domains by utilizing a hierarchy of goals and subgoals. Hierarchical planning allows AI systems to make decisions at different levels of abstraction, enabling them to tackle complex tasks and environments effectively.


In hierarchical planning, the high-level task is represented as the ultimate goal, and it is decomposed into subgoals or actions at lower levels of the hierarchy. The hierarchy can be organized as a tree or a directed acyclic graph (DAG), with the high-level goal as the root node and the lowest-level tasks or actions as leaf nodes. Planning can occur at different levels of the hierarchy, with the system selecting goals and generating plans for achieving subgoals or actions. The plans generated at different levels are then synthesized into a cohesive plan for execution.


Hierarchical planning refers to a problem-solving approach that involves breaking down complex tasks into a hierarchical structure of smaller sub-tasks or actions that can be executed by an intelligent agent.


Hierarchical planning in artificial intelligence (AI) involves the use of various techniques to effectively decompose tasks, abstract them at different levels, allocate tasks to appropriate agents or resources, and integrate execution plans. Here's a brief overview of these techniques:


Hierarchical planning in artificial intelligence (AI) is a rapidly evolving field, and several future directions hold promise for advancing the capabilities and applications of hierarchical planning. Some of the potential future directions of hierarchical planning in AI are:


Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches.


In this paper, we present ML-Plan, a new approach for AutoML based on hierarchical task networks (HTNs). HTN is an established AI planning technique (Erol et al. 1994; Nau et al. 2003) usually implemented as a heuristic best-first search over the graph induced by the planning problem. There have been earlier HTN-based approaches to configure data mining pipelines by Kietz et al. (2012) and Nguyen et al. (2014). However, the optimization potential of these techniques is rather limited. In fact, Kietz et al. (2012) ranks candidates based on usage frequencies of RapidMiner, and Nguyen et al. (2014) adopts a hill-climbing approach guided by a database of known problems. In contrast, ML-Plan guides the search by randomly completing partial pipelines like the one

Hierarchical Planning In Artificial Intelligence Ppt

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