Method for synthesizing random target plan tree based on control variables

Abstract

The invention relates to a method for synthesizing a random target plan tree based on control variables, and the method comprises the steps: constructing a tree-shaped topological structure based on targets, plans and actions, and carrying out the updating of the tree-shaped topological structure based on the variation of variables, and obtaining the environment state of the target, the pre-condition of the plan corresponding to the target and the pre-condition and the post-condition of each execution step in each plan. The target plan tree has rationality, flexibility and authenticity, the topological structure of the target plan tree can be customized and controlled by a user, and the condition of each node in the generated target plan tree and the association between the nodes accord with the intelligent agent plan; according to the method, the test of the intelligent agent represented based on the target plan tree can be carried out in a unified environment, so that evaluation and comparison of different intelligent agent programs are facilitated; the synthesized target plan tree does not aim at a certain specific domain type, has generality, and can be synthesized in a large quantity according to parameters specified by a user, so that the intelligent agent program can be conveniently and comprehensively tested.

Description

technical field [0001] The invention relates to the technical field of a computer system based on a specific calculation model, in particular to a method for synthesizing a random target planning tree based on control variables. Background technique [0002] With the rapid development of the field of artificial intelligence in recent years, more and more people pay more and more attention to the intelligence of computing systems. A multi-agent system (Multi-Agent System, MAS) refers to a system that realizes complex intelligence through interaction and collaboration between agents, and is used to solve problems that are difficult or impossible for a single agent to solve. It is used in important fields such as urban transportation and industrial manufacturing. have wide application. [0003] According to the different internal decision-making mechanism of the agent, it can be divided into a variety of architectures, including deductive reasoning-based architecture, reactive...

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