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Multi-agent high-order belief cognition planner realization method

A multi-agent and implementation method technology, applied in the direction of instruments, computing models, artificial life, etc., can solve problems such as search space expansion and difficulties

Inactive Publication Date: 2017-05-31
SUN YAT SEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This often leads to a dramatic expansion of the search space of algorithms seeking feasible planning
In addition, after introducing the interaction between agents, it will be very difficult to give a natural and feasible semantic definition of how agents update and evolve knowledge.

Method used

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  • Multi-agent high-order belief cognition planner realization method
  • Multi-agent high-order belief cognition planner realization method

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Experimental program
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Embodiment 1

[0029] Such as Figure 1-2 As shown, a multi-agent high-order belief cognitive planner implementation method includes the following steps:

[0030] S1: Input the epddl file, and use the language compiler to convert the epddl file into a formula tree with K-module dynamic words. The epddl file is the number of agents and the premise and effect of actions for the multi-agent cognitive planning problem. Initially An integration document describing the knowledge base and the target;

[0031] S2: Transform the formula tree into CDF paradigm, which represents a knowledge base;

[0032] S3: Input the obtained CDF paradigm as the initial knowledge base into the PrAO planning algorithm, and the PrAO planning algorithm is a multi-agent high-order belief cognitive planner solving algorithm;

[0033] S4: The PrAO planning algorithm uses the data structure of the linked list to build a map and generate a solution.

[0034] In the specific implementation process, in steps S1-S2, use lex+...

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Abstract

The invention provides a multi-agent high-order belief cognition planner realization method. The method comprises the following steps of: inputting an epddl file and converting the epddl file into a formula tree with a k-mode dynamic word through a language compiler; converting the formula tree into a CDF normal form; inputting the obtained CDF normal form into a PrAO planning algorithm by taking the CDF normal form as an initial knowledge base, wherein the PrAO planning algorithm is a multi-agent high-order belief cognition planner solution algorithm; and carrying out mapping by the PrAO planning algorithm through using a data structure of linked list, and generating a solution. The invention discloses a new cognition planning language epddl; and compared with the current mainstream planning language pddl, the epddl language is not only suitable for the condition of relatively simple classical planning, but also has an ability of processing multi-agent cognition planning.

Description

technical field [0001] The invention relates to the field of automatic planning of artificial intelligence based on computing theory, and more specifically, relates to a method for realizing a multi-agent high-order belief cognitive planner. Background technique [0002] Automatic planning is an extremely important branch in the field of artificial intelligence. Its main purpose is to describe entities and actions in actual application scenarios, and automatically generate action trees or action sequences that can achieve a certain goal. To achieve this goal, we need to represent the knowledge base of entities, the premise and effect of actions, and goals through formal methods. On this basis, we can try to experiment with this and implement a correct application planner that can handle a series of planning instances, so as to automatically generate feasible and correct planning solutions to solve various planning problems. [0003] As far as the field of automatic plannin...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/00
CPCG06N3/008
Inventor 黄晓刘咏梅
Owner SUN YAT SEN UNIV