Blackboard Architecture for Detecting and Notifying Failures for Component-Based Unmanned Systems

Shin, Michael; Kang, Taeghyun; Kim, Sung Hoon

doi:10.1007/s10846-017-0677-4

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…The CS selects the appropriate KS according to the event. Many articles on BBS are quite detailed (e.g., Shin et al, 2018).…”

Section: Preliminaries Sdfmentioning

confidence: 99%

ASTS: Autonomous switching of task-level strategies

Wang,

Lv,

Wang

et al. 2023

International Journal of Applied Mathematics and Computer Scien

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Autonomous coordination of multi-agent systems can improve the reaction and dispatching ability of multiple agents to emergency events. The existing research has mainly focused on the reactions or dispatching in specific scenarios. However, task-level coordination has not received significant attention. This study proposes a framework for autonomous switching of task-level strategies (ASTS), which can automatically switch strategies according to different scenarios in the task execution process. The framework is based on the blackboard system, which takes the form of an instance as an agent and the form of norm(s) as a strategy; it uses events to drive autonomous cooperation among multiple agents. A norm may be triggered when an event occurs. After the triggered norm is executed, it can change the data, state, and event in ASTS. To demonstrate the autonomy and switchability of the proposed framework, we develop a fire emergency reaction dispatch system. This system is applied to emergency scenarios involving fires. Five types of strategies and two control modes are designed for this system. Experiments show that this system can autonomously switch between different strategies and control modes in different scenarios with promising results. Our framework improves the adaptability and flexibility of multiple agents in an open environment and represents a solid step toward switching strategies at the task level.

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“…The CS selects the appropriate KS according to the event. Many articles on BBS are quite detailed (e.g., Shin et al, 2018).…”

Section: Preliminaries Sdfmentioning

confidence: 99%

ASTS: Autonomous switching of task-level strategies

Wang,

Lv,

Wang

et al. 2023

International Journal of Applied Mathematics and Computer Scien

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“…Each game agent (which in our simulation environment is a tank game object) has attached a behavior tree controller. The concept of blackboards is widely used to communicate between AI systems [25]. In our implementation of behavior trees, there are two kinds of blackboards:…”

Section: B High Level Usage Of Behavior Trees and Communicationmentioning

confidence: 99%

Automatic Difficulty Management and Testing in Games using a Framework Based on Behavior Trees and Genetic Algorithms

Păduraru

Paduraru

2019

2019 24th International Conference on Engineering of Complex Computer Systems (ICECCS)

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The diversity of agent behaviors is an important topic for the quality of video games and virtual environments in general. Offering the most compelling experience for users with different skills is a difficult task, and usually needs important manual human effort for tuning existing code. This can get even harder when dealing with adaptive difficulty systems. Our papers main purpose is to create a framework that can automatically create behaviors for game agents of different difficulty classes and enough diversity. In parallel with this, a second purpose is to create more automated tests for showing defects in the source code or possible logic exploits with less human effort.Index Terms-behavior tree, genetic algorithms, functional testing, automatic difficulty management.

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