Multi-agent blackboard architecture for a mobile robot

2016

Expert Systems

The blackboard architecture has particular utility in applications where conclusions must be drawn in the absence of human involvement. Through properly crafted rules, this approach can determine what a collection of data may mean. This can be utilized by other onboard software to limit transmission to only relevant data or conclusions. Some applications, such as robotic exploration, may preclude or severely limit the availability of controller‐to‐craft‐agent communications. In this case, the maintenance of the system must be performed autonomously.This paper discusses common maintenance tasks that may be relevant to a long‐running blackboard‐based system (e.g. clearing extraneous data from the blackboard regularly to improve performance, archiving relevant but infrequently used data for performance enhancement). A system for automating these tasks is presented that is suitable for most blackboard‐style systems. Its particular utility to those that cannot be easily accessed is discussed. Quantitative analysis of the value of removing rules from the system is performed and presented, and these results are extrapolated to several prospective blackboard maintenance approaches.

Section: Hayesmentioning

confidence: 99%

Automating maintenance for a one‐way transmitting blackboard system used for autonomous multi‐tier control

2016

Expert Systems

“…Applications include speech recognition [25], disaster response [26], robotic control [27][28][29][30][31], e-learning [32,33], network management [34], preventing terrorism [35], caring for the elderly [36], project scheduling [37], application testing [38], protein modeling [39,40], software selection [41], risk management [42], system problem diagnosis [43,44], and military control [45]. …”

Section: Blackboard Architecturementioning

confidence: 99%

Using a blackboard architecture or expert system to identify obfuscated targets from symptoms

2015

SPIE Proceedings

A variety of techniques exist for enhancing or inferring the existence and characteristics of an obscured or partially concealed target. Targets, however, may be completely blocked from view, presenting nothing to enhance and no image area to extend inferentially. Despite the difficulty, concealed (particularly intentionally) targets may be the most important to detect. This paper proposes a technique for using a Blackboard Architecture or Expert system to infer a target's existence from symptoms (maneuvers of other units, water and soil deformation, etc.) and discusses the differences between the two approaches (Blackboard Architecture and expert system) for doing so.

“…In addition to Hayes-Roth's work, these include robotic control [11][12][13][14][15], e-learning [16,17], network management [18], application testing [19], speech recognition [20], disaster response [21], software selection [22], caring for the elderly [23], project scheduling [24], risk management [25], protein modeling [26,27], system problem diagnosis [28,29], military control [30] and even preventing terrorism [31].…”

Section: A Blackboard Architecturementioning

confidence: 99%

The Use of the Blackboard Architecture for a Decision Making System for the Control of Craft with Various Actuator and Movement Capabilities

2014 11th International Conference on Information Technology: New Generations

Reza

2014

This paper provides an overview of an approach to the control of multiple craft with heterogeneous movement and actuation characteristics that is based on the Blackboard software architecture. An overview of the Blackboard architecture is provided. Then, the operational and mission requirements that dictate the need for autonomous control are characterized and the utility of the Blackboard architecture is for meeting these requirements is discussed. The performance of a best-path solver and naïve solver are compared. The results demonstrate that the best-path solver outperforms the naïve solver in the amount of time taken to generate a solution; however, the number of solverruns to be executed against the Blackboard must be sufficient to allow the lower individual-run times to offset the time required to propagate the data utilized by the best-path solver for solution generation through the database.The existence of other justifications for this approach (even if the number of runs for each data propagation cycle is not sufficient) is also discussed.