Blackboard Control Research Articles

Research on Innovation and Entrepreneurship Approach in Universities Based on Large Data

The development of computer technology has promoted the processing technology of large data, meanwhile, the upsurge of college students’ innovation and entrepreneurship has also been promoted. Therefore, it is very meaningful to study the way of innovation and entrepreneurship by using large data technology. Firstly, the research status and development trend of innovation entrepreneurship are analyzed. The development of large data is introduced, and then the coupling point of innovative entrepreneurship and Internet technology is analyzed, and the large data algorithm suitable for the research of innovative startups is designed. Finally, by using the distributed Blackboard Control algorithm in the large data technology, the innovation and entrepreneurship approach of university is analyzed. Through experimental analysis and verification, the proposed improved large data technology, compared to other algorithms, in some performance has a certain advantage.

On Controlling Drones for Disaster Relief

Drones or unmanned aerial vehicles (UAVs) are increasingly deployed for disaster relief, from aiding in drug or supply delivery to search and rescue victims. For a large-scale mission, the more drones are used, the more difficult they are to control and manage in a timely manner. Automated control is one of the central techniques used in knowledge-based drone management systems to enhance the quality and efficiency of the disaster relief mission. Although many drone management systems have been developed, automatic control of these systems remains a difficult problem. Many approaches have "human in the loop" that trades the degree of autonomy with the efficiency and perhaps accuracy. Our goal is not to fully automate the system but to reduce user interactions where intelligible (e.g., selecting appropriate drones/actions based on prioritized needs) by means of significant advances in control mechanisms. This paper presents a knowledge-based framework for intelligent drone management by explicitly representing control knowledge for use in selecting appropriate task-specific actions. The paper describes how control decisions are derived using domain knowledge relevant to drones and disaster relief. By employing a blackboard control architecture, our framework provides adaptability for dynamic control behaviors and flexibility to handle unanticipated situations during the drone mission activities. The paper illustrates the use of the framework on specific scenarios.

Research on Cooperation Mechanism of Multi-Agent Grinding Control System Based on Blackboard Model

In order to solve the complex industrial control problems in grinding control,we construct multi-agent grinding control system based on the blackboard model in this paper. Through the study on the structure and control mechanism of the blackboard model, we design a framework for blackboard model in multi-agent grinding control system, divide and introduce the function model of the blackboard in detail, establish blackboard control agent to control and coordinate how the individual agent takes use of the blackboard resources centralizedly.Aiming at actual situations of the grinding control,we put forward cooperation mechanism multi-agent based on a combination of tasks sharing and results sharing, establish control decision agent to solve the conflict caused by multi-agent cooperation. The cooperation mechanism multi-agent grinding control system improves the execution ability of the individual agent, avoids the conflict caused by different goals and the competition of resources, ensures the coordinated executive of multiple tasks.

Realization on Multi-Agent Intelligent Decision Support System Based on Blackboard

The majority of work is completed by collaboration of various components in a particular group environment, and the architecture of Multi-Agent Intelligent Decision Support System based on blackboard is designed using blackboard system and Agent. At the same time, implementation technique on Agent in system is provided, that includes abstract structure of Agent and modeling of Agent action. In addition to, mechanism of breakdown the task on blackboard control Agent is described. The system is helpful to resolve the complicated works though Multi-Agent.

Medical applications of enhanced rule-based expert systems

The paper describes several types of efficiency enhancements of ‘classical’ rule-based diagnostic expert systems. The blackboard control structure enables to explore more knowledge bases of the same syntax in parallel, the taxonomy structures make fast zooming of attention possible and provide additional inference mechanism based on inheritance principles. The applicability of the enhancing techniques is documented by four case studies exploring the extended FEL-EXPERT shell in different tasks of medical decision-making. The authors consider the enhancing techniques as useful steps on the way from ‘classical’ diagnostic expert systems towards more complex multi-agent decision tools.

Signal understanding and tool condition monitoring

This paper presents a new method, called signal understanding, for the monitoring of engineering processes, in particularly for the monitoring of tool conditions in machining processes. The new method is based on the blackboard system, an artificial intelligence method developed in the 1980s. It emulates a group of experts examining sensor signals from various aspects, and making monitoring decisions step-by-step. The blackboard system consists of two blackboards: an event blackboard, used to track the interpretations of the signal by the experts and a control blackboard, used to direct the interpretation process leading to monitoring decisions. As demonstrated by an example of tool condition monitoring in end milling, the method has a number of advantages over the existing methods, including improved reliability and reduced decision time.

A pragmatic implementation of medical temporal reasoning for clinical medicine

In most development tools for knowledge-based systems object–property–value-triples are the basic ontological representation. We have extended this approach for temporal reasoning by replacing the values with time-objects. A time-object is a combination of a value with a time-point or a time-interval. By preserving the object–property–value-triples basic artificial intelligence techniques can be applied without modifications in the temporal case. The greater complexity of temporal inferences is compensated by a blackboard control architecture enabling small knowledge-sources. The control of the strategic reasoning is in accordance with the select and test model. A prototype of a knowledge-based advisor for the acute radiation syndrome has been implemented and partially evaluated. Due to the affected proliferative tissues it is a strongly time-oriented medical domain.

An object-oriented cooperative distributed problem solving shell with groupware management ability

In this paper, an Object-Oriented Cooperative Distributed Problem Solving Shell (OOCDPSS) with groupware management ability is designed. The groupware management facilities, which satisfy the needs of development and execution of the cooperative distributed problem solving activities, can reinforce concurrency and parallelism, accommodation and integration of heterogeneous, uncoordinated problem solvers cooperating on one common goal. This OOCDPSS system constructs the non-embedded groupware architecture by a hierarchy of leader agents. The leader agent that adopts a distributed blackboard control mechanism can assist the cooperating problem solvers with all the cooperative and coordinative activities at run time through a democratic, contract-net protocol. In this blackboard control mechanism, the system control unit and domain control unit are explicitly separated and put side-by-side in a simple control loop to simplify the control mechanism design. The event messages drive the above control units with the programmable heuristic knowledge, including the static knowledge, procedural knowledge, and strategic, situation-action behavior knowledge. In this OOCDPSS, a completely transparent environment and a programmable distributed blackboard control mechanism are provided. This environment automatically assists and supervises the progress of cooperative problem solving activities at run time, thereby raising the effective level of use among the preexisting problem solvers. In addition, by using the programmable heuristic knowledge, our approach can be efficiently implemented on the cooperative problem solving plans with a range of configurations. ©1997 John Wiley & Sons, Ltd.

An object‐oriented cooperative distributed problem solving shell with groupware management ability

In this paper, an Object-Oriented Cooperative Distributed Problem Solving Shell (OOCDPSS) with groupware management ability is designed. The groupware management facilities, which satisfy the needs of development and execution of the cooperative distributed problem solving activities, can reinforce concurrency and parallelism, accommodation and integration of heterogeneous, uncoordinated problem solvers cooperating on one common goal. This OOCDPSS system constructs the non-embedded groupware architecture by a hierarchy of leader agents. The leader agent that adopts a distributed blackboard control mechanism can assist the cooperating problem solvers with all the cooperative and coordinative activities at run time through a democratic, contract-net protocol. In this blackboard control mechanism, the system control unit and domain control unit are explicitly separated and put side-by-side in a simple control loop to simplify the control mechanism design. The event messages drive the above control units with the programmable heuristic knowledge, including the static knowledge, procedural knowledge, and strategic, situation-action behavior knowledge. In this OOCDPSS, a completely transparent environment and a programmable distributed blackboard control mechanism are provided. This environment automatically assists and supervises the progress of cooperative problem solving activities at run time, thereby raising the effective level of use among the preexisting problem solvers. In addition, by using the programmable heuristic knowledge, our approach can be efficiently implemented on the cooperative problem solving plans with a range of configurations. ©1997 John Wiley & Sons, Ltd.

Computer-aided monitoring system for flexible assembly operations

A computer-aided monitoring strategy for flexible assembly operations is presented. This strategy uses knowledge-based expert systems for error detection and diagnosis. The hierarchy of the system is embedded in a blackboard control structure to facilitate parallel, asynchronous communication and to enhance the flexibility of the system. The developed detection and diagnosis modules of the system were validated experimentally on a prototype robotic assembly work cell. The investigation demonstrated the uniqueness of the new system through the elimination of error propagation and the need for event backtracking if an environment change occurs.

Diverse reasoning in automated model formulation

Diverse reasoning supports a dynamic integration of various reasoning methods in a computerized system. This paper describes a control blackboard approach to simulate the control features observed in the expert's model formulation protocols. The diverse reasoning concept is incorporated so that the model formulation process is dynamically in a plan-directed, action-directed, or data-directed fashion. The diverse reasoning concept facilitates the control features simulation. By analyzing the diverse reasoning behavior in the proposed system, this paper contributes to a better understanding of and support to the modeling process for the design of intelligent decision support systems. The usefulness of the prototype system is also evaluated using an empirical experiment.

Alcod idss: Assisting the Australian stock market surveillance team's review process

ALCOD is a cooperative multiagent intelligent decision support system to assist stock market surveillance teams to classify alerted noncompliant events transacted on the exchange. ALCOD facilitates the review of the classifications. The system combines heuristic approxi mate and causal reasoning and is centered around a relational database which is used as a control blackboard.

Using programming expertise for controlling software synthesis

Achieving efficient software implementations requires a great deal of knowledge, intelligence, and expertise on the part of programmers. One way to enhance software productivity is to incorporate the knowledge and skills of expert programmers into software synthesis systems to automate software development processes. Although many software synthesis systems have been developed, automatic control of synthesis remains a difficult problem. Understanding the role of expertise in software synthesis, and making it more explicit, can help us not only to gain autonomy in controlling the synthesis processes but also to better justify the design, implementations, selection of data structures or algorithms employed in constructing code. Our project aims at making synthesis as autonomous as possible by advances in intelligent control mechanisms to reduce user interaction in the synthesizer. In our earlier work, a blackboard control framework for controlling synthesis processes was introduced. This paper describes how...

Evolution of blackboard control architectures

This paper examines the issues that arise in the control of blackboard systems for aplications with large and complicated search spaces by analyzing the evolution of blackboard control architectures. The authors feel that the issues addressed here apply more greatly to AI application domains involving complex multidimensional search, in which control knowledge is as important to successful problem solving as is domain knowledge. Evolution is viewed largely from the context of the Hearsay-II (HSII) speech understanding system. The appeal of the blackboard model is that it provides great flexibility in structuring problem solving. On the other hand, many of the features that are responsible for this flexibility make effective control difficult because they complicate the process of estimating the expected value of potential actions. Among the key themes in the evolution of blackboard control is the development of mechanisms that support more sophisticated goal-directed reasoning. In the basic control mechanism of HSII, control decisions could consider only the local and immediate effects of possible actions. Thus, the value of potential actions in meeting the system goals could be evaluated in only a limited manner. The development of appropriate abstractions of the intermediate state of problem solving can be used to evaluate the non-local effect of actions relative to the overall problem-solving goals. In addition, blackboard systems went from the implicit representation of goals in HSII to explicit representation of the goals that must be satisfied in order to meet the overall goals of the system. This allowed the implementation of various styles of goal-directed reasoning (e.g., subgoaling and planning) that were not supported in the basic HSII control mechanism. Other architectural mechanisms were concerned with efficiency issues. This article examines a number of different blackboard control architectures that have evolved from the basic model of HSII:HASP/SIAP's even-based control. CRYSALIS' hierarchial control, the DVMT's goal-directed architecture, the control blackboard architecture (BB1), model-based incremental planning for the DVMT, and the RESUN interpretation framework. A longer version of this paper is available as a technical report. It also includes analyses of the channelized, parameterized control loop version of the DVMT (Decker, Humphrey, & Lesser, 1989), ATOME's hybrid multistage control (Laasri, & Maitre, 1989) and CASSANDRA's distributed control (Craig, 1989).

A blackboard framework on top of prolog

Abstract BEST (Blackboard Expert Systems Toolkit) aimed to provide the ability to produce large-scale evolvable, heterogeneous intelligent systems. Apart from providing all necessary blackboard infrastructure and control capabilities, BEST allows its user to integrate different programming paradigms within a single blackboard application. It does not matter if one knowledge source is a forward- or backward-chaining rule-based system, another uses model-based reasoning paradigm, and still another is a conventional, procedural program. Moreover, because representational flexibility is similarly important in blackboard systems, and the blackboard model does not place any prior restrictions on what type of information can be placed on the blackboard, the authors offer multiparadigm knowledge representation language, built on top of Prolog, therefore avoiding restricting users to a single way of expressing either knowledge or data. BEST's problem-solving facilities include assumption-based truth maintenance, temporal, hypothetical, and approximate reasoning. The generality and flexibility of the BEST environment present the first-time blackboard application developer with numerous implementation choices, providing a wide range of options and capabilities,what significantly increases the productivity of BEST programmers, and improves the performance of the application they produce. The ability to explain its reasoning and to defend its decision also distinguished BEST from most other blackboard systems.

A blackboard control architecture for model selection and sequencing

Model management systems are computerised systems that facilitate the management of large numbers of decision models used in organizations. Model selection and sequencing in a model management system is the problem of processing a given model base in order to arrive at a sequence of models that can be executed to produce a set of required outputs (goal). Prior solution approaches do not attempt to solve this problem such that the goal is achieved while best meeting the objectives of the user. Instead, research to date has typically provided the first sequence of models which satisfy the goal, without attempting to optimise the objectives of the user. This restricts the applicability of many existing approaches to problems with unique solutions or to situations where users exhibit no preference among the candidate model sequences (i.e. solutions). In many real-world problems, however, multiple solutions may exist and users may prefer a certain solution over the others, based on a variety of criteria such as solution cost, accuracy and so on. In this paper, we present an architecture based on the concept of blackboard control that solves the model selection and sequencing problem while attempting to optimise the objectives of the user. We also discuss the applicability of the proposed approach for solving other problems encountered in the area of model management.

A VLIW architecture for optimal execution of branch-intensive loops

aim ABSTRACT propose a VLIW architectural model for optimal execution of branch-intensive loops as well as a new single-chip architecture UWR-2 for digital signal and image processing based on this model. In this architecture the instructions belonging to different iterations and different paths can be executed simultaneously. Instruction level parallelism can be exploited in a wider scope as multi-branching can be pmcessed in each machine cycle. A mechanism called the pipeline control blackboard(PCBB) is also proposed to support conditional branches. The URPR-2 can not only execute loops with basic block at high speed but also can run loops with conditional branches at a cost of reduced time and space occupancy.

Hybrid knowledge-based systems for therapy planning

The design and development of a knowledge-based system (KBS) for therapy planning may benefit from an epistemological analysis of this generic medical task. We specialized a previously formulated epistemological model of medical reasoning toward therapy planning by defining an appropriate ontology and an inference model. We propose a computational framework for the implementation of such epistemological model. Then, we discuss how to choose the formalisms for knowledge representation. It will become evident that a KBS for therapy planning usually requires more than one formalism. We experimented the combined use of production rules, frames, and probabilistic models, such as influence diagrams. From the computational point of view, the system is based on a blackboard control architecture, where control knowledge and domain knowledge are represented explicitly and separately. The medical domain where the system has been experimented is hematology, more specifically the therapy of anemic patients. Clinical examples from this field provide empirical evidence supporting our claims.

Goal-driven blackboard control architecture based on extending partially complete general goal trees

A control architecture for goal-driven blackboard systems is introduced. The basic elements of the architecture are goals, policies, strategies, methods and knowledge sources. The basic control loop employs a bidding mechanism to determine the knowledge source to be executed at the current cycle. The architecture employs separate control and domain blackboards, and separate knowledge sources for the control problem and for representing the domain knowledge. The major characteristics of the architecture are that it has a simple and uniform structure, and that its basic control loop is based on a formal basis, namely, extending a partially complete general goal tree. The architecture is implemented in Smalltalk and tested on a multiple-task planning problem.

Implementation of a Computerized Patient Advice System using the HELP clinical information system

A COMputerized Patient Advice System (COMPAS) was designed to test the feasibility of using the HELP clinical information system to direct the respiratory therapy of intensive care (ICU) patients acutely ill with adult respiratory distress syndrome. A modified blackboard control architecture allowed the application of knowledge in either a forward or a backward chaining mode. Expert clinicians recommended decision logic and actions for five different modes of ventilatory support. The clinical staff used COMPAS to manage the ICU ventilatory support of five patients for a total of 624 hr. During that time there were 407 decision-making opportunities. COMPAS automatically generated therapy suggestions 379 (93.1%) times and the clinical staff accepted COMPAS's recommendation in 320 (84.4%) of these cases. These results suggest that the ventilatory support of severely ill ICU patients can be managed by a clinical information system using a blackboard control architecture.