An architecture for heuristic control of real-time processes

Abstract Process management combines complementary approaches of heuristic reasoning and analytical process control. Management of a continuous process requires monitoring the environment and the controlled system, assessing the ongoing situation, developing and revising planned actions, and controlling the execution of the actions. For knowledge-intensive domains, process management entails the potentially time-stressed cooperation among a variety of expert systems. By redesigning a blackboard control architecture in an object-oriented framework, researchers obtain an approach to process management that considerably extends blackboard control mechanisms and overcomes limitations of blackboard systems

Improving performance of blackboard systems

In this thesis, we deal with blackboard system performance issues. We show that blackboard system performance can be improved using parallel processing strategies and a novel blackboard architecture.We study traditional blackboard architectures using a novel performance frame¬ work. This is a useful tool for directing system optimisation efforts. We present the analysis of four blackboard systems present in the literature.nalysis of four blackboard systems present in the literature. Besides localised optimisation efforts, one of the most promising approaches for improving blackboard system performance is the use of parallel processing techniques. However, traditional blackboard architectures present both data and control contention when implemented in parallel.In this thesis we present a novel blackboard architecture, the Active Blackboard Architecture (ABB). We based ABB on a novel variation of the traditional "Blackboard and Experts" metaphor, called "Blackboard, Experts and Desks". This new metaphor introduces a new element, the desks, used by the experts to perform their work.The ABB architecture is based on an active blackboard, capable of processing on its own, and a decentralised control model. This avoids control contention and bottlenecks. We describe this architecture using the Z specification language, and implemented and evaluated in the EPCC Meiko Computing Surface, a multi-transputer distributed memory parallel machine.The ABB Parallel prototype is an object oriented implementation of the ABB model that overcomes both data and control bottlenecks by having a distributed blackboard and using the ABB control model. Based on a series of experiments, we show that the new architecture allows to achieve much greater effective parallelism in a blackboard system. We also present some ways in which the system can be tailored to specific application needs, improving in this way its overall performance

Teraphim: a domain-independent framework for constructing blackboard-controlled, blackboard-based expert systems in Prolog

The blackboard architecture, in which a set of independent knowledge sources communicate by means of a global data base known as a blackboard, has been suggested as a generally useful design for knowledge-based systems. Teraphim is a domain-independent frame work for writing blackboard-based expert systems in Prolog. It implements concepts common to a range of previous blackboard architecture programs, such as HEARSAY-III and BB1. Teraphim includes as its basic elements a partitioned blackboard, a simple blackboard-controlled scheduler, a set of general-purpose scheduling heuristics to control the scheduler, a generic knowledge source with the ability to ask the user questions about incomplete data, modifiable methods of reasoning about uncertain data, and a simple explanation facility that traces the origins of terms on the problem blackboard. Trials of the system indicate that it can be used to implement expert systems to solve either synthesis or analysis problems. The blackboard architecture of Teraphim lends itself to experimentation with the kinds of knowledge representation and control knowledge needed to solve problems. Prolog proved to be a convenient language for writing blackboard-based systems

The Use of the Blackboard Archiecture for a Decision making System for the Control of Craft with various Actuator and Movement Capabilities

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 solver-runs 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

BB-CLIPS: Blackboard extensions to CLIPS

This paper describes a set of extensions made to CLIPS version 4.3 that provide capabilities similar to the blackboard control architecture described by Hayes-Roth. There are three types of additions made to the CLIPS shell. The first extends the syntax to allow the specification of blackboard locations for CLIPS facts. The second implements changes in CLIPS rules and the agenda manager that provide some of the powerful features of the blackboard control architecture. These additions provide dynamic prioritization of rules on the agenda allowing control strategies to be implemented that respond to the changing goals of the system. The final category of changes support the needs of continuous systems, including the ability for CLIPS to continue execution with an empty agenda

MARBLE: A system for executing expert systems in parallel

This paper details the MARBLE 2.0 system which provides a parallel environment for cooperating expert systems. The work has been done in conjunction with the development of an intelligent computer-aided design system, ICADS, by the CAD Research Unit of the Design Institute at California Polytechnic State University. MARBLE (Multiple Accessed Rete Blackboard Linked Experts) is a system of C Language Production Systems (CLIPS) expert system tool. A copied blackboard is used for communication between the shells to establish an architecture which supports cooperating expert systems that execute in parallel. The design of MARBLE is simple, but it provides support for a rich variety of configurations, while making it relatively easy to demonstrate the correctness of its parallel execution features. In its most elementary configuration, individual CLIPS expert systems execute on their own processors and communicate with each other through a modified blackboard. Control of the system as a whole, and specifically of writing to the blackboard is provided by one of the CLIPS expert systems, an expert control system

An architecture for presenting auditory awareness information in pervasive computing environments

Presented at the 12th International Conference on Auditory Display (ICAD), London, UK, June 20-23, 2006.In this paper we present how awareness can be supported in pervasive computing environments through auditory information. We introduce an application which uses soundscapes to support people's awareness of each other's presence in an office environment. We describe several techniques for construction and control of such soundscapes. Finally, we present an architecture for designing and controlling soundscapes. The architecture is based on managers, agents, evaluators, a blackboard information storage, and a control language, it emphasizes reusability and extensibility, and it is built upon a common system framework

Multi-agent blackboard architecture for supporting legal decision making

Our research objective is to design a system to support legal decision-making using the multi-agent blackboard architecture. Agents represent experts that may apply various knowledge processing algorithms and knowledge sources. Experts cooperate with each other using blackboard to store facts about current case. Knowledge is represented as a set of rules. Inference process is based on bottom-up control (forward chaining). The goal of our system is to find rationales for arguments supporting different decisions for a given case using precedents and statutory knowledge. Our system also uses top-down knowledge from statutes and precedents to interactively query the user for additional facts, when such facts could affect the judgment. The rationales for various judgments are presented to the user, who may choose the most appropriate one. We present two example scenarios in Polish traffic law to illustrate the features of our system. Based on these results, we argue that the blackboard architecture provides an effecive approach to model situations where a multitude of possibly conflicting factors must be taken into account in decision making. We briefly discuss two such scenarios: incorporating moral and ethical factors in decision making by autonomous systems (e.g. self-driven cars), and integrating eudaimonic (well-being) factors in modeling mobility patterns in a smart city

The StoreGate: a Data Model for the Atlas Software Architecture

The Atlas collaboration at CERN has adopted the Gaudi software architecture which belongs to the blackboard family: data objects produced by knowledge sources (e.g. reconstruction modules) are posted to a common in-memory data base from where other modules can access them and produce new data objects. The StoreGate has been designed, based on the Atlas requirements and the experience of other HENP systems such as Babar, CDF, CLEO, D0 and LHCB, to identify in a simple and efficient fashion (collections of) data objects based on their type and/or the modules which posted them to the Transient Data Store (the blackboard). The developer also has the freedom to use her preferred key class to uniquely identify a data object according to any other criterion. Besides this core functionality, the StoreGate provides the developers with a powerful interface to handle in a coherent fashion persistable references, object lifetimes, memory management and access control policy for the data objects in the Store. It also provides a Handle/Proxy mechanism to define and hide the cache fault mechanism: upon request, a missing Data Object can be transparently created and added to the Transient Store presumably retrieving it from a persistent data-base, or even reconstructing it on demand.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 4 pages, LaTeX, MOJT00

Blackboard System Generator (BSG): An Alternative Distributed Problem-Solving Paradigm

The classical blackboard model employs a number of relaxations of team decision theory that are commonly organized into three panels of AI heuristics, including: 1) a shared information panel that offers a capability for ensuring agent knowledge sharing, 2) a contract formalism for the agent and event scheduling, coordinating, and control panel, and 3) a blackboard panel for metalevel planning and guidance that offers whole situation recognition, top down reasoning, and adaptive learning. The nature and implications of these relaxations are explained in terms of the blackboard system generator (BSG) and via comparisons to what is done in other blackboard shells. Particular attention is paid to theoretical relaxations inherent in the classical blackboard model and to research opportunities arising as a result. Progress made to date to counteract adverse effects of some of these relaxations is described in terms of a project management/work breakdown paradigm adopted in BSG that: 1) alleviates the knowledge engineering bottlenecks of traditional blackboards and that provides BSG with a semantic rather than just syntactic understanding of blackboard control and scheduling; 2) allows a distributed problem-solving capability for connecting agents at virtual addresses on a logical network and that permits concurrent processing on any machine available on the network; 3) establishes an open architecture that includes techniques for integrating preexisting agent methods (e.g., expert systems, procedures, or data bases) while laying the foundation for assessing the impact of “black boxes” on the global and local objective functions; and 4) utilizes project management techniques for team agents planning as well as an analogical reasoner subsystem for BSG metaplanning and generic controlled learning. This latter item is supported by a connectionist scheme for its associative memory. The techniques of each of the three panels and of the four sets of paradigm-related advances are described along with selected results from classroom teaching experiments and from three applications using BSG to date