Laffey, T.J., Cox, P. A., Schmidt, J. L., Kao, S. M., and Read, J. Y. Real-time Knowledge-Based Systems. AI magazine 9(1):27-45, 1988.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....algorithms have been developed for evaluation of probabilistic networks and for dynamic programming. But the technique has been less successful in the area of knowledge based systems. 2. 2 Real time knowledge based systems In a 1988 comprehensive survey of real time knowledge based systems [Laffey et al. 1988], the authors concluded that Currently, ad hoc techniques are used for making a system produce a response within a specified time interval. Unfortunately, not much has been changed since that survey was conducted. The primary method for achieving real time performance is based in many cases on ....

T. J. Laffey, P. A. Cox, J. L. Schmidt, S. IV[. Kao and J. . Read. Real-time knowl- edge based systems. AI Magazine 9(1):27-45, Spring 1988.

....programming community. This work is supported by the French Ministry of Defense (DRET) 1. Introduction Historically, the field of artificial intelligence has not been directly concerned with real time performance. Consequently, few systems are capable of reasoning in time and about time [Laf 88] Currently, a great deal of research effort is spent to design timely intelligent agents, and two major approaches are proposed [Hay 94] The first approach requires some limitations in complexity of AI techniques providing bounded response times. This approach, called embedded AI, makes no ....

Laffey T. J., Cox P. A., Schmidt J. L., Kao S. M., Read J. Y., "Real-time knowledge based systems", AI Magazine, Vol. 9, n ffi 1, Spring 1988

....this work and identify the strengths and weaknesses of existing models. The focus of the analysis is on real time decision making as a component of an architecture for artificial agent construction. How do most AI systems cope with time constraints In a comprehensive survey of real time AI [Laffey et al. 1988] that covered 48 systems, the authors claimed that Currently, ad hoc techniques are used for making a system produce a response within a specified time interval. Unfortunately, not much has been changed since that survey was conducted. The primary method for achieving real time performance is ....

....scheduler. Real time systems must not only produce correct results but also meet certain timing constraints. In traditional real time systems, the timing constraints impose a fixed time allocation to the problem solving component so that the system can meet a certain deadline. For example, Laffey et al. 1988] define real time systems by the capability to guarantee a response after a fixed time has elapsed, where the fixed time is provided as part of the problem statement. This conservative approach leads to inflexible systems that may be under utilized since in many domains there are no clear, rigid ....

[Article contains additional citation context not shown here]

T. J. Laffey, P. A. Cox, J. L. Schmidt, S. M. Kao and J. Y. Read. Real-time knowledge based systems. AI Magazine 9(1):27--45, Spring 1988.

....is to speed up production systems several times over. However, these techniques are not yet adequate for continuous problem solving systems. Typical examples can be found in real time expert systems, where new techniques are required to adapt themselves to the dynamically changing environment [Laffey et al. 1988]. To satisfy real time constraints, various agent centered approaches are currently being studied. Lesser et al. 1988] discussed approximate processing techniques. Hayes Roth et al. 1989] introduced adaptive intelligent systems that reason about and interact with other dynamic entities in ....

T. J. Laffey, P. A. Cox, J. L. Schmidt, S. M. Kao, and J. Y. Read, "Real-Time Knowledge-Based Systems," AI Magazine, Vol. 9, No. 1, pp. 27-45, 1988.

....constraints between the technical process and the planning process exist. There are several characteristics of real time applications that distinguish them from other applications. We outline some of the important requirements. These and some further Problems are discussed in more detail in [10]. Representation of concurrency and sequence In real time applications different activities occur concurrently (e.g. several autonomous robots are moving) and sometimes a strict sequence is necessary (e.g. in the process of assembling parts) It must be possible to synchronize several ....

Thomas J. Laffey, Preston A. Cox, James L. Schmidt, Simon M. Kao, Jackson Y. Read. "Real-Time Knowledge-Based Systems". AI Magazine, Spring, pp 27-45, 1988.

....There are several characteristics that distinguish them from other applications. I outline some of the important requirements by means of the described application. These points set the standard for the evaluation of my system. A deeper and more general discussion of these points can be found in [25]. 8 . Safety The system must guarantee that trains do not collide. Besides the software this issue relays also heavily on the hardware. If for example the brake of a train does not function the software is not able to stop the train. However, due to redundancy in the hardware, some failures ....

Laffey, T.J.; Cox, P.A.; Schmidt, J.L.; Kao, S.M.; Read, J.Y., Real-Time KnowledgeBased Systems, AI Magazine , Spring, pp. 27--45, 1988.

....been interest in applying them to controlling complex real world systems which involve hard deadlines and dynamic situations. In such systems, when the hard deadlines are not respected, the failure may be catastrophic. Therefore, real time applications require constant and predictable performance [Laffey 88] Unfortunately, many AI techniques are not suited to analyses that would provide guaranteed response times. Thus, an apparent conflict between the nature of AI and the needs of the realworld is perceived. While AI methods are characterised by unpredictable or high variance performance, ....

T.J. Laffey, P.A. Cox, J.L. Schmidt, S.M. KAO et J.Y. Read. "RealTime Knowledge-based systems". AI Magazine. Spring 1988, 27-45. 19

....Agents In section 2.1, we introduce a neutral framework in which to discuss agents and their environments. In sections 2.2 and 2. 3, we operationalize environmental characteristics and agent requirements in the terms of the framework and show how the former motivate the latter (see also [12, 30, 31, 34, 42, 46, 49]) 2.1 A Framework Following [42] we model an intelligent agent as a dynamic embedded system. The overall system is modeled as a time series of states in which instants of time are mapped to a state space of values representing the variables of interest. A change in the value of a state ....

Laffey, T., Cox, P.A., Schmidt, J.L., Kao, S.M., and Read, J.Y. Real-time knowledge-based systems. AI Magazine, 9:1, 1988.

...., and a certain value an execution threshold. We developed a planning procedure based on the above claim, and call it SIP (Success probability based Interleave Planning) A domain First we define a dynamic environment. The problem definition is generalized from real time knowledgebased systems (Laffy et al. 1988) and the simplified Tileworld (Kinny Georgeff 1991) Definition 1 (a dynamic environment) A dynamic environment where a SIP agent acts is a problem space where goals appear and disappear as time passes. Each goal G i has value V i and a SIP agent repeatedly tries to achieve a goal before it ....

Laffy, T. J.; Cox, P. A.; Schmidt, J. L.; Kao, S. M.; and Read, J. Y. 1988. Real-time knowledge-based systems. In AI magazine, volume 9. 27--45.

....enable development of expert system which can be embedded in the present ground control systems, accept sensor data and reason using the same. 2. 1 Requirements Before we took up the design of the shell, we identified the following essential features of the physical system that affect the design [3]. They are ffl sporadic events: events like alarms which occur sporadically and cannot be anticipated. ffl continuous operation: the ground control operation is continuous and is spread over hours or even days depending on the complexity of the launch vehicle. ffl delayed feedbacks and ....

T. J. Laffey, P. A. Cox, J. L. Schmidt, S.M. Kai, and J. Y. Read. Real time knowledge based systems. AI magazine, 9(1):27--45, 1988.

....Intelligence (AI) techniques in traditional real time systems appears as a very promising approach to cope with the growing complexity of these systems. It is especially the case in complex applications such as process control, intensive care monitoring, robotics or aircraft pilot advising [1]. However, there are great difficulties for traditional knowledge based systems to meet the requirements of real time applications. In fact, the interest of real time This paper presents a research work carried out in the framework of the ESPRIT REAKT project sponsored by the EEC. REAKT ....

T. Laffey, P. Cox, J. Schmidt, S. Kao and J. Read. Real-time knowledge-based systems. Artificial Intelligence Magazine, Spring, 1988.

....an important area of application for expert systems. Systems such as Escort [2] an expert system for complex operations in real time) and Realm [3] a reactor emergency action level monitor) are two of many expert systems developed for process industries. For surveys of this work, see [4] and [5]. These systems aim to reduce the cognitive load on operators, usually by helping to diag2 nose the cause of alarms and possibly to suggest corrective actions. Most of these expert systems get their knowledge of symptoms, faults, and corrective actions through the usual process of codifying human ....

Thomas J. Laffey, Preston A. Cox, James L. Schmidt, Simon M. Kao, and Jackson Y. Read. Real-Time Knowledge-Based Systems. AI Magazine, 9(1), 1988.

.... applications, medical applications, process control applications, and robotics applications, where these systems are evaluated in terms of attributes such as speed, response, temporal reasoning, asynchronous input, interrupt handling, continuous operation, noisy data, and focus of attention [47]. Real time knowledge based systems and expert systems in general involve planning and execution of actions. For example, Shekhar and Dutta [87] combined search and execution into planning, and optimized the total time spent in both. Approximation search, data approximation, and knowledge ....

T. J. Laffey, P. A. Cox, J. L. Schmidt, S. M. Kao, and J. Y. Read, "Real-Time Knowledge-Based Systems," AI Magazine, pp. 27-45, Spring 1988.

....on the tasks generated by CIRCA. 2 Overview of CIRCA The increasing complexity of real time computer control systems has motivated a growing interest in applying mature AI techniques to the control of real time systems, in an effort to develop more intelligent, flexible automated systems [3, 9, 10]. One way to achieve intelligent real time control is to use AI methods to automatically design real time control methods on line, dynamically altering the executing control system in response to changes in the system s goals or its environment [8] Illustrated in Figure 1, CIRCA automates the ....

T. J. Laffey, P. A. Cox, J. L. Schmidt, S. M. Kao, and J. Y. Read, "Real-Time KnowledgeBased Systems," AI Magazine, vol. 9, no. 1, pp. 27--45, 1988.

....those earlier claims to show the types of conditions under which filtering leads to improved performance, and the types of conditions that require restrictions on filtering. 1 Introduction Many existing and potential AI applications involve systems that are situated in dynamic environments: Laffey et al. 1988] list examples from aerospace, communications, medical, process control, and robotics applications. Optimal deliberation in the decision theoretic sense is impossible in such environments. All systems have computational resource limits: their deliberations take time. During the time in which a ....

T. J. Laffey, P. A. Cox, J.L. Schmidt, S.M. Kao, and J.Y. Read. Real-time knowledge-based systems. Artificial Intelligence Magazine, 9:27--45, 1988.

....algorithms have been developed for evaluation of probabilistic networks and for dynamic programming. But the technique has been less successful in the area of knowledge based systems. 2. 2 Real time knowledge based systems In a 1988 comprehensive survey of real time knowledgebased systems [ Laoeey et al. 1988 ] the authors concluded that iCurrently, ad hoc techniques are used for making a system produce a response within a speci ed time interval. j Unfortunately, not much has been changed since that survey was conducted. The primary method for achieving real time performance is based in many cases on ....

T. J. Laoeey, P. A. Cox, J. L. Schmidt, S. M. Kao and J. Y. Read. Real-time knowledge based systems. AI Magazine 9(1):2745, Spring 1988.

....AI capabilities in applications demanding high performance and negligible response times. As a result, designers typically choose a fixed level of output quality, and then perform the necessary precompilation and optimization to achieve that level within a fixed time limit. Laffey et al. [32] survey a large number of application programs for realtime AI, and note, somewhat despairingly, that Currently, ad hoc techniques are used for making a system produce a response within a specified time interval. The inappropriateness of solution optimality as a criterion for success has been ....

Laffey, T. J., Cox, P. A., Schmidt, J. L., Kao, S. M., and Read, J. Y. (1988) Real-time knowledge-based systems. AI Magazine, 9(1), 27-45.

....on being able to decide which goals to pursue in the first place, and when to abandon or suspend the pursuit of an existing goal. Dynamic environments are ubiquitous. There is a large and growing number of AI applications, current or planned, that involve deploying agents in dynamic environments [45]. These applications include equipment malfunction monitoring [23] process management for manufacturing [58] medical patient The Uses of Plans 7 monitoring [33] crisis management [13] and support for combat personnel [4] In addition, as the examples in this paper demonstrate, even humbler ....

T. J. Laffey, P. A. Cox, J. L. Schmidt, S. M. Kao, and J. Y. Read. Real-time knowledge-based systems. AI Magazine, 9(1):27--45, 1988. The Uses of Plans 42

No context found.

Laffey, T.J., Cox, P. A., Schmidt, J. L., Kao, S. M., and Read, J. Y. Real-time Knowledge-Based Systems. AI magazine 9(1):27-45, 1988.

No context found.

T. J LAFFEY, P. A. COX, J.L SCHMIDT, S. M. KAO and J.Y READ, Real-time knowledge-based systems. AI Magazine, 9(1):27-45, 1988.

No context found.

T. J. Laaffey, P. A. Cox, J. L. Schmidt, S. M. Kao and J. Y. Read. Real-Time Knowledge Based Systems. AI Magazine, 9(1), 27-45, Spring 1988.

No context found.

T. J. Laffey, P. A. Cox, J. L. Schmidt, S. M. Kao and J. Y. Read. Real-time knowledge based systems. AI Magazine, 9(1):27-45, Spring 1988.

No context found.

Thomas J. Laffey, Preston A. Cox, James L. Schmidt, Simon M. Kao, and Jackson Y. Read. Real-time knowledge-based systems. AI Magazine, 9(1):27-- 45, Spring 1988.

No context found.

Laffey, T.J., P.A. Cox, J.L. Schmidt, S.M. Kao and J.Y. Read. 1988. Real-Time Knowledge-Based Systems. AI Magazine, 9(1), 27-45.

No context found.

Laffey, T., J., Cox, P., A., Schmidt, J., L., Kao, S., M., and Read, J., Y. Real-Time Knowledge-Based Systems. AI magazine 9, 1, 1988, 27-45.

First 50 documents

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC