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Problem domain

About: Problem domain is a research topic. Over the lifetime, 3132 publications have been published within this topic receiving 78308 citations.


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Journal ArticleDOI
TL;DR: How heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching is described and an optimality property of a class of search strategies is demonstrated.
Abstract: Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be compared. This paper describes how heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching and demonstrates an optimality property of a class of search strategies.

10,366 citations

Journal ArticleDOI
TL;DR: The objective is to describe the performance of design-science research in Information Systems via a concise conceptual framework and clear guidelines for understanding, executing, and evaluating the research.
Abstract: Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science The behavioral-science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology Our objective is to describe the performance of design-science research in Information Systems via a concise conceptual framework and clear guidelines for understanding, executing, and evaluating the research In the design-science paradigm, knowledge and understanding of a problem domain and its solution are achieved in the building and application of the designed artifact Three recent exemplars in the research literature are used to demonstrate the application of these guidelines We conclude with an analysis of the challenges of performing high-quality design-science research in the context of the broader IS community

10,264 citations

Journal ArticleDOI
TL;DR: This article deals with the execution of a simulation program on a parallel computer by decomposing the simulation application into a set of concurrently executing processes and introduces interesting synchronization problems that are at the heart of the PDES problem.
Abstract: Parallel discrete event simulation (PDES), sometimes called distributed simulation, refers to the execution of a single discrete event simulation program on a parallel computer. PDES has attracted a considerable amount of interest in recent years. From a pragmatic standpoint, this interest arises from the fact that large simulations in engineering, computer science, economics, and military applications, to mention a few, consume enormous amounts of time on sequential machines. From an academic point of view, parallel simulation is interesting because it represents a problem domain that often contains substantial amounts of parallelism (e.g., see [59]), yet paradoxically, is surprisingly difficult to parallelize in practice. A sufficiently general solution to the PDES problem may lead to new insights in parallel computation as a whole. Historically, the irregular, data-dependent nature of PDES programs has identified it as an application where vectorization techniques using supercomputer hardware provide little benefit [14].A discrete event simulation model assumes the system being simulated only changes state at discrete points in simulated time. The simulation model jumps from one state to another upon the occurrence of an event. For example, a simulator of a store-and-forward communication network might include state variables to indicate the length of message queues, the status of communication links (busy or idle), etc. Typical events might include arrival of a message at some node in the network, forwarding a message to another network node, component failures, etc.We are especially concerned with the simulation of asynchronous systems where events are not synchronized by a global clock, but rather, occur at irregular time intervals. For these systems, few simulator events occur at any single point in simulated time; therefore parallelization techniques based on lock-step execution using a global simulation clock perform poorly or require assumptions in the timing model that may compromise the fidelity of the simulation. Concurrent execution of events at different points in simulated time is required, but as we shall soon see, this introduces interesting synchronization problems that are at the heart of the PDES problem.This article deals with the execution of a simulation program on a parallel computer by decomposing the simulation application into a set of concurrently executing processes. For completeness, we conclude this section by mentioning other approaches to exploiting parallelism in simulation problems.Comfort and Shepard et al. have proposed using dedicated functional units to implement specific sequential simulation functions, (e.g., event list manipulation and random number generation [20, 23, 47]). This method can provide only a limited amount of speedup, however. Zhang, Zeigler, and Concepcion use the hierarchical decomposition of the simulation model to allow an event consisting of several subevents to be processed concurrently [21, 98]. A third alternative is to execute independent, sequential simulation programs on different processors [11, 39]. This replicated trials approach is useful if the simulation is largely stochastic and one is performing long simulation runs to reduce variance, or if one is attempting to simulate a specific simulation problem across a large number of different parameter settings. However, one drawback with this approach is that each processor must contain sufficient memory to hold the entire simulation. Furthermore, this approach is less suitable in a design environment where results of one experiment are used to determine the experiment that should be performed next because one must wait for a sequential execution to be completed before results are obtained.

1,615 citations

Journal ArticleDOI
TL;DR: Examples of the ABSTRIPS system's performance are presented that demonstrate the significant increases in problem-solving power that this approach provides, and some further implications of the hierarchical planning approach are explored.

1,239 citations

Journal ArticleDOI
TL;DR: The notion of the ontological level is introduced, intermediate between the epistemological and the conceptual levels discussed by Brachman, as a way to characterize a knowledge representation formalism taking into account the intended meaning of its primitives.
Abstract: The purpose of this paper is to defend the systematic introduction of formal ontological principles in the current practice of knowledge engineering, to explore the various relationships between ontology and knowledge representation, and to present the recent trends in this promising research area. According to the "modelling view" of knowledge acquisition proposed by Clancey, the modelling activity must establish a correspondence between a knowledge base and two separate subsystems: the agent's behaviour (i.e. the problem-solving expertise ) and its own environment (the problem domain ). Current knowledge modelling methodologies tend to focus on the former sub-system only, viewing domain knowledge as strongly dependent on the particular task at hand: in fact, AI researchers seem to have been much more interested in the nature of reasoning rather than in the nature of the real world. Recently, however, the potential value of task-independent knowledge bases (or "ontologies") suitable to large scale integration has been underlined in many ways. In this paper, we compare the dichotomy between reasoning and representation to the philosophical distinction between epistemology and ontology. We introduce the notion of the ontological level, intermediate between the epistemological and the conceptual levels discussed by Brachman, as a way to characterize a knowledge representation formalism taking into account the intended meaning of its primitives. We then discuss some formal ontologic distinctions which may play an important role for such purpose.

1,140 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20223
2021101
202087
2019107
201888
2017104