Knowledge-based simulation

An architecture for a knowledge-based simulator is described. The task of scheduling represents an area in which such a tool might be applied. More specifically, scheduling for crew and ground support activities for the shuttle and space station would benefit from the application of knowledge-based simulation. The knowledge-based simulator would allow the crew and support personnel to schedule and reschedule activities in a timely and flexible manner in order to examine and test possible plans

Construction of dynamic stochastic simulation models using knowledge-based techniques

Over the past three decades, computer-based simulation models have proven themselves to be cost-effective alternatives to the more structured deterministic methods of systems analysis. During this time, many techniques, tools and languages for constructing computer-based simulation models have been developed. More recently, advances in knowledge-based system technology have led many researchers to note the similarities between knowledge-based programming and simulation technologies and to investigate the potential application of knowledge-based programming techniques to simulation modeling. The integration of conventional simulation techniques with knowledge-based programming techniques is discussed to provide a development environment for constructing knowledge-based simulation models. A comparison of the techniques used in the construction of dynamic stochastic simulation models and those used in the construction of knowledge-based systems provides the requirements for the environment. This leads to the design and implementation of a knowledge-based simulation development environment. These techniques were used in the construction of several knowledge-based simulation models including the Advanced Launch System Model (ALSYM)

Knowledge-based simulation using object-oriented programming

Simulations have become a powerful mechanism for understanding and modeling complex phenomena. Their results have had substantial impact on a broad range of decisions in the military, government, and industry. Because of this, new techniques are continually being explored and developed to make them even more useful, understandable, extendable, and efficient. One such area of research is the application of the knowledge-based methods of artificial intelligence (AI) to the computer simulation field. The goal of knowledge-based simulation is to facilitate building simulations of greatly increased power and comprehensibility by making use of deeper knowledge about the behavior of the simulated world. One technique for representing and manipulating knowledge that has been enhanced by the AI community is object-oriented programming. Using this technique, the entities of a discrete-event simulation can be viewed as objects in an object-oriented formulation. Knowledge can be factual (i.e., attributes of an entity) or behavioral (i.e., how the entity is to behave in certain circumstances). Rome Laboratory's Advanced Simulation Environment (RASE) was developed as a research vehicle to provide an enhanced simulation development environment for building more intelligent, interactive, flexible, and realistic simulations. This capability will support current and future battle management research and provide a test of the object-oriented paradigm for use in large scale military applications

Knowledge based cloud FE simulation of sheet metal forming processes

The use of Finite Element (FE) simulation software to adequately predict the outcome of sheet metal forming processes is crucial to enhancing the efficiency and lowering the development time of such processes, whilst reducing costs involved in trial-and-error prototyping. Recent focus on the substitution of steel components with aluminum alloy alternatives in the automotive and aerospace sectors has increased the need to simulate the forming behavior of such alloys for ever more complex component geometries. However these alloys, and in particular their high strength variants, exhibit limited formability at room temperature, and high temperature manufacturing technologies have been developed to form them. Consequently, advanced constitutive models are required to reflect the associated temperature and strain rate effects. Simulating such behavior is computationally very expensive using conventional FE simulation techniques. This paper presents a novel Knowledge Based Cloud FE (KBC-FE) simulation technique that combines advanced material and friction models with conventional FE simulations in an efficient manner thus enhancing the capability of commercial simulation software packages. The application of these methods is demonstrated through two example case studies, namely: the prediction of a material's forming limit under hot stamping conditions, and the tool life prediction under multi-cycle loading conditions

Unraveling the influence of domain knowledge during simulation-based inquiry learning

This study investigated whether the mere knowledge of the meaning of variables can facilitate inquiry learning processes and outcomes. Fifty-seven college freshmen were randomly allocated to one of three inquiry tasks. The concrete task had familiar variables from which hypotheses about their underlying relations could be inferred. The intermediate task used familiar variables that did not invoke underlying relations, whereas the abstract task contained unfamiliar variables that did not allow for inference of hypotheses about relations. Results showed that concrete participants performed more successfully and efficiently than intermediate participants, who in turn were equally successful and efficient as abstract participants. From these findings it was concluded that students learning by inquiry benefit little from knowledge of the meaning of variables per se. Some additional understanding of the way these variables are interrelated seems required to enhance inquiry learning processes and outcomes

An Evolutive Model of Knowledge-Based Organizational Populations

In this paper we develop an evolutionary agent-based simulation model derived from a knowledge-based theoretical framework and use it to explore the effect of knowledge management strategies on the evolution of a group of knowledge-intensive organizations located in a given geographical area. We then present the results of different runs of the simulation model.knowledge management strategies

Cross Layer Aware Adaptive MAC based on Knowledge Based Reasoning for Cognitive Radio Computer Networks

In this paper we are proposing a new concept in MAC layer protocol design for Cognitive radio by combining information held by physical layer and MAC layer with analytical engine based on knowledge based reasoning approach. In the proposed system a cross layer information regarding signal to interference and noise ratio (SINR) and received power are analyzed with help of knowledge based reasoning system to determine minimum power to transmit and size of contention window, to minimize backoff, collision, save power and drop packets. The performance analysis of the proposed protocol indicates improvement in power saving, lowering backoff and significant decrease in number of drop packets. The simulation environment was implement using OMNET++ discrete simulation tool with Mobilty framework and MiXiM simulation library.Comment: 8 page

A quest for a better simulation-based knowledge elicitation tool

Knowledge elicitation is a well-known bottleneck in the development of Knowledge-Based Systems (KBS). This is mainly due to the tacit property of knowledge, which renders it unfriendly for explication and therefore, analysis. Previous research shows that Visual Interactive Simulation (VIS) can be used to elicit episodic knowledge in the form of example cases of decisions from the decision makers for machine learning purposes, with a view to building a KBS subsequently. Notwithstanding, there are still issues that need to be explored; these include how to make a better use of existing commercial off-the-shelf VIS packages in order to improve the knowledge elicitation process' effectiveness and efficiency. Based in a Ford Motor Company (Ford) engine assembly plant in Dagenham (East London), an experiment was planned and performed to investigate the effects of using various VIS models with different levels of visual fidelity and settings on the elicitation process. The empirical work that was carried out can be grouped broadly into eight activities, which began with gaining an understanding of the case study. Next, it was followed by four concurrent activities of designing the experiment, adapting a current VIS model provided by Ford to support a gaming mode and then assessing it, and devising the meaures for evaluating the elicitation process. Following these, eight Ford personnel, who are proficient decision makers in the simulated operations system, were organised to play with the game models in 48 knowledge elicitation sessions over 19 weeks. In so doing, example cases were collected during the personnel's interactions with the game models. Lastly, the example cases were processed and analysed, and the findings were discussed. Eventually, it seems that the decisions elicited through a 2-Dimensional (2D) VIS model are probably more realistic than those elicited through other equivalent models with a higher level of visual fidelity. Moreover, the former also emerges to be a more efficient knowledge elicitation tool. In addition, it appears that the decisions elicited through a VIS model that is adjusted to simulate more uncommon and extreme scenes are made for a wider range of situations. Consequently, it can be concluded that using a 2D VIS model that has been adjusted to simulate more uncommon and extreme situations is the optimal VIS-based means for eliciting episodic knowledge

Simulating Knowledge-Generation and -Distribution Processes in Innovation Collaborations and Networks

An agent-based simulation model representing a theory of the dynamic processes involved in innovation in modern knowledge-based industries is described. The agent-based approach al-lows the representation of heterogeneous agents that have individual and varying stocks of knowledge. The simulation is able to model uncertainty, historical change, effect of failure on the agent population, and agent learning from experience, from individual research and from partners and collaborators. The aim of the simulation exercises is to show that the artificial innovation networks show certain characteristics they share with innovation networks in knowledge intensive industries and which are difficult to be integrated in traditional models of industrial economics.innovation networks, agent-based modelling, scale free networks

An Algorithm for Probabilistic Alternating Simulation

In probabilistic game structures, probabilistic alternating simulation (PA-simulation) relations preserve formulas defined in probabilistic alternating-time temporal logic with respect to the behaviour of a subset of players. We propose a partition based algorithm for computing the largest PA-simulation, which is to our knowledge the first such algorithm that works in polynomial time, by extending the generalised coarsest partition problem (GCPP) in a game-based setting with mixed strategies. The algorithm has higher complexities than those in the literature for non-probabilistic simulation and probabilistic simulation without mixed actions, but slightly improves the existing result for computing probabilistic simulation with respect to mixed actions.Comment: We've fixed a problem in the SOFSEM'12 conference versio