Plans, Actions and Dialogues Using Linear Logic

We describe how Intuitionistic Linear Logic can be used to provide a unified logical account for agents to find and execute plans. This account supports the modelling of agent interaction, including dialogue; allows agents to be robust to unexpected events and failures; and supports significant reuse of agent specifications. The framework has been implemented and several case studies have been considered. Further applications include human–computer interfaces as well as agent interaction in the semantic web.     

Supporting Unified Distributed Management and Autonomic Decisions: Design,Implementation and Deployment

Nowadays, the prevailing use of networks based on traditional centralized management systems reflects on a fast increase of the management costs. The growth in the number of network equipments and services reinforces the need to distribute the management responsibilities throughout the network devices. In this approach, each device executes common network management functionalities, being part of the overall network management platform. In this paper, we present a Unified Distributed Network Management (UDNM) framework that provides a unified (wired and wireless) management network solution, where further different network services can take part of this infrastructure, e.g., flow monitoring, accurate routing decisions, distributed policies dissemination, etc. This framework is divided in two main components: (A) Situation awareness, which sets up initial information through bootstrapping, discovery, fault-management process and exchange of management information; (B) Autonomic Decision System (ADS) that performs distributed decisions in the network with incomplete information. We deploy the UDNM framework in a testbed which involves two cities (\(\approx\)250 km between), different standards (IEEE 802.3, IEEE 802.11 and IEEE 802.16e) and network technologies, such as, wired virtual grid, wireless ad-hoc gateways, ad-hoc mobile access devices. The UDNM framework integrates management functionalities into the managed devices, proving to be a lightweight and easy-respond framework. The performance analysis shows that the UDNM framework is feasible to unify devices management functionalities and to take accurate decisions on top of a real network.     

Distributed planning and control systems for the virtual enterprise: organizational requirements and development life-cycle

This paper describes the requirements analysis and system specification of an Order Promise module to be used as part of a broader Decision Support System for production and operations planning of a Virtual Enterprise. This work is part of a broader project with a particular focus on the microelectronics industry which is a good example of Virtual Enterprise, and where a quick response to the customers needs and to unpredictable changes in production conditions is considered a major factor for success. First, the analysis and specification are presented within a development framework that involves the study of organizational issues of semiconductor enterprises. The use of ontological engineering for supporting the communication and shared understanding of the system concepts is explained and a virtual enterprise ontology is outlined. Following the clarification of the concept of virtual enterprise, the generic techno-organizational requirements for the information system are derived. Finally, a specification of the global planning module and a more detailed one regarding the order promise module is presented.     

Learning based brain emotional intelligence as a new aspect for development of an alarm system

The multi criteria and purposeful prediction approach has been introduced and is implemented by the fast and efficient behavioral based brain emotional learning method. On the other side, the emotional learning from brain model has shown good performance and is characterized by high generalization property. New approach is developed to deal with low computational and memory resources and can be used with the largest available data sets. The scope of paper is to reveal the advantages of emotional learning interpretations of brain as a purposeful forecasting system designed to warning; and to make a fair comparison between the successful neural (MLP) and neurofuzzy (ANFIS) approaches in their best structures and according to prediction accuracy, generalization, and computational complexity. The auroral electrojet (AE) index are used as practical examples of chaotic time series and introduced method used to make predictions and warning of geomagnetic disturbances and geomagnetic storms based on AE index.     

The renal factor in the post-traumatic "fluid overload" syndrome

Hypervolemia with hypertension often occurs 36-72 hours following massive blood and fluid replacement for hypovolemic shock. This syndrome of "fluid overload" has been attributed to the rapid intravascular flux of previously sequestered fluid in patients with impaired diuresis. This hypothesis was tested in 35 injured patients who received a mean of 9.3 L of blood and 17.4 L of salt during resucitation. The renal parameters measured soon after resuscitation included: 1) renal clearance of inulin (GFR), para-amino hippurate (ERPF), milliosmoles, sodium, and free water; 2) inulin space, renal vascular resistance (RVR), O2 consumption, renin, renal blood flow (RBF), and response to furosemide. Eighteen patients developed hypertension, hypervolemia, and respiratory insufficiency. When compared to the 17 normovolemic, non-hypertensive patients, the 18 hypervolemic patients had significantly increased RVR, with a significant decrease in RBF despite an increase in plasma volume and cardiac output. Furosemide produced less diuresis and natriuresis in the hypertensive patients. The balance between hypovolemia and "fluid overload" seemed percarious in the hypertensive patients. Peripheral renin and catecholamine levels were normal in both groups. Patients with post-traumatic "fluid overload" appear to have a combination of hypervolemia, respiratory insufficiency, hypertension, increased cardiac output, decreased extracellular fluid space, and decreased renal perfusion. These findings suggest that decreased interstitial fluid space compliance rather than "fluid overload" is the underlying factor leading to respiratory insufficiency. The therapeutic aspects of these findings are discussed.     

Protein analysis as a measure of active biomass in activated sludge

Conventional methods to determine the biomass in activated sludge are the measurement of total suspended solids (TSS) and volatile suspended solids (VSS). Such methods do not distinguish between active biomass and inactive organic material. In this study, biomass was determined with both conventional methods and also through measuring the protein content with the modified Lowry method. In order to investigate the relationship between activity and biomass concentration in terms of TSS, VSS and protein content, some starvation experiments were conducted. It was found that the protein fraction of VSS differs under different starvation conditions. The biological activity of the activated sludge was measured as oxygen uptake rate (OUR). The strongest correlation could be measured between protein and OUR under various conditions. The results show that protein is an appropriate parameter for the measurement of the biological activity.     

A consistent stable numerical scheme for a nonlinear option pricing model in illiquid markets

Markets liquidity is an issue of very high concern in financial risk management. In a perfect liquid market the option pricing model becomes the well-known linear Black–Scholes problem. Nonlinear models appear when transaction costs or illiquid market effects are taken into account. This paper deals with the numerical analysis of nonlinear Black–Scholes equations modeling illiquid markets when price impact in the underlying asset market affects the replication of a European contingent claim. Numerical analysis of a nonlinear model is necessary because disregarded computations may waste a good mathematical model. In this paper we propose a finite-difference numerical scheme that guarantees positivity of the solution as well as stability and consistency.     

A general computational recognition primed decision model with multi-agent rescue simulation benchmark

Analytical decision making strategies rely on weighing pros and cons of multiple options in an unbounded rationality manner. Contrary to these strategies, recognition primed decision (RPD) model which is a primary naturalistic decision making (NDM) approach assumes that experienced and professional decision makers when encounter problems in real operating conditions are able to use their previous experiences and trainings in order to diagnose the problem, recall the appropriate solution, evaluate it mentally, and implement it to handle the problem in a satisficing manner. In this paper, a computational form of RPD, now called C-RPD, is presented. Unified Modeling Language was used as a modeling language to represent the proposed C-RPD model in order to make the implementation easy and obvious. To execute the model, RoboCup Rescue agent simulation environment, which is one of the best and the most famous complex and multi-agent large-scale environments, was selected. The environment simulates the incidence of fire and earthquakes in urban areas where it is the duty of the police forces, firefighters and ambulance teams to control the crisis. Firefighters of SOS team are first modeled and implemented by utilizing C-RPD and then the system is trained using an expert’s experience. There are two evaluations. To find out the convergence of different versions developed during experience adding, some of the developed versions are chosen and evaluated on seven maps. Results show performance improvements. The SOS team ranked first in an official world championship and three official open tournaments.     

Detecting improvement using Shewhart attribute control charts when the lower control limit is zero

In this paper, we present a method to monitor count data so as to be able to detect improvement when the counts are low enough to cause the lower limit to be zero. The method, which is proposed as an add-on to the conventional Shewhart control chart, consists in counting the number of samples in which zero defectives or zero defects per unit occur and signaling an increase in quality if k-in-a-row or 2-in-t samples have zero counts of defectives or zero defects per unit. This method enjoys some similarities to the very popular Shewhart control chart in that it is easy to design, understand and use. It is flexible, robust, and, like the Shewhart chart, yields detection frequencies that are optimal for very large shifts and good for other shifts. Some comparisons with traditional CUSUM charts are provided. Figures enabling Shewhart control chart users to easily design low-side add-on control charts are given for c and np charts.     

Disciplining Information Systems: Truth and its Regimes

This paper provides a critical discussion of the ongoing concern of Information Systems (IS) academics on the status of IS as an academic discipline. The focal claim of the argument is that the status of IS as an ‘academic discipline’ is not an ontological or an epistemological question but rather a political one. In defending this claim the paper will draw on the work of Foucault, in particular his discussion on the relation between power and knowledge. The paper will conclude that the advice given by Paul (2002) and others may be appropriate but for very different reasons. It will claim that it is equally important, if not more so, to know the reasons why claims to legitimacy may succeed or not, for such claims to be effective as a political programme.