Mohamad Allouche

Amalgamation in cartographic generalization using Kohonen's feature nets

Empirical observations of the way cartographers deal with generalization problems lead to the hypothesis that they first detect patterns of anomalies in the cartographic data set and then eliminate anomalies by transforming the data. Automatically identifying patterns of anomalies on the map is a difficult task when using GIS functions or traditional algorithmic approaches. Techniques based on the use of neural networks have been widely used in artificial intelligence in order to solve pattern‐recognition problems. In this paper, we explore how Kohonen‐type neural networks can be used to deal with map generalization applications in which the main problem is to identify high‐density regions that include cartographic elements of the same type. We also propose an algorithm to replace cartographic elements located in a region by its surrounding polygon. The use of this type of neural network permitted us to generate different levels of grouping according to the chosen zoom‐scale on the map. These levels correspond to a multiple representation of the generalized cartographic elements. As an illustration, we apply our approach to the automatic replacement of a group of houses represented as a set of very close points in the original data set, by a polygon representing the corresponding urban area in the generalized map.

Transportation risk analysis using probabilistic model checking

Elaboration of an approach for transportation risk assessment and contingency evaluation.Modeling risk prone transportation tasks as composed Markov Decision Process (MDP).Assessment of transportation tasks expressed as MDP via probabilistic model checking.Provision of decision making support via decision trees built from the model checking output.Evaluation of risk related properties expressed in probabilistic temporal logic. Transportation and supply chain activities represent essential components in many endeavors covering both public and private domains. However, the underlying transport networks are complex and potentially fragile due to weather, natural disasters or other risk factors. Thus, assessing transportation related risk represents a key decision support capability along with the ability to evaluate contingency options for risk mitigation. In this paper, we address these issues by adopting probabilistic model checking to evaluate the risk and contingency options related to transportation tasks. In this pursuit, risk related properties are assessed for behavioral models capturing the transport system. Moreover, we show the usefulness of constructing decision trees that can provide insightful means of risk appraisal. The proposed approach can help decision makers evaluate contingency options and determine lower and upper cost bounds for risky transportation tasks such as those involved in humanitarian aid provision. The proposed approach is also illustrated with a case study.

Energy-Efficient Monitor Deployment in Collaborative Distributed Setting

Monitoring large supply networks can be efficiently performed by employing active radio-frequency identification (a-RFID) sensors. Relay nodes collect and process workflow information from the sensors. The knowledge derived from the collected information is periodically communicated to monitors using onboard energy sources. Energy can be saved by communicating with closer monitors. In this setup, multiple decision makers with their own deployment budget collaborate to deploy monitors for minimizing energy consumption of the relay nodes. The contribution of this paper is threefold. It elaborates a mathematical model for distributed monitor deployment in budget-constrained supply-chain networks, it proposes a heuristic technique to find near-optimal allocation of monitors in tractable manner, and it illustrates a collaborative procedure to jointly minimize energy consumption along with a fair sharing of total deployment cost among multiple decision makers. The approach is presented through a case study. In addition, benchmark results are provided for a number of problem instances.

Hierarchy aware distributed plan execution monitoring

Elaboration of a hierarchy aware and distributed monitoring approach suitable for shared information awareness.Aggregation of distributed nodes into clusters and cluster heads in order to localize the information exchange at the level of the distributed nodes.Gossip based communication across the clusters along with asymmetric clustering to reflect hierarchical relationships among participants.Formalization of the information sharing technique based on communicating Markov Decision Processes and probability analysis using probabilistic model checking. Collaborative plan execution is becoming increasingly important given its potential for operational agility and cost reduction. In this paper we propose a distributed and hierarchy aware monitoring procedure for operational plan execution taking place in a dynamic environment characterized by unreliable communication and exogenous events. The contribution of this paper consists in employing a hierarchical clustering approach supporting a multi-party and hierarchy aware information sharing mechanism that is resilient to disruptions in the execution environment. The proposed distributed monitoring procedure uses asymmetric clustering to reflect hierarchical relationships along with gossip based communication across the clusters. Of significance is the information sharing mechanism formalization which utilizes a fresh information window in conjunction with communicating Markov Decision Processes. We show the usefulness of assessing shared information awareness via probabilistic model checking for various combinations of clustering topology and disruption conditions. In this context, we assess formal specifications expressed in probabilistic temporal logic and show how the model checking results can be used to derive the best fresh window value to maximize an information awareness utility function. An illustrative case study is also presented.

Gossiping Based Distributed Plan Monitoring

Abstract Joint plan execution is gaining momentum due to its benefits in terms of cost effectiveness and operational agility. In this paper, we introduce a lightweight gossip based multi-agent distributed protocol for plan execution monitoring in a dynamic environment characterized by unreliable communication links and exogenous events. The information obtained from the monitoring process can be used as support for detecting plan deviations and applying corrective measures. The contribution of this paper consists in the elaboration of an agent centric information sharing mechanism that is resilient to adverse changes in the execution environment by exhibiting a high degree of tolerance to communication errors. The distributed monitoring procedure is elaborated along with a relevant case study and experimental results.

Evolutionary learning algorithm for reliable facility location under disruption

Abstract Facility location represents an important supply chain problem aiming at minimizing facility establishment and transportation cost to meet customer demands. Many facility location problem (FLP) instances can be modelled as p-median problems (PMP) and uncapacitated facility location (UFL) problems. While, most solution approaches assume totally reliable deployed facilities, facilities often experience disruptions and their failure often leads to a notably higher cost. Therefore, determination of facility locations and fortification of a subset of them within a limited budget are crucial to supply chain organizations to provide cost effective services in presence of probable disruptions. We propose an evolutionary learning technique to near-optimally solve two research problems: Reliable p-Median Problem and Reliable Uncapacitated Facility Location Problem considering heterogeneous facility failure probabilities, one layer of backup and limited facility fortification budget. The technique is illustrated using a case study and its performance is evaluated via benchmark results. We also provide an analysis on the effects on facility location by prioritizing customer demands and adopting geographic distance calculation. The approach allows fast generation of cost-effective and complete solution using reasonable computing power. Moreover, the underlying technique is customizable offering a trade-off between solution quality and computation time.

Partitioning of transportation networks under disruption

Abstract Graph partitioning is an optimization problem that deals with dividing a large geographical transportation network into subnetworks in favor of balancing the workload and minimizing the communication among them. Over the past decades, various models have been developed in such a way to satisfy a multi-objective problem such as delivery time and managerial cost. In real life, because of inevitable changes during network’s lifetime, it is vital to offer survivability and resilience in the existence of network failure and disruption. This paper proposes a partitioning technique called Hierarchical recursive progression1+ (HRP1+) that is scalable and can deal with large and complex networks. The HRP1+ method is an extension of the HRP1 algorithm. The approach is tested on benchmark data-sets using different disruption scenarios, including partial and complete disruptions on network edges and nodes.

Towards a Distributed Plan Execution Monitoring Framework

Abstract Distributed monitoring is challenging yet essential in order to address scalability issues observed in the context of large-scale plan execution. A formal framework can be very helpful in analyzing and reasoning about plan spec- ification, execution, and monitoring. In this paper, we elaborate on a distributed monitoring calculus framework that allows specifying and executing plans for multi-agent systems in a distributed environment. The framework allows taking into account a highly dynamic and uncertain environment that can be a contributor to the changing conditions possibly disrupting and causing the plan to fail. Furthermore, the calculus provides sound foundations for designing and evaluating monitoring algorithms and protocols. In order to achieve effective monitoring, we propose an automata- based approach, inspired by runtime security verification research initiatives. The proposed automata allow enforcing monitoring properties while the given plan is executed at the agents side.

The Fusion of Fuzzy Temporal Plans: Managing Uncertainty and Time in Decentralized Command and Control Systems

Managing uncertainty in planning and plan execution activities is a key issue. This issue is even more critical in a network enabled environment where several tasks are distributed over the environment and can be carried out by different partners under different temporal constraints. In previous work (Allouche & Boukhtouta, 2009) a framework for distributed temporal plan fusion and monitoring has been proposed. A set of agents are tasked to coordinate the execution of different plans with different temporal constraints. Those plans are fused into one single plan, called coordinated plan. A coordinated plan can be executed and monitored by several agents while respecting the original temporal constraints of each agent’s plan. The temporal constrains are set on tasks duration or/and between tasks. Each temporal constraint specifies the minimum and maximum authorized temporal distance between two events that typically represents the beginning or the end of execution of a task. In our opinion, the choice of such temporal constraints is not realistic in a distributed environment where different players must execute a common mission with limited and incomplete knowledge of their environment. The violation of a temporal constraint even with one unit of time will cause the plan execution to fail. For example, if a temporal constraint specifies that the duration of a task should last between 10 and 20 minutes, the fact that the task duration is 21 minutes is sufficient for the failure of any plan that contains this task. The fact that this situation is very likely to happen and that one minute late might be acceptable for a decision-maker, a new framework with degrading solutions for the problem is needed. In this work, we use fuzzy temporal constrains to maintain the execution of a plan with a degradation of its performance. In this context, the decision-maker decides whether the current execution is acceptable or not. The following sections are organized as follows: Section 2 presents some related work. Section 3 presents a general framework for the fusion of fuzzy temporal plans. This framework is then applied to a Combat Search and Rescue (CSAR) mission in Section 4. Finally, Section 5 presents some conclusions and future work.