Models of incremental concept formation

Given a set of observations, humans acquire concepts that organize those observations and use them in classifying future experiences. This type of concept formation can occur in the absence of a tutor and it can take place despite irrelevant and incomplete information. A reasonable model of such human concept learning should be both incremental and capable of handling this type of complex experiences that people encounter in the real world. In this paper, we review three previous models of incremental concept formation and then present CLASSIT, a model that extends these earlier systems. All of the models integrate the process of recognition and learning, and all can be viewed as carrying out search through the space of possible concept hierarchies. In an attempt to show that CLASSIT is a robust concept formation system, we also present some empirical studies of its behavior under a variety of conditions

Heuristic assignment of CPDs for probabilistic inference in junction trees

Many researches have been done for efficient computation of probabilistic queries posed to Bayesian networks (BN). One of the popular architectures for exact inference on BNs is the Junction Tree (JT) based architecture. Among all the different architectures developed, HUGIN is the most efficient JT-based architecture. The Global Propagation (GP) method used in the HUGIN architecture is arguably one of the best methods for probabilistic inference in BNs. Before the propagation, initialization is done to obtain the potential for each cluster in the JT. Then with the GP method, each cluster potential becomes cluster marginal through passing messages with its neighboring clusters. Improvements have been proposed by many researchers to make this message propagation more efficient. Still the GP method can be very slow for dense networks. As BNs are applied to larger, more complex, and realistic applications, developing more efficient inference algorithm has become increasingly important. Towards this goal, in this paper, we present some heuristics for initialization that avoids unnecessary message passing among clusters of the JT and therefore it improves the performance of the architecture by passing lesser messages

Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies

An algorithm that learns from a set of examples should ideally be able to exploit the available resources of (a) abundant computing power and (b) domain-specific knowledge to improve its ability to generalize. Connectionist theory-refinement systems, which use background knowledge to select a neural network's topology and initial weights, have proven to be effective at exploiting domain-specific knowledge; however, most do not exploit available computing power. This weakness occurs because they lack the ability to refine the topology of the neural networks they produce, thereby limiting generalization, especially when given impoverished domain theories. We present the REGENT algorithm which uses (a) domain-specific knowledge to help create an initial population of knowledge-based neural networks and (b) genetic operators of crossover and mutation (specifically designed for knowledge-based networks) to continually search for better network topologies. Experiments on three real-world domains indicate that our new algorithm is able to significantly increase generalization compared to a standard connectionist theory-refinement system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file

Hybrid routing and bridging strategies for large scale mobile ad hoc networks

Multi-hop packet radio networks (or mobile ad-hoc networks) are an ideal technology to establish instant communication infrastructure for military and civilian applications in which both hosts and routers are mobile. In this dissertation, a position-based/link-state hybrid, proactive routing protocol (Position-guided Sliding-window Routing - PSR) that provides for a flat, mobile ad-hoc routing architecture is described, analyzed and evaluated. PSR is based on the superposition of link-state and position-based routing, and it employs a simplified way of localizing routing overhead, without having to resort to complex, multiple-tier routing organization schemes. A set of geographic routing zones is defined for each node, where the purpose of the ith routing zone is to restrict propagation of position updates, advertising position differentials equal to the radius of the (i-i )th routing zone. Thus, the proposed protocol controls position-update overhead generation and propagation by making the overhead generation rate and propagation distance directly proportional to the amount of change in a node\u27s geographic position. An analytical model and framework is provided, in order to study the various design issues and trade-offs of PSR routing mechanism, discuss their impact on the protocol\u27s operation and effectiveness, and identify optimal values for critical design parameters, under different mobility scenarios. In addition an in-depth performance evaluation, via modeling and simulation, was performed in order to demonstrate PSR\u27s operational effectiveness in terms of scalability, mobility support, and efficiency. Furthermore, power and energy metrics, such as path fading and battery capacity considerations, are integrated into the routing decision (cost function) in order to improve PSR\u27s power efficiency and network lifetime. It is demonstrated that the proposed routing protocol is ideal for deployment and implementation especially in large scale mobile ad hoc networks. Wireless local area networks (WLAN) are being deployed widely to support networking needs of both consumer and enterprise applications, and IEEE 802.11 specification is becoming the de facto standard for deploying WLAN. However IEEE 802.11 specifications allow only one hop communication between nodes. A layer-2 bridging solution is proposed in this dissertation, to increase the range of 802.11 base stations using ad hoc networking, and therefore solve the hotspot communication problem, where a large number of mobile users require Internet access through an access point. In the proposed framework nodes are divided into levels based on their distance (hops) from the access point. A layer-2 bridging tree is built based on the level concept, and a node in certain level only forwards packets to nodes in its neighboring level. The specific mechanisms for the forwarding tree establishment as well as for the data propagation are also introduced and discussed. An analytical model is also presented in order to analyze the saturation throughput of the proposed mechanism, while its applicability and effectiveness is evaluated via modeling and simulation. The corresponding numerical results demonstrate and confirm the significant area coverage extension that can be achieved by the solution, when compared with the conventional 802.1 lb scheme. Finally, for implementation purposes, a hierarchical network structure paradigm based on the combination of these two protocols and models is introduced

Multiple solutions based particle swarm optimization for cluster-head-selection in wireless-sensor-network

Wireless sensor network (WSN) has a significant role in wide range of scientific and industrial applications. In WSN, within the operation area of sensor nodes the nodes are randomly deployed. The constraint related to energy is considered as one of the major challenges for WSN, which may not only affect the sensor nodes efficiency but also influences the operational capabilities of the network. Therefore, numerous attempts of researches have been proposed to counter this energy problem in WSN. Hierarchical clustering approaches are popular techniques that offered the efficient consumption of the energy in WSN. In addition to this, it is understood that the optimum choice of sensor as cluster head can critically help to reduce the energy consumption of the sensor node. In recent years, metaheuristic optimization is used as a proposed technique for the optimal selection of cluster heads. Furthermore, it is noteworthy here that proposed techniques should be efficient enough to provide the optimal solution for the given problem. Therefore, in this regard, various attempts are made in the form of modified versions or new metaheuristic algorithms for optimization problems. The research in the paper offered a modified version of particle-swarm-optimization (PSO) for the optimal selection of sensor nodes as cluster heads. The performance of the suggested algorithm is experimented and compared with the renowned optimization techniques. The proposed approach produced better results in the form of residual energy, number of live nodes, sum of dead nodes, and convergence rate

Adaptive laser link reconfiguration using constraint propagation

This paper describes Harris AI research performed on the Adaptive Link Reconfiguration (ALR) study for Rome Lab, and focuses on the application of constraint propagation to the problem of link reconfiguration for the proposed space based Strategic Defense System (SDS) Brilliant Pebbles (BP) communications system. According to the concept of operations at the time of the study, laser communications will exist between BP's and to ground entry points. Long-term links typical of RF transmission will not exist. This study addressed an initial implementation of BP's based on the Global Protection Against Limited Strikes (GPALS) SDI mission. The number of satellites and rings studied was representative of this problem. An orbital dynamics program was used to generate line-of-site data for the modeled architecture. This was input into a discrete event simulation implemented in the Harris developed COnstraint Propagation Expert System (COPES) Shell, developed initially on the Rome Lab BM/C3 study. Using a model of the network and several heuristics, the COPES shell was used to develop the Heuristic Adaptive Link Ordering (HALO) Algorithm to rank and order potential laser links according to probability of communication. A reduced set of links based on this ranking would then be used by a routing algorithm to select the next hop. This paper includes an overview of Constraint Propagation as an Artificial Intelligence technique and its embodiment in the COPES shell. It describes the design and implementation of both the simulation of the GPALS BP network and the HALO algorithm in COPES. This is described using a 59 Data Flow Diagram, State Transition Diagrams, and Structured English PDL. It describes a laser communications model and the heuristics involved in rank-ordering the potential communication links. The generation of simulation data is described along with its interface via COPES to the Harris developed View Net graphical tool for visual analysis of communications networks. Conclusions are presented, including a graphical analysis of results depicting the ordered set of links versus the set of all possible links based on the computed Bit Error Rate (BER). Finally, future research is discussed which includes enhancements to the HALO algorithm, network simulation, and the addition of an intelligent routing algorithm for BP

A statistical mechanics approach for an effective, scalable, and reliable distributed load balancing scheme for grid networks

The advances in computer and networking technologies over the past decades produced new type of collaborative computing environment called Grid Networks. Grid network is a parallel and distributed computing network system that possesses the ability to achieve a higher computing throughput by taking advantage of many computing resources available in the network. To achieve a scalable and reliable Grid network system, the workload needs to be efficiently distributed among the resources accessible on the network. A novel distributed algorithm based on statistical mechanics that provides an efficient load-balancing paradigm without any centralised monitoring is proposed here. The resulting load-balancer would be integrated into Grid network to increase its efficiency and resources utilisation. This distributed and scalable load-balancing framework is conducted using the biased random sampling (BRS) algorithm. In this thesis, a novel statistical mechanics approach that gives a distributed loadbalancing scheme by generating almost regular networks is proposed. The generated network system is self-organised and depends only on local information for load distribution and resource discovery. The in-degree of each node refers to its free resources, and job assignment and resource updating processes required for load balancing are accomplished by using random sampling (RS). An analytical solution for the stationary degree distributions has been derived that confirms that the edge distribution of the proposed network system is compatible with ER random networks. Therefore, the generated network system can provide an effective loadbalancing paradigm for the distributed resources accessible on large-scale network 1 systems. Furthermore, it has been demonstrated that introducing a geographic awareness factor in the random walk sampling can reduce the effects of communication latency in the Grid network environment. Theoretical and simulation results prove that the proposed BRS load-balancing scheme provides an effective, scalable, and reliable distributed load-balancing scheme for the distributed resources available on Grid networks

Scalable wireless sensor networks for dynamic communication environments: simulation and modelling

This thesis explores the deployment of Wireless Sensor Networks (WSNs) on localised maritime events. In particular, it will focus on the deployment of a WSN at sea and estimating what challenges derive from the environment and how they affect communication. This research addresses these challenges through simulation and modelling of communication and environment, evaluating the implications of hardware selection and custom algorithm development. The first part of this thesis consists of the analysis of aspects related to the Medium Access Control layer of the network stack in large-scale networks. These details are commonly hidden from upper layers, thus resulting in misconceptions of real deployment characteristics. Results show that simple solutions have greater advantages when the number of nodes within a cluster increases. The second part considers routing techniques, with focus on energy management and packet delivery. It is shown that, under certain conditions, relaying data can increase energy savings, while at the same time allows a more even distribution of its usage between nodes. The third part describes the development of a custom-made network simulator. It starts by considering realistic radio, channel and interference models to allow a trustworthy simulation of the deployment environment. The MAC and Routing techniques developed thus far are adapted to the simulator in a cross-layer manner. The fourth part consists of adapting the WSN behaviour to the variable weather and topology found in the chosen application scenario. By analysing the algorithms presented in this work, it is possible to find and use the best alternative under any set of environmental conditions. This mechanism, the environment-aware engine, uses both network and sensing data to optimise performance through a set of rules that involve message delivery and distance between origin and cluster hea