Animation modeling with petri nets

This paper introduces the use of Petri Nets as a modeling and analysis tool for animation environments. Firstly, the original formulation for Petri Nets is applied in two animation situations, one modeled as a state machine and another exploring interdependent transitions. Increasing the complexity level, some modeling extensions are discussed and more sophisticated animation examples are studied.     

Specification of software pipelining using petri nets

This paper presents a flexible model for software pipelining using the petri nets. Our technique, called the Petri Net Pacemaker (PNP), can create near optimal pipelines with less algorithmic effort than other techniques. The pacemaker is a novel idea which exploits the cyclic behavior of petri nets to model the problem of scheduling operations of a loop body for software pipelining. A way of improving the performance of loops containing predicates is given. The PNP technique also shows how nested loops can be pipelined. A comparison with some of the other techniques is presented. THis work was partially supported by the National Science Foundation under grants CDA-9100788 and CDA-9200371.     

Discovery of activity composites using topic models: An analysis of unsupervised methods

In this work we investigate unsupervised activity discovery approaches using three topic model (TM) approaches, based on Latent Dirichlet Allocation (LDA), $n$-gram TM (NTM), and correlated TM (CTM). While LDA structures activity primitives, NTM adds primitive sequence information, and CTM exploits co-occurring topics. We use an activity composite/primitive abstraction and analyze three public datasets with different properties that affect the discovery, including primitive rate, activity composite specificity, primitive sequence similarity, and composite-instance ratio. We compare the activity composite discovery performance among the TM approaches and against a baseline using $k$-means clustering. We provide guidelines for method and optimal TM parameter selection, depending on data properties and activity primitive noise. Results indicate that TMs can outperform $k$-means clustering up to 17%, when composite specificity is low. LDA-based TMs showed higher robustness against noise compared to other TMs and $k$-means.     

Enhancing activity recognition using CPD-based activity segmentation

Segmenting behavior-based sensor data and recognizing the activity that the data represents are vital steps in all applications of human activity learning such as health monitoring, security, and intervention. In this paper, we enhance activity recognition by identifying activity transitions. To accomplish this goal, we introduce a change point detection-based activity segmentation model which partitions behavior-driven sensor data into non-overlapping activities in real time. In addition to providing valuable activity information, activity segmentation also can be used to improve the performance of activity recognition. We evaluate our proposed segmentation-enhanced activity recognition method on data collected from 29 smart homes. Results of this analysis indicate that the method not only provides useful information about activity boundaries and transitions between activities but also increases recognition accuracy by 7.59% and f measure by 6.69% in comparison with the traditional window-based methods.     

Analysis of contract net in multi-agent systems

Application of contract net protocol requires the development of a bid evaluation procedure specific to the problem. Care must be taken to apply contract net protocol to tasks that involve precedence constraints among different operations and heterogeneous resources. The lack of a process model in the original contract net protocol makes it difficult to determine the feasibility of the resulting contracts. We propose a model to facilitate the development of the bid evaluation procedure by extending our previous results to handle tasks with more complex process structure. We formulate an optimization problem to find a minimal cost feasible execution sequence for a task.     

基于广义随机Petri网的CIMS多任务可靠性研究

CIMS生产的复杂性要求其可靠性模型可以精确地反映生产过程,由于传统的可靠性建模方法无法兼顾CIMS的时间动态特性,所以针对一个复杂的有多个加工任务的CIMS制造单元,由于其机器加工工件的时间均为指数分布,则采用广义随机Petri网进行多任务可靠性建模,在此基础上基于Petri网行为表达式,将矩母函数思想引入其中,不必生成可达标识图就可通过计算模型的传递函数,进而得到整个系统的多任务可靠度,该可靠性指标可以更直观地反映具有多种加工任务的整个CIMS的运行性能。     

A study on modeling of human spatial behavior using multi-agent technique

This research follows the design and implementation of an agent-based modeling environment written in Java program language on AnyLogic simulation platform to facilitate observing the human spatial behaviors of electric taxis and passengers. The system is developed with a view to improve decision analysis and support for users or companies. In order to achieve the object of decision support for the user, we provide a graphic user interface (GUI) to support user making decision real time. User can simulate the real condition via GUI in our electric taxi DAR operation system to observe the simulation process and the result to help reasonable decision-making immediately. We validate our multi-agent simulation model with the electric taxi DAR operation system case study. Finally, according to the analysis result, we demonstrate that our multi-agent simulation model and GUI can help users or companies quickly make a quality and accurate decision to reduce the decision-making cost and time. In this condition, users or companies can strengthen their competitive advantage and response ability with the reduction of decision-making risk.     

The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition

There is a growing interest on using ambient and wearable sensors for human activity recognition, fostered by several application domains and wider availability of sensing technologies. This has triggered increasing attention on the development of robust machine learning techniques that exploits multimodal sensor setups. However, unlike other applications, there are no established benchmarking problems for this field. As a matter of fact, methods are usually tested on custom datasets acquired in very specific experimental setups. Furthermore, data is seldom shared between different groups. Our goal is to address this issue by introducing a versatile human activity dataset recorded in a sensor-rich environment. This database was the basis of an open challenge on activity recognition. We report here the outcome of this challenge, as well as baseline performance using different classification techniques. We expect this benchmarking database will motivate other researchers to replicate and outperform the presented results, thus contributing to further advances in the state-of-the-art of activity recognition methods.     

Multi-agent event recognition by preservation of spatiotemporal relationships between probabilistic models

We present a new method for multi-agent activity analysis and recognition that uses low level motion features and exploits the inherent structure and recurrence of motion present in multi-agent activity scenarios.     

Video understanding for complex activity recognition

This paper presents a real-time video understanding system which automatically recognises activities occurring in environments observed through video surveillance cameras. Our approach consists in three main stages: Scene Tracking, Coherence Maintenance, and Scene Understanding. The main challenges are to provide a robust tracking process to be able to recognise events in outdoor and in real applications conditions, to allow the monitoring of a large scene through a camera network, and to automatically recognise complex events involving several actors interacting with each others. This approach has been validated for Airport Activity Monitoring in the framework of the European project AVITRACK.     

Emergent intelligent properties of progressively structured pattern recognition nets

The n-tuple recognition net is seen as a building brick of a progression of network structures. The emergent ‘intelligent’ properties of such systems are discussed. They include the amplification of confidence for the recognition of images that differ in small detail, a short term memory of the last seen image, sequence sensitivity, sequence acceptance and saccadic inspection as an aid in scene analysis.     

Performance evaluation of SDIAGENT, a multi-agent system for distributed fuzzy geospatial data conflation

A rapid growth of available geospatial data requires development of systems capable of autonomous data retrieval, integration and validation. Mobile agents may provide the suitable framework for developing such systems since this technology, in a natural way, can deal with the distributed heterogeneous nature of such data. In this paper, we evaluate SDIAGENT our, recently introduced, multi-agent architecture for geospatial data integration and conflation, and compare its model performance with that of client/server and single-agent approaches. Experimental results for several realistic scenarios, under varying conditions, are presented for these three system architectures. We analyze the performance alteration for various numbers of participating nodes, varying amount of database accesses, processing loads, and network loads.     

Unsupervised learning for human activity recognition using smartphone sensors

To provide more sophisticated healthcare services, it is necessary to collect the precise information on a patient. One impressive area of study to obtain meaningful information is human activity recognition, which has proceeded through the use of supervised learning techniques in recent decades. Previous studies, however, have suffered from generating a training dataset and extending the number of activities to be recognized. In this paper, to find out a new approach that avoids these problems, we propose unsupervised learning methods for human activity recognition, with sensor data collected from smartphone sensors even when the number of activities is unknown. Experiment results show that the mixture of Gaussian exactly distinguishes those activities when the number of activities k is known, while hierarchical clustering or DBSCAN achieve above 90% accuracy by obtaining k based on Caliński–Harabasz index, or by choosing appropriate values for ɛ and MinPts when k is unknown. We believe that the results of our approach provide a way of automatically selecting an appropriate value of k at which the accuracy is maximized for activity recognition, without the generation of training datasets by hand.     

Pinning synchronization of networked multi-agent systems: spectral analysis

Pinning synchronization of a networked multi-agent system with a directed communication topology is investigated from a spectral analysis approach. Some new types of synchronized regions for networked ...     

Rationality of reward sharing in multi-agent reinforcement learning

In multi-agent reinforcement learning systems, it is important to share a reward among all agents. We focus on theRationality Theorem of Profit Sharing ⁵⁾ and analyze how to share a reward among all profit sharing agents. When an agent gets adirect reward R (R>0), anindirect reward μR (μ≥0) is given to the other agents. We have derived the necessary and sufficient condition to preserve the rationality as follows;

多智能体遗传算法投影寻踪建模与实证研究

采用多智能体遗传算法（MGA）进行投影寻踪聚类（PPC）建模,对投影向量约束条件采用两种不改变迭代进化过程的归一化处理方法,经三种不同类型的数据分别进行建模,得到了相同的建模结果,有效地解决了求解最佳投影向量的最优化问题。对评价指标数据采用极大化或极小化（不同的归一化）处理方式,得到的投影向量系数互为相反数,同一样本的投影值之间只相差一个常数,说明PPC建模技术既可用于探索性研究,也可用于验证性分析。PPC技术主要用于大样本情况,稳健性和可靠性均较好;指标之间存在明显的相关性,会影响建模结果的有效性和合理性。     

Design of a web-based application of the coupled multi-agent system model and environmental model for watershed management analysis using Hadoop

Watersheds are modeled as coupled human and natural systems (CHNSs) by coupling a multi-agent system (MAS) model and an environmental model. The computational intensity and model accessibility of the coupled models are addressed in this paper. Multithreaded programming is used to improve the computational efficiency. As a result, the total running time of the coupled models is reduced by 80%, from one hour for a sequential run to twelve minutes with an eight-core desktop machine, running the model in parallel. To make the coupled models publicly accessible, a web-based application of the coupled models is implemented in the Hadoop-based cloud computing environment, which allows users to access and execute the model simultaneously without an increase in latency. This study presents a case of cyberinfrastructure design for complex watershed management problems, especially to parallelize computational models and provide model accessibility with user scalability.     

Distributed control of multi-agent systems over unknown communication networks using extremum seeking

In this paper, the solution of large-scale real-time optimization problems of multi-agent systems (MAS) is tackled in a distributed and a cooperative manner without the requirement of exact knowledge of network connectivity. Each agent in the communication network measures a local disagreement cost in addition to its local cost. The agents must work collaboratively to ensure that the system's unknown overall cost (i.e., the sum of the local cost of all the agents) is minimized. In order to minimize this cost, the local disagreement cost of all the agents must first be minimized. This minimization requires the solution of a consensus estimation problem and ensures that the agents reach agreement on their decision variables. To address this challenging problem, a distributed proportional-integral extremum seeking control technique is proposed, one that solves both problems simultaneously. Three simulation examples are included, they demonstrate the effectiveness and robustness of the proposed technique.     

Pattern classification of non-sparse data using optimal interpolative nets

In this paper we introduce two pattern classifiers for non-sparse data (i.e. data with overlapping class distributions) which use the optimal interpolative neural network (OI-net), derived by one of the authors based on a generalized Fock (GF) space formulation. We present a statistical pattern classifier operating as a two-stage algorithm. The first stage consists of a pre-processing operation involving a k-N N editing of the original training set T. The operation results in a new training set, T_e, which in the second stage is classified by an OI-net constructed by the recursive least squares algorithm. We also propose a new data specific classifier which has an additional third computational stage, in which samples of the original training set are added to the network piece by piece until satisfactory classification results are obtained. During the computation process the training set is iteratively updated until the number of mis-classified samples is minimized. The performance of these two classifiers has been evaluated in some illustrative examples.