Mobile Agents in Interaction Spaces

Mobile agent systems are difficult to reason about and implement efficiently and safely. Theoretical work, most notably process calculi, provide solid semantics for mobile systems. However, the theory is often too abstract to match with the requirements of practical implementations. To fill this gap, intermediate models must be proposed. We present in this paper such a model named Interaction Spaces, a metaphor of geometrical spaces in which agents interact through simple transformations. The framework captures high-level distributed semantics, most notably asynchronous, multicast communications on FIFO channels. It also refines and implements the channel passing feature of the pi-calculus, together with the mobility of agent themselves. Above interaction spaces, we propose a full-fledged agent calculus and its associated operational semantics.     

Markovian Search Games in Heterogeneous Spaces

In this paper, we consider how to search for a mobile evader in a large heterogeneous region when sensors are used for detection. Sensors are modeled using probability of detection. Due to environmental effects, this probability will not be constant over the entire region. We map this problem to a graph-search problem, and even though deterministic graph search is NP-complete, we derive a tractable optimal probabilistic search strategy. We do this by defining the problem as a dynamic game played on a Markov chain. We prove that this strategy is optimal in the sense of Nash. Simulations of an example problem illustrate our approach and verify our claims.     

Improving Performance of Heterogeneous Agents

Agents provide services not only to humans users but also to agents in one or more multiagent systems. When agents are confronted with multiple tasks to perform (or requests to satisfy), the agent can reduce load on itself by attempting to take advantage of commonalities between the tasks that need to be performed. In this paper, we develop a logical theory by which such “heavily loaded” agents can merge commonalities amongst such tasks. In our framework, agents can be built on top of legacy codebases. We propose a logical formalism called invariants using which agent developers may specify known commonalities between tasks – after this, we propose a sound and complete mechanism to derive all possible derived commonalities. An obvious A ^*-based algorithm may be used to merge a set of tasks in a way that minimised expected execution cost. Unfortunately the execution time of this algorithm is prohibitive, even when only 10 tasks need to be merged, thus making it unusable in practice. We develop heuristic algorithms for this problem that take much less time to execute and produce almost as good ways of merging tasks.     

异构信息空间中时间感知的实体集成框架

异构信息空间中的实体和关联关系普遍具有时间信息、多种时间版本的实体数据共存,而传统的实体集成忽略了时间信息,不支持时间维度上的集成。提出一种异构信息空间中时间感知的实体集成框架T-EI,从大量异构实体数据中聚集事实形成干净的、完整的、具有时间信息的实体概貌,进而支持时间感知的实体搜索。T-EI利用实体及关联关系所具有的时间信息提出时间感知的实体识别算法,并通过考虑数据时效性提出时间感知的数据融合算法。在真实数据集上的实验结果表明了T-EI的可行性和有效性。     

异构信息空间中时间感知的跨类型实体推荐

随着实体搜索成为信息检索的一种新趋势,实体推荐也成为业界和学术界的热门研究问题之一。异构信息空间中的异构实体间彼此相互关联,因此跨类型实体推荐至关重要。此外,异构实体具有时间信息,异构信息空间中的实体不断随时间演化,用户希望得到在时间上最相关的实体推荐。提出一个时间感知的跨类型实体推荐框架T-ERe,利用异构实体间丰富的关联关系和查询日志实现跨类型的实体推荐。T-ERe考虑实体的时间信息和查询的时间上下文, 给用户推荐时间上最相关的多种类型的实体。在真实数据集上的实验结果表明了T-ERe的可行性和有效性。     

A Simulation Framework for Heterogeneous Agents

We introduce a generic simulation framework suitable for agent-based simulations featuring the support of heterogeneous agents, hierarchical scheduling, and flexible specification of design parameters. One key aspect of this framework is the design specification: we use a format based on the Extendible Markup Language (XML) that is simple-structured yet still enables the design of flexible models. Another issue in agent-based simulations, especially when ready-made components are used, is the heterogeneity arising from both the agents' implementations and the underlying platforms. To tackle such obstacles, we introduce a wrapper technique for mapping the functionality of agents living in an interpreter-based environment to a standardized JAVA interface, thus facilitating the task for any control mechanism (like a simulation manager) because it has to handle only one set of commands for all agents involved. Again, this mapping is made by an XML-based definition format. We demonstrate the technique by applying it to a simple sample simulation of two mass marketing firms operating in an artificial consumer environment.     

Asset Price Dynamics among Heterogeneous Interacting Agents

In this paper, we investigate the presence of rationalherding on asset price dynamics during the intra-day trading withheterogeneous interacting agents, whose information set is notcomplete. In the model, individual probability measures offinancial investment strategies are defined using statisticalmechanics concepts. In addition, there is a learning processtoward the best strategy, implemented as a geneticalgorithm. Simulations show that imitative behavior can be arational strategy, since it allows an investor to gain excessreturns on an asset by exploiting information regarding pricedynamics not strictly contained in the fundamental solution. Herdbehavior is rational in the sense that it produces profits at theexpense of increasing the complexity of the system.     

Interactive Navigation of Heterogeneous Agents Using Adaptive Roadmaps

We present a novel algorithm for collision-free navigation of a large number of independent agents in complex and dynamic environments. We introduce adaptive roadmaps to perform global path planning for each agent simultaneously. Our algorithm takes into account dynamic obstacles and interagents interaction forces to continuously update the roadmap based on a physically-based dynamics simulator. In order to efficiently update the links, we perform adaptive particle-based sampling along the links. We also introduce the notion of 'link bands' to resolve collisions among multiple agents. In practice, our algorithm can perform real-time navigation of hundreds and thousands of human agents in indoor and outdoor scenes.     

Network Formation with Heterogeneous Agents and Absolute Friction

We present a model of strategic network formation with absolute friction and heterogeneous agents. The individual payoffs from a given network are determined by the difference of an agent specific utility function that depends on the number of her direct links and the sum of her link-costs. These link-costs decompose in a symmetric function that represents the social distance between the two agents and an agent specific function representing the partner’s quality. From a theoretical point of view, we analyze strong pairwise stable networks (Belleflamme and Bloch, Int Econ Rev 45(2):387–411, 2004) and show that our model always has a unique strong pairwise stable network which is also strongly stable (Dutta and Mutuswami, J Econ Theory, 76:322–344, 1997). From a practical point of view, we provide an algorithm that reproduces this stable network from information on the individual payoff structure. We illustrate the use of this algorithm by applying it to an informal risk sharing network data set from the village of Nyakatoke in rural Tanzania.     

Consensus for Agents with Double Integrator Dynamics in Heterogeneous Networks

This paper studies the convergence properties of consensus algorithms for agents with double integrator dynamics communicating over networks modelled by undirected graphs. The positions and velocities of the agents are shared along heterogeneous, i.e. different, undirected communication networks. The main result is that consensus can be achieved, even though the networks along which position and velocity information are shared are different, and not even connected. Insights on the consensus rate are given based only on the topological properties of the network.     

Larks: Dynamic Matchmaking Among Heterogeneous Software Agents in Cyberspace

Service matchmaking among heterogeneous software agents in the Internet is usually done dynamically and must be efficient. There is an obvious trade-off between the quality and efficiency of matchmaking on the Internet. We define a language called Larks for agent advertisements and requests, and present a flexible and efficient matchmaking process that uses Larks. The Larks matchmaking process performs both syntactic and semantic matching, and in addition allows the specification of concepts (local ontologies) via ITL, a concept language. The matching process uses five different filters: context matching, profile comparison, similarity matching, signature matching and constraint matching. Different degrees of partial matching can result from utilizing different combinations of these filters. We briefly report on our implementation of Larks and the matchmaking process in Java. Fielded applications of matchmaking using Larks in several application domains for systems of information agents are ongoing efforts.     

异构信息空间中实体关联关系挖掘算法CFRQ4A

丰富的实体关联关系是在异构信息空间中进行数据分析、数据挖掘、知识发现和语义查询等许多应用的前提条件和关键所在.然而不同于同构信息网络,由于异构信息空间中实体关联关系的复杂性、多样性和异构性使得实体关联关系挖掘并不是一件简单的任务,更具有挑战性.以作者文献网络为例,提出了一个通用的,由聚类、过滤、推理和量化4步骤组成的异构信息空间中基于聚类的实体关联关系挖掘算法CFRQ4A(clustering,filtering,reasoning and qualifying for associations).CFRQ4A算法不仅利用了异构实体自身的属性值,还利用了异构信息网络的结构(路径)信息;在挖掘过程中引入关联关系约束来保证关联关系的语义和逻辑正确性,并且针对实体关联关系的特点提出了关联强度量化模型.在真实数据集DBLP上的实验结果表明所提出算法是可行和有效的.     

异构信息空间中时间感知的查询时实体识别与数据融合

已有的传统的实体识别技术大多是以线下、非实时的方式,在静态数据集上进行,对于大数据集的执行通常需要大量的时间和系统资源。对于异构信息空间中具有时间信息、不断演化的异构实体来说,时间感知的查询时实体识别与数据融合越来越成为一种保证数据质量和满足用户需求的发展趋势。针对异构信息空间中使用时间上下文的关键字查询进行的实体搜索,提出一种时间感知的查询时实体识别与数据融合方法TQ-ER,以给用户提供准确的实体概貌(entity profile);提出一种迭代式时间感知的实体候选集生成算法。TQ-ER充分利用查询的时间上下文和实体的时间信息给正确的回答一个给定查询所需要的、最少的实体数据,以进行识别与数据融合。在真实数据集上的大量实验结果表明了TQ-ER的有效性和正确性。     

一种基于移动Agent的异构网络管理模型

分析了集中式网络管理方法的局限性,介绍了移动Agent的特点及与传统方法相比的优势,提出了一种针对异构网络的基于移动Agent的网管模型,详细地介绍了该模型的设计思想、结构、管理方式及安全性、容错性、独立性等特点。设计实现了两种移动Agent:故障诊断代理和故障搜索代理。使用它们评估和验证了移动A gent管理实际的异构网络的能力,它们能够在一个特定大小的网络中准确定位故障并分析故障的原因。     

Complementarity Modeling of a Ramsey-Type Equilibrium Problem with Heterogeneous Agents

We contribute to the field of Ramsey-type equilibrium models with heterogeneous agents. To this end, we state such a model in a time-continuous and time-discrete form, which in the latter case leads to a finite-dimensional mixed complementarity problem. We prove the existence of solutions of the latter problem using the theory of variational inequalities and present further properties of its solutions. Finally, we compute the growth dynamics in a calibrated model in which households differ with respect to their relative risk aversion, their discount factors, their initial wealth, and with respect to their interest rates on savings.

     

An Automated Teamwork Infrastructure for Heterogeneous Software Agents and Humans

Agent integration architectures enable a heterogeneous, distributed set of agents to work together to address problems of greater complexity than those addressed by the individual agents themselves. Unfortunately, integrating software agents and humans to perform real-world tasks in a large-scale system remains difficult, especially due to three main challenges: ensuring robust execution in the face of a dynamic environment, providing abstract task specifications without all the low-level coordination details, and finding appropriate agents for inclusion in the overall system. To address these challenges, our Teamcore project provides the integration architecture with general-purpose teamwork coordination capabilities. We make each agent team-ready by providing it with a proxy capable of general teamwork reasoning. Thus, a key novelty and strength of our framework is that powerful teamwork capabilities are built into its foundations by providing the proxies themselves with a teamwork model.Given this teamwork model, the Teamcore proxies addresses the first agent integration challenge, robust execution, by automatically generating the required coordination actions for the agents they represent. We can also exploit the proxies' reusable general teamwork knowledge to address the second agent integration challenge. Through team-oriented programming, a developer specifies a hierarchical organization and its goals and plans, abstracting away from coordination details. Finally, KARMA, our Knowledgeable Agent Resources Manager Assistant, can aid the developer in conquering the third agent integration challenge by locating agents that match the specified organization's requirements. Our integration architecture enables teamwork among agents with no coordination capabilities, and it establishes and automates consistent teamwork among agents with some coordination capabilities. Thus, team-oriented programming provides a level of abstraction that can be used on top of previous approaches to agent-oriented programming. We illustrate how the Teamcore architecture successfully addressed the challenges of agent integration in two application domains: simulated rehearsal of a military evacuation mission and facilitation of human collaboration.     

Situated Interaction in Art

This paper describes metaphors and design strategies used to conceive and develop a hand-held, location-aware tourist guide that delivers information related to the surrounding space mainly by reacting to the physical movements of the visitors. The guide is designed to minimise the boundary between the physical space and the related information through a number of situated and contextual-aware interaction mechanisms. These mechanisms are conceived to support the activity both at individual and social level.     

QoS-Aware Tethering in a Heterogeneous Wireless Network using LTE and TV White Spaces

Wireless networks have resource limitations; in a dense area, cellular spectrum resources are insufficient and affect the system performance. A Long Term Evolution (LTE) network aims to serve heterogeneous users with different QoS requirements. Traditional approaches need new infrastructure and degrade performance of delay sensitive applications, which may result in users with minimum rate requirements being in high blocking probability. To utilize wireless resources efficiently, users want to access the same medium to connect with the same multicast group and be overhauled at the same time. In this paper, a new technique is proposed for operator-controlled called the QoS-Aware Tethering in a Heterogeneous Wireless Network using LTE and TV White Spaces (QTHN) to improve QoS for Constant Bit Rate (CBR) and Best Effort (BE) users. The proposed QTHN converts the whole dense wireless network into hexagonal clusters via two layer network communication. In a cluster, one node is selected as a cluster head and all other nodes act as slaves. Within a cluster, a cluster head acts as an access point known as a Hotspot (H), which is further connected to the Base-station (BS). The proposed QTHN aims to improve QoS within heterogeneous wireless network using LTE and unused White Spaces in a wireless dense area. Simulation results show that the proposed QTHN reduced the numbers of blocked users and improved network utility.