~(99)Tc~m-MVNE-膦混配配合物的制备及其在小鼠体内分布研究

为探索锝膦混配配合物的制备方法,寻找新的心肌显像剂,合成了一个新的N3S配体(MVNE)和3个膦配体。通过交换反应在室温下制备了放化纯>95%的99Tcm-MVNE, 然后分别与3种膦配体混配得到放化纯>90%的99Tcm-MVNE-膦混配配合物。小鼠体内的生物分布研究表明,该类混配配合物有较高的的心肌摄取,血液清除也较快。     

Congregation Formation in Multiagent Systems

We present congregating both as a metaphor for describing and modeling multiagent systems (MAS) and as a means for reducing coordination costs in large-scale MAS. When agents must search for other agents to interact with, congregations provide a way for agents to bias this search towards groups of agents that have tended to produce successful interactions in the past. This causes each agent's search problem to scale with the size of a congregation rather than the size of the population as a whole. In this paper, we present a formal model of a congregation and then apply Vidal and Durfee's CLRI framework [24] to the congregating problem. We apply congregating to the affinity group domain, and show that if agents are unable to describe congregations to each other, the problem of forming optimal congregations grows exponentially with the number of agents. The introduction of labelers provides a means of coordinating agent decisions, thereby reducing the problem's complexity. We then show how a structured label space can be exploited to simplify the labeler's decision problem and make the congregating problem linear in the number of labels. We then present experimental evidence demonstrating how congregating can be used to reduce agents' search costs, thereby allowing the system to scale up. We conclude with a comparison to other methods for coordinating multiagent behavior, particularly teams and coalitions.     

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.     

Cooperation and Competition in ALIAS: A Logic Framework for Agents that Negotiate

This paper presents ALIAS, an agent architecture based on intelligent logic agents, where the main form of agent reasoning is abduction. The system is particularly suited for solving problems where knowledge is incomplete, where agents may need to make reasonable hypotheses about the problem domain and other agents, and where the raised hypotheses have to be consistent for the overall set of agents. ALIAS agents are pro-active, exhibiting a goal-directed behavior, and autonomous, since each one can solve problems using its own private knowledge base. ALIAS agents are also social, because they are able to interact with other agents, in order to cooperatively solve problems. The coordination mechanisms are modeled by means of LAILA, a logic-based language which allows to express intra-agent reasoning and inter-agent coordination. As an application, we show how LAILA can be used to implement inter-agent dialogues, e.g., for negotiation. In particular, LAILA is well-suited to coordinate the process of negotiation aimed at exchanging resources between agents, thus allowing them to execute the plans to achieve their goals.     

Shop-floor scheduling at shipbuilding yards using the multiple intelligent agent system

The intelligent agent system has become a typical approach to research distributed artificial intelligence and distributed problem solving. However, despite its various technical advantages, the distribution and expansion of the intelligent agent system approach has been limited because existing methodologies rely on specialized applications and therefore require intensive investment to develop new systems. In an attempt to conquer these difficulties, a standardized methodology to construct intelligent agent systems is proposed. This approach deviates from current agent system approaches of repeatedly constructing new and customized expert systems. Specifically, it provides an economical method for developing intelligent agent systems by investigating the possibility of standardizing message communication protocols in linguistics speech-act theory and by supplementing traditional algorithmic systems with intelligent segments using, among others, expert system tools. To verify effectiveness, the shop-floor scheduling system of a large-scale shipbuilding yard has been redesigned, developed, and tested using this approach. This shop-floor scheduling system requires the scheduling of when and where to process block construction under various constraints. It is a difficult four-dimensional time and space allocation problem involving traditional NP-complete search spaces. The tested intelligent agent system proposes an innovative method for reducing the search space into three levels: the algorithm level, the agent intelligence level, and the level of cooperation among agents. It also provides methods for solving deadlock occurrence and non-uniformity problems resulting from parallel processing. Test results demonstrate applicability and economy, among other technical advantages.     

Knowledge-directed Adaptation in Multi-level Agents

Most work on adaptive agents have a simple, single layerarchitecture. However, most agent architectures support three levels ofknowledge and control: a reflex level for reactive responses, a deliberatelevel for goal-driven behavior, and a reflective layer for deliberateplanning and problem decomposition. In this paper we explore agentsimplemented in Soar that behave and learn at the deliberate and reflectivelevels. These levels enhance not only behavior, but also adaptation. Theagents use a combination of analytic and empirical learning, drawing from avariety of sources of knowledge to adapt to their environment. We hypothesize that complete, adaptive agents must be able to learn across all three levels.     

Interleaving Planning and Robot Execution for Asynchronous User Requests

ROGUE is an architecture built on a real robot which provides algorithms for the integration of high-level planning, low-level robotic execution, and learning. ROGUE addresses successfully several of the challenges of a dynamic office gopher environment. This article presents the techniques for the integration of planning and execution.ROGUE uses and extends a classical planning algorithm to create plans for multiple interacting goals introduced by asynchronous user requests. ROGUE translates the planner';s actions to robot execution actions and monitors real world execution. ROGUE is currently implemented using the PRODIGY4.0 planner and the Xavier robot. This article describes how plans are created for multiple asynchronous goals, and how task priority and compatibility information are used to achieve appropriate efficient execution. We describe how ROGUE communicates with the planner and the robot to interleave planning with execution so that the planner can replan for failed actions, identify the actual outcome of an action with multiple possible outcomes, and take opportunities from changes in the environment.ROGUE represents a successful integration of a classical artificial intelligence planner with a real mobile robot.     

Adaptive personal agents

Personal agents are an important advance in the management of electronic information. Accurate user profiling is critical to the personalisation of agent-based services. In this paper we describe a framework for personal agents, which provides a range of services using a common user profile. The user profile is automatically learned from observation of the electronic documents a user reads, and tracks user interests over time. We report on a large trial of this framework, which has given us important insights into the effectiveness of personal agent applications. In particular we discuss the benefits obtained from the use of a common profile and the interoperation of several personal agent applications.     

基于移动代理的信息收集方法研究

传统的用软盘收集信息的方法存在一些不足之处，本文介绍了移动代理技术，给出了用移动代理技术来实现信息收集的方法，对该方法进行了安全性分析，并说明了该方法的优点。     

The Value of Temptation

There is an implicit assumption in electronic commerce that induces the buyers to believe that their deals will be handled appropriately. However, after a seller has already committed to a buyer, he may be tempted by several requests though he will not be able to supply them all. We analyze markets in which a finite set of automated buyers interacts repeatedly with a finite set of automated sellers. These sellers can satisfy one buyer at a time, and they can be tempted to break a commitment they already have. We have found the perfect equilibria that exist in markets with a finite horizon, and with an unrestricted horizon. A significant result stemming from our study reveals that sellers are almost always tempted to breach their commitments. However, we also show that if markets' designers implement an external mechanism that restricts the automated buyers actions, then sellers will keep their commitments.