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.     

An ontology for commitments in multiagent systems:

Social commitments have long been recognized as an important concept for multiagent systems. We propose a rich formulation of social commitments that motivates an architecture for multiagent systems, which we dub spheres of commitment. We identify the key operations on commitments and multiagent systems. We distinguish between explicit and implicit commitments. Multiagent systems, viewed as spheres of commitment (SoComs), provide the context for the different operations on commitments. Armed with the above ideas, we can capture normative concepts such as obligations, taboos, conventions, and pledges as different kinds of commitments. In this manner, we synthesize ideas from multiagent systems, particularly the idea of social context, with ideas from ethics and legal reasoning, specifically that of directed obligations in the Hohfeldian tradition.     

Agents in Electronic Commerce: Component Technologies for Automated Negotiation and Coalition Formation

Automated negotiation and coalition formation among self-interested agents are playing an increasingly important role in electronic commerce. Such agents cannot be coordinated by externally imposing their strategies. Instead the interaction protocols have to be designed so that each agent is motivated to follow the strategy that the protocol designer wants it to follow. This paper reviews six component technologies that we have developed for making such interactions less manipulable and more efficient in terms of the computational processes and the outcomes: 1. OCSM-contracts in marginal cost based contracting, 2. leveled commitment contracts, 3. anytime coalition structure generation with worst case guarantees, 4. trading off computation cost against optimization quality within each coalition, 5. distributing search among insincere agents, and 6. unenforced contract execution. Each of these technologies represents a different way of battling self-interest and combinatorial complexity simultaneously. This is a key battle when multi-agent systems move into large-scale open settings.     

Real-Time Search for Autonomous Agents and Multiagent Systems

Since real-time search provides an attractive framework for resource-bounded problem solving, this paper extends the framework for autonomous agents and for a multiagent world. To adaptively control search processes, we propose -search which allows suboptimal solutions with error, and -search which balances the tradeoff between exploration and exploitation. We then consider search in uncertain situations, where the goal may change during the course of the search, and propose a moving target search (MTS) algorithm. We also investigate real-time bidirectional search (RTBS) algorithms, where two problem solvers cooperatively achieve a shared goal. Finally, we introduce a new problem solving paradigm, called organizational problem solving, for multiagent systems.     

A general framework for coherence in a CKBS

Coherence in a distributed system is meant to offset the disadvantages of distribution. The paper explores four issues under coherence, namely preservation of knowledge consistency across the agents, reliability of the overall system, integration of local solutions and the global performance. It presents some general strategies that can be employed to improve coherence in a CKBS, which include a weak consistency with versions for knowledge revision, and a recovery mechanism based on a hierarchic three-stage coordination, which ensures the correct isolation of potentially hierarchic multiagent actions. The paper goes on to identify the sources and classes of conflicts in global integration, and it suggests remedies, which at worst case would involve negotiation. In global performance, it focusses on planning and result synthesis, as the two most important problem domains, and suggests strategies ameliorate performance.     

Level-of-detail for cognitive real-time characters

We present a solution for the real-time simulation of artificial environments containing cognitive and hierarchically organized agents at constant rendering framerates. We introduce a level-of-detail concept to behavioral modeling, where agents populating the world can be both reactive and proactive. The disposable time per rendered frame for behavioral simulation is variable and determines the complexity of the presented behavior. A special scheduling algorithm distributes this time to the agents depending on their level-of-detail such that visible and nearby agents get more time than invisible or distant agents. This allows for smooth transitions between reactive and proactive behavior. The time available per agent influences the proactive behavior, which becomes more sophisticated because it can spend time anticipating future situations. Additionally, we exploit the use of hierarchies within groups of agents that allow for different levels of control. We show that our approach is well-suited for simulating environments with up to several hundred agents with reasonable response times and the behavior adapts to the current viewpoint.     

Learning to Improve Coordinated Actions in Cooperative Distributed Problem-Solving Environments

Coordination is an essential technique in cooperative, distributed multiagent systems. However, sophisticated coordination strategies are not always cost-effective in all problem-solving situations. This paper presents a learning method to identify what information will improve coordination in specific problem-solving situations. Learning is accomplished by recording and analyzing traces of inferences after problem solving. The analysis identifies situations where inappropriate coordination strategies caused redundant activities, or the lack of timely execution of important activities, thus degrading system performance. To remedy this problem, situation-specific control rules are created which acquire additional nonlocal information about activities in the agent networks and then select another plan or another scheduling strategy. Examples from a real distributed problem-solving application involving diagnosis of a local area network are described.     

DART: A DISTRIBUTED ANALYSIS OF REPUTATION AND TRUST FRAMEWORK

Artificial societies—distributed systems of autonomous agents—are becoming increasingly important in open distributed environments, especially in e‐commerce. Agents require trust and reputation concepts to identify communities of agents with which to interact reliably. We have noted in real environments that adversaries tend to focus on exploitation of the trust and reputation model. These vulnerabilities reinforce the need for new evaluation criteria for trust and reputation models called exploitation resistance which reflects the ability of a trust model to be unaffected by agents who try to manipulate the trust model. To examine whether a given trust and reputation model is exploitation‐resistant, the researchers require a flexible, easy‐to‐use, and general framework. This framework should provide the facility to specify heterogeneous agents with different trust models and behaviors. This paper introduces a Distributed Analysis of Reputation and Trust (DART) framework. The environment of DART is decentralized and game‐theoretic. Not only is the proposed environment model compatible with the characteristics of open distributed systems, but it also allows agents to have different types of interactions in this environment model. Besides direct, witness, and introduction interactions, agents in our environment model can have a type of interaction called a reporting interaction, which represents a decentralized reporting mechanism in distributed environments. The proposed environment model provides various metrics at both micro and macro levels for analyzing the implemented trust and reputation models. Using DART, researchers have empirically demonstrated the vulnerability of well‐known trust models against both individual and group attacks.     

Agent之间重复交互策略研究

在Agent构成的社会中信息可以共享,由于单一的交互对共享信息是没有作用的,因此,提出重复的交互策略。重复交互可以使Agent彼此共享信息,通过惩罚来达到系统的平衡,惩罚是通过忽视被惩罚的Agent的询问来实现的。通过简单地介绍一个周期模型,并且定义了延迟的折扣因子和预期效用,从而考虑了更一般的情况,即建立了n个周期模型,描述了通过观察询问的历史记录中没有应答的情况,来决定是否执行惩罚,并且通过试验进行了验证。随着n的增加,Agent的预期效用增加,而平衡的存在越来越少。     

Learning Sequences of Compatible Actions Among Agents

Action coordination in multiagent systemsis a difficult task especially in dynamicenvironments. If the environment possessescooperation, least communication,incompatibility and local informationconstraints, the task becomes even moredifficult. Learning compatible action sequencesto achieve a designated goal under theseconstraints is studied in this work. Two newmultiagent learning algorithms called QACE andNoCommQACE are developed. To improve theperformance of the QACE and NoCommQACEalgorithms four heuristics, stateiteration, means-ends analysis, decreasing reward and do-nothing, aredeveloped. The proposed algorithms are testedon the blocks world domain and the performanceresults are reported.     

Sliding mode coordination control for multiagent systems with underactuated agent dynamics

In this paper, we develop a new integrated coordinated control and obstacle avoidance approach for a general class of underactuated agents. We use graph-theoretic notions to characterise communication topology in the network of underactuated agents as determined by the information flow directions and captured by the graph Laplacian matrix. Obstacle avoidance is achieved by surrounding the stationary as well as moving obstacles by elliptical or other convex shapes that serve as stable periodic solutions to planar systems of ordinary differential equations and using transient trajectories of those systems to navigate the agents around the obstacles. Decentralised controllers for individual agents are designed using sliding mode control approach and are only based on data communicated from the neighbouring agents. We demonstrate the efficacy of our theoretical approach using an example of a system of wheeled mobile robots that reach and maintain a desired formation. Finally, we validate our results experimentally.     

卫星系统综合效能分析研究

从卫星系统应用角度提出了基于信息支援保障的卫星系统综合效能评估指标体系,并应用层次分析法建立了效能分析的多属性综合评价模型与方法。为量化指标值,提出了基于领域专家知识的定性与定量相结合的量化指标方法。定量分析表明了多属性综合评价方法对复杂系统效能评估是有效的。     

GEAMAS: A Generic Architecture for Agent-Oriented Simulations of Complex Processes

This paper's object is to present the results of the GEAMAS project which aims at modeling and simulating natural complex systems. GEAMAS is a generic architecture of agents used to study the behavior emergence in such systems. It is a multiagent program meant to develop simulation applications. Modeling complex systems requires to reduce, to organize the system complexity and to describe suitable components. Complexity of the system can then be tackled with an agent-oriented approach, where interactions lead to a global behavior. This approach helps in understanding how non-determinist behavior can emerge from interactions between agents, which is near of self-organized criticality used to explain natural phenomena. In the Applied Artificial Intelligence context, this paper presents an agent software architecture using a model of agent. This architecture is composed of three abstract levels over which the complexity is distributed and reduced. The architecture is implemented in ReActalk, an open agent-oriented development tool, which was developed on top of Smalltalk-80. To illustrate our purpose and to validate the architecture, a simulation program to help in predicting volcanic eruptions was investigated. This program was run over a period of one year and has given many satisfying results unattainable up to there with more classical approaches.     

Learning Communication Strategies in Multiagent Systems

In this paper we describe a dynamic, adaptive communication strategy for multiagent systems. We discuss the behavioral parameters of each agent that need to be computed, and provide a quantitative solution to the problem of controlling these parameters. We also describe the testbed we built and the experiments we performed to evaluate the effectiveness of our methodology. Several experiments using varying populations and varying organizations of agents were performed and are reported. A number of performance measurements were collected as each experiment was performed so the effectiveness of the adaptive communications strategy could be measured quantitatively.The adaptive communications strategy proved effective for fully connected networks of agents. The performance of these experiments improved for larger populations of agents and even approached optimal performance levels. Experiments with non-fully connected networks showed that the adaptive communications strategy is extremely effective, but does not approach optimality. Other experiments investigated the ability of the adaptive communications strategy to compensate for distracting agents, for systems where agents are required to assume the role of information routers, and for systems that must decide between routing paths based on cost information.