ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada / This paper describes a service-based Intelligent Agent (IA) approach for machine learning and data mining of distributed heterogeneous data streams. We focus on an open architecture framework that enables the programmer/analyst to build an IA suite for mining, examining and evaluating heterogeneous data for semantic representations, while iteratively building the probabilistic model in real-time to improve predictability. The Framework facilitates model development and evaluation while delivering the capability to tune machine learning algorithms and models to deliver increasingly favorable scores prior to production deployment. The IA Framework focuses on open standard interoperability, simplifying integration into existing environments.

Engineering Information and Communication Technology (ICT) for robust information sharing is the fundamental area of investigation in thesis. Robust workflow based information sharing systems have the potential to be part of robust information infrastructures providing positive effects for the individuals and teams as well as opportunities for societal and economical gains. Challenges in design and implementation of open socio-technical systems include identifying engineering principles empowering individual and team using the systems as well as supporting flexibility in design and maintenance. Of specific importance are principles supporting semantically correct information sharing. Information sharing in open socio-technical systems is given affordances due to coordination and exchange of services. Approaches ensuring robust semantically correct information sharing and user empowerments are key requirements especially since changes in context, roles and intentions are the rule and not the exception in socio-technical systems. Empirical observations of behaviours have been important for identifying critical patterns in workflow. A configuration of models and methods under the umbrella Participatory Design has been used including Ethnography and approaches based on Situation Theory, Knowledge Engineering, Interaction Design and Computer Supported Cooperative Work. The results of the configurations of methodologies are context sensitive since the methodologies are domain dependant. Three cases illustrating engineering support for empowerment of individuals and teams in open sociotechnical systems are presented. Two cases are based on studies performed in Sölvesborg and concerns engineering principles towards empowering individuals with cognitive impairments via ambient assistance. In the third case the focus is on hand-over situations and ontologies/abbreviations assuring semantically correct information sharing in distributed handling of critical emergency calls in Swedish Emergency Service Centres (SOS centres). The main contributions in this thesis, methodological contributions included, are engineering principles for open socio-technical systems from an empowerments perspective. The principles support understanding of workflows, information flows, interaction models, data models, semantics of information, trust, resilience, validation and training as well as assurance mechanisms in hand-over of critical operations. Identification and validation of key service qualities including mechanisms for improving performance critical tasks of semantics in information sharing are contributions. Service, Agent based and sensor approaches presented are final contributions.

This research investigates approaches to improve the performance of multi-agent based simulation (MABS) applications executed in distributed computing environments. MABS is a type of micro-level simulation used to study dynamic systems consisting of interacting entities, and in some cases, the number of the simulated entities can be very large. Most of the existing publicly available MABS tools are single-threaded desktop applications that are not suited for distributed execution. For this reason, general-purpose multi-agent platforms with multi-threading support are sometimes used for deploying MABS on distributed resources. However, these platforms do not scale well for large simulations due to huge communication overheads. In this research, different strategies to deploy large scale MABS in distributed environments are explored, e.g., tuning existing multi-agent platforms, porting single-threaded MABS tools to distributed environment, and implementing a service oriented architecture (SOA) deployment model. Although the factors affecting the performance of distributed applications are well known, the relative significance of the factors is dependent on the architecture of the application and the behaviour of the execution environment. We developed mathematical performance models to understand the influence of these factors and, to analyze the execution characteristics of MABS. These performance models are then used to formulate algorithms for resource management and application tuning decisions. The most important performance improvement solutions achieved in this thesis include: predictive estimation of optimal resource requirements, heuristics for generation of agent reallocation to reduce communication overhead and, an optimistic synchronization algorithm to minimize time management overhead. Additional application tuning techniques such as agent directory caching and message aggregations for fine-grained simulations are also proposed. These solutions were experimentally validated in different types of distributed computing environments. Another contribution of this research is that all improvement measures proposed in this work are implemented on the application level. It is often the case that the improvement measures should not affect the configuration of the computing and communication resources on which the application runs. Such application level optimizations are useful for application developers and users who have limited access to remote resources and lack authorization to carry out resource level optimizations.

Multi-agent systems are applied to distributed problem-solving applications because of their ability to overcome the limitations that individual agents face when solving complex problems. Large numbers of agents acting as problem-solvers on networks suggest a virtual marketplace. In this marketplace, groups of self-interested agents can interact to solve highly constrained and distributed problems by assuming varying roles and forming “temporary teams”. This dissertation presents a decision making mechanism for multi-agent team formation between self-interested agents in a competitive, open and dynamic environment. An agent perceives environmental uncertainties, and models those uncertainties into simplified categories such as risks and benefits. The dissertation further demonstrates how an agent’s attitudes shape how risk and rewards are weighted when making decisions among multiple alternatives. Accordingly, agent-borne attitudes toward proactive behavior, risk, reward, and urgency are proposed as the basis of the proposed team formation mechanism. Finally, a learning technique assists an agent in continuously learning what attitudes it needs in order to adapt to dynamic environments and increase its resulting rewards. / text

The research described in this thesis stems from ROBOCARE1, a three year research project aimed at developing software and robotic technology for providing intelligent support for elderly people. This thesis deals with two problems which have emerged in the course of the project’s development: Multi-agent coordination with scarce resources. Multi-agent planning is concerned with automatically devising plans or strategies for the coordinated enactment of concurrently executing agents. A common realistic constraint in applications which require the coordination of multiple agents is the scarcity of resources for execution. In these cases, concurrency is affected by limited capacity resources, the presence of which modifies the structure of the planning/coordination problem. Specifically, the first part of this thesis tackles this problem in two contexts, namely when planning is carried out centrally (planning from first principles), and in the context of distributed multi-agent coordination. Domain modeling for scheduling applications. It is often the case that the products of research in AI problem solving are employed to develop applications for supporting human decision processes. Our experience in ROBOCARE as well as other domains has often called for the customization of prototypical software for real applications. Yet the gap between what is often a research prototype and a complete decision support system is seldom easy to bridge.The second part of the thesis focuses on this issue from the point of view of scheduling software deployment.Overall, this thesis presents three contributions within the two problems mentioned above. First, we address the issue of planning in concurrent domains in which the complexity of coordination is dominated by resource constraints. To this end, an integrated planning and scheduling architecture is presented and employed to explore the structural trademarks of multi-agent coordination problems in function of their resource-related characteristics. Theoretical and experimental analyses are carried out revealing which planning strategies are most fit for achieving plans which prescribe efficient coordination subject to scarce resources.We then turn our attention to distributed multi-agent coordination techniques (specifically, a distributed constraint optimization (DCOP) reduction of the coordination problem). Again, we consider the issue of achieving coordinated action in the presence of limited resources. Specifically, resource constraints impose n-ary relations among tasks. In addition, as the number of n-ary relations due to resource contention are exponential in the size of the problem, they cannot be extensionally represented in the DCOP representation of the coordination problem. Thus, we propose an algorithm for DCOP which retains the capability to dynamically post n-ary constraints during problem resolution in order to guarantee resource-feasible solutions. Although the approach is motivated by the multi-agent coordination problem, the algorithm is employed to realize a general architecture for n-ary constraint reasoning and posting.Third, we focus on a somewhat separate issue stemming from ROBOCARE, namely a software engineering methodology for facilitating the process of customizing scheduling components in real-world applications. This work is motivated by the strong applicative requirements of ROBOCARE. We propose a software engineering methodology specific to scheduling technology development. Our experience in ROBOCARE as well as other application scenarios has fostered the development of a modeling framework which subsumes the process of component customization for scheduling applications. The framework aims to minimize the effort involved in deploying automated reasoning technology in practise, and is grounded on the use of a modeling language for defining how domain-level concepts are grounded into elements of a technology-specific scheduling ontology.

代理者（Agent）是一個自主性的軟體程式，可以幫助代表人類在網際網路上從事各種的電子化服務（E-Service）。由於目前多代理者系統缺少了安全管理的機制，以致於目前為止代理者代表人類在網上從事活動的行為還不被大家接受。因此，我們提出了一套以代理者為導向的公鑰匙架構（Agent-Oriented Public Key Infrastructure, APKI），各式各樣的數位憑證被產生、儲存、註銷及驗證，以滿足不同存取控制的需求。例如，代理者的認證是以代理者身份憑證為基礎，而授權的部分則以授權憑證或屬性憑證來做驗證。透過這些數位憑證，我們可以在虛擬網路上的代理者之間建立一條信任路徑，一個安全的電子化服務的實際應用範例將會以此架構實作及呈現出來，以驗證我們所提架構的可行性。 / Agent is autonomous software that mediates e-service for human on the Internet. The acceptance of agent-mediated e-service (AMES) is very slow for the lacking of security management infrastructure for multi-agent system. Therefore we proposed an agent-oriented public key infrastructure (APKI) for multi-agent e-service. In this APKI, a taxonomy of digital certificates are generated, stored, verified, and revoked to satisfy different access and delegation control purposes. Agent identity certificate was designed for agent’s authentication whereas attributed and agent authorization certificates were proposed for agent’s authorization and delegation. Using these digital certificates, we establish agent trust relationships on the cyberspace. A trusted agent-mediated e-service scenario will be shown to demonstrate the feasibility of our APKI.

Multi-agent Systems (MAS) provide an increasingly relevant field of research due to their many applications to modelling real world situations where the behaviour of many individual, self-motivated, agents must be reasoned about and controlled. The problem of agent social reasoning is central to MAS, where an agent reasons about its actions and interactions with other agents. This is the most important component of MAS, as it is the interactions, cooperation and competition between agents that make MAS a powerful approach suited for tackling many complex problems. Existing work focuses either on specific types of social reasoning or general purpose agent practical reasoning - reasoning directed toward actions. This thesis argues that social reasoning should be considered separately from practical reasoning. There are many possible benefits to this separation compared to existing approaches. Principally, it can allow general algorithms for agent implementation, analysis and bounded reasoning. This viewpoint is motivated by the desire to implement social reasoning agents and allow for a more general theory of social reasoning in agents. This thesis presents the novel Expectation- Strategy-Behaviour (ESB) framework for social reasoning, which provides a generic way to specify and execute agent reasoning approaches. ESB is a powerful tool, allowing an agent designer to write expressive social reasoning specifications and have a computational model generated automatically. Through a formalism and description of an implemented reasoner based on this theory it is shown that it is possible and beneficial to implement a social reasoning engine as a complementary component to practical reasoning. By using ESB to specify, and then implement, existing social reasoning schemes for joint commitment and normative reasoning, the framework is shown to be a suitable general reasoner. Examples are provided of how reasoning can be bounded in an ESB agent and the mechanism to allow analysis of agent designs is discussed. Finally, there is discussion on the merits of the ESB solution and possible future work.

In this thesis an architecture, similar to subsumption architectures, is presented which uses low level behaviour modules, based on combinations of machine learning techniques, to create teams of autonomous agents cooperating via shared plans for interaction. The purpose of this is to perform effective single plan execution within multiple scenarios, using a modern team based first person shooter video game as the domain and visualiser. The main focus is showing that through basic machine learning mechanisms, applied in a multi-agent setting on sparse data, plans can be executed on game levels of varying size and shape without sacrificing team goals. It is also shown how different team members can perform locally sub-optimal operations which contribute to a globally better strategy by adding exploration data to the machine learning mechanisms. This contributes to the reinforcement learning problem of exploration versus exploitation, from a multi-agent perspective.

2006 Dozier Award Winner / Agent-based models can provide paleoanthropologists with a view of behavioral dynamics and site formation processes as they unfold in digital caricatures of past societies and paleoenvironments. This paper argues that the agent-based methodology has the most to offer when used to conduct controlled, repeatable experiments within the context of behavioral laboratories. To illustrate the potential of this decidedly heuristic approach, I provide a case study of a simple agent-based model currently being used to investigate the evolution of Plio-Pleistocene hominin food sharing in East Africa. The results of this null model demonstrate that certain levels of ecological patchiness can facilitate the evolution of even simple food sharing strategies among equally simple hominin foragers. More generally, they demonstrate the potential that agent-based models possess for helping historical scientists act as their own informants as to what could have happened in the past.

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