Abstract-This paper introduces new analytical techniques for performing vulnerability analysis of state estimation when it is subject to a hidden false data injection cyber-attack on a power grid's SCADA system. Specifically, we consider ac state estimation and describe how the physical properties of the system can be used as an advantage in protecting the power system from such an attack. We present an algorithm based on graph theory which allows determining how many and which measurement signals an attacker will attack in order to minimize his efforts in keeping the attack hidden from bad data detection. This provides guidance on which measurements are vulnerable and need increased protection. Hence, this paper provides insights into the vulnerabilities but also the inherent strengths provided by ac state estimation and network topology features such as buses without power injections.

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In this paper, we present Warren, a multi-agent system for intelligent portfolio management, which is motivated by the great benefits of working in teams within the domain of Distributed Artificial Intelligence (DAI) and TextMiner which takes advantage of information retrieval techniques to complement quantitative financial information. In the portfolio management domain, software agents that evaluate the risks associated with the individual companies in a portfolio should be able to read news articles that indicate the financial outlook of a company. There is a positive correlation between news reports on a company's financial outlook and its attractiveness as an investment. Since it is impossible for financial analysts or investors to track and read each one, it would be very helpful to have a technology for automatically analyzing news reports that reflect positively or negatively on a company's financial outlook. It is also necessary for an agent to learn changes in the news autonomously. To accomplish these tasks, we devised a new text classification method and a sampling method. With comprehensive quantitative information gathered by efficient coordinations between agents, and the supplementing of quantitative information by financial news analysis, we showed a successful application of a multi-agent system for portfolio management.

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In this paper, we describe how agents can support collaborative planning within international coalitions, formed in an ad hoc fashion as a response to military and humanitarian crises. As these coalitions are formed rapidly and without much lead time or co-training, human planners may be required to observe a plethora of policies that direct their planning effort. In a series of experiments, we show how agents can support human planners, ease their cognitive burden by giving advice on the correct use of policies and catch possible violations. The experiments show that agents can effectively prevent policy violations with no significant extra cost.

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Recently the number of autonomous agents and multiagent systems (MAS) that have been developed by different developers has increased. Despite efforts for the creation of standards (eg. in communication languages, registration protocols etc.), it is clear that at least in the near term heterogeneous agents and MASs will be prevalent. Therefore, mechanisms that allow agents and/or MASs to interoperate and transact are needed. In this paper we report on a case study and lessons learned of an interoperator agent we developed. We discuss requirements for interoperation mechanisms, resulting challenges and our design decisions and implementation of the RETSINA-OAA InterOperator 1.