Abstract. This paper presents an ontology and a filtering algorithm used in an agent-based system to support communication in case of incidents in the mobile human surveillance domain. In that domain reaching the right people as soon as possible is of the essence when incidents occur. The main goal of our efforts is to significantly reduce the response time in case of incidents by proposing and setting up the communication to the right people. Experimental results show that this can reduce the response time by more than 50%, e.g., from 40 to 20 minutes. To continuously improve the accuracy of the proposed communications, the agent-based system uses feedback mechanisms. An implementation of this system, ASK-ASSIST, has been deployed at a mobile human surveillance company.

We develop and evaluate ASK-ASSIST, a decision support system that enables automatically setting up communication networks and sharing knowledge amongst security personnel, in particular when they are confronted with a series of unexpected incidents while on patrol. Currently, team leaders of mobile security guards are still forced to assess the consequences of a series of incidents and to take decisions in a heuristic way. On the basis of incomplete and uncertain information they have to determine the security situation, who to bring in contact with whom, and which personnel or which information systems should be lined up in order to resolve the incidents as a whole. ASK-ASSIST, however, automates robust dynamic formation and coordination of the right coalitions of security personnel, such that they support human improvisation in determining the nature of incidents and taking decisions by the coalitions. These coalitions comprise security personnel, communication security infrastructures and knowledge management security infrastructures. ASK-ASSIST is based on a framework for selforganization that generates ranked lists of incident-specific critical (re)configurations of coalitions in which joint tasks are assigned to agents associated with roles, like guards or team leaders. Our main contributions are the grounding and evaluation of the framework underlying ASK-ASSIST. We instantiate, validate and test the system on the basis of real data. These data concern organizational structures reflected in the coalitions and implicit or explicit feedback provided by personnel from a private security company. The provided feedback relates to the daily operational mobile security surveillance processes including those that involve functionalities of ASK-ASSIST.

We develop and evaluate ASK-ASSIST, a decision support system that enables automatically setting up communication networks and sharing knowledge amongst security personnel, in particular when they are confronted with a series of unexpected incidents while on patrol. Currently, team leaders of mobile security guards are still forced to assess the consequences of a series of incidents and to take decisions in a heuristic way. On the basis of incomplete and uncertain information they have to determine the security situation, who to bring in contact with whom, and which personnel or which information systems should be lined up in order to resolve the incidents as a whole. ASK-ASSIST, however, automates robust dynamic formation and coordination of the right coalitions of security personnel, such that they support human improvisation in determining the nature of incidents and taking decisions by the coalitions. These coalitions comprise security personnel, communication security infrastructures and knowledge management security infrastructures. ASK-ASSIST is based on a framework for selforganization that generates ranked lists of incident-specific critical (re)configurations of coalitions in which joint tasks are assigned to agents associated with roles, like guards or team leaders. Our main contributions are the grounding and evaluation of the framework underlying ASK-ASSIST. We instantiate, validate and test the system on the basis of real data. These data concern organizational structures reflected in the coalitions and implicit or explicit feedback provided by personnel from a private security company. The provided feedback relates to the daily operational mobile security surveillance processes including those that involve functionalities of ASK-ASSIST.

Business Activity Monitoring (BAM) and Business Intelligence (BI) solutions are both intended to provide insight into the activities and performance of the enterprise. Deployment of such systems requires extensive tailoring to the enterprise, best left to experts. The dynamics of the enterprise demands a solution to the maintenance of BAM/BI solutions. This paper presents an Ontology-based BAM-Agent, called OBAMA that supports the maintenance of the system in light of changing business processes. Furthermore, for the formulation of aspects and properties to be monitored, it combines the expressive power of SQL, and TTL (a temporal trace language of first order logic). OBAMA helps in the preparation of regular assessment reports on the enterprise, taking into account key performance indicators as set by its operation manager. The paper describes the architecture, the combination of SQL, and TTL techniques for monitoring, and provides description of its kernel processes. OBAMA's performance in a surveillance company is presented.

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