Youngwoo Seo

Youngwoo Seo

Field Roboticist, Computer Scientist, Entrepreneur, Explorer

Enabling Multi-Agents System to be Intelligent

Published:

Multi-Agents System for Enforcing Need-to-know Security Policy The “Need-to-know” authorization is that grants access to confidential information only if that information is necessary for the requester’s task or project. Here, confidential information refers to property containing knowledge that is sensitive to an individual or organizations; hence its careless release may have a damaging effect. We devised a multi-agent system architecture for the adaptive authorization of access to confidential information. The developed system provides “need-to-know” content-based authorization of requests to access confidential information that extend the protections offered by security mechanisms such as access control lists (ACLs). We treat the authorization task as a text classification problem in which the classifier must learn a human supervisor’s decision criteria with small amounts of labeled information, e.g. 20 to 30 textual documents, and to be capable of generalizing to other documents with a near-zero false alarm rate. Since “need-to-know” authorizations must be determined for multiple tasks, multiple users, and multiple collections of confidential information, with quick turn-around from definition to use, the authorization agent must be adaptive and capable of learning new profiles quickly and with little impact on the productivity of the human supervisor and the human end-user. When a request for confidential information occurs, the authorization agent compares the content of the requested information to the description of the requester’s project. The request is approved for access if the requester’s project is determined to be “relevant” to the requested item. An unauthorized attempt to acceess a unit of confidential information that the requester does not “need to know” is undoubtly rejected because the requester’s project description is not at all similar to that information. To this end, we examined five different text classification methods for solving this problem, ``agentified” the best performer, and inserted it in a secure document management system context. This work is significant in that it enables a human supervisor to conveniently and cost-effectively identify arbitrary subsets of confidential information and to associate security policies to it. The multi-agent system, by integrating with a secure document management system, enables the automatic enforcement of such security policies, as well as tracks authorized and unauthorized attempts to access the confidential information. Read the following papers to learn more about this work:

Information Fusion for Command and Control This work aims at developing a method that helps a software agent discern a set of relevant and essential information from all the available information sources. The resulting method enables software agent to accomplish a given task, on time, by effectively utilizing those identified set of information. Furthermore, it could be very useful to handle properly the problem of “data overload, information starvation.” For example, such provision will help a human decision maker draw a timely conclusion with less uncertainty. As a preliminary work, I had investigated the related literature such as the study of Link Analysis, Social Network Analysis, and modeling of trust/reputation in the multi-agent systems. As a result of this work, a new model for estimating reliablity (or trust) of information provided by agents in multi-agents community is developed. This model provides an agent with a method that helps to determine which agents in the same community are trustworthy so that it can accomplish its tasks efficiently by collaborating trustworthy agents. Following those notions of human intuitively, the trustworthiness of an agent is estimated by linearly combining two factors: truster’s direct experiences and the statement of target agent’s reputations from other agents. Read the following papers to learn more about this work:

Multi-Agents System for Assisting Fincial Portfolio Management The WARREN system, an application of the RETSINA multi-agent architecture, deploys a number of different, autonomous software agents that acquire information from and monitor changes to stock reporting databases. These agents also interpret stock information, suggest the near future of an investment, and track and filter relevant financial news articles for the user to read. Read the following papers to learn more about this work: