Detecting Intrusions in Mobile Agents

This research identifies and analyzes ways that ensure the secure operation of a multi-agent system (MAS) on an untrusted host. Because of the propensity of the malicious host to spy and leak usage patterns, block useful work and feed false or misleading information, it becomes necessary for the software agent to detect intrusions and protect its security. While enabling secure transactions on a malicious host is considered one of the most challenging problems in mobile agent security, ideas such as mess-up code, time-limited black-box security, and alternating usage of secure hardware can be found in literature. The goal of this project is to design a simple protocol that utilizes a combination of the aforementioned ideas in conjunction with some novel ideas in code/data separation, trust-establishment and a probabilistic method to mask legitimate data transmission and interaction patterns to demonstrate certain levels of security can be achieved in a malicious environment

An Autonomous Performance Control Framework for Distributed Multi-Agent Systems: A Queueing Theory Based Approach

Distributed Multi-Agent Systems (DMAS) such as supply chains functioning in highly dynamic environments need to achieve maximum overall utility during operation. The utility from maintaining performance is an important component of their survivability. This utility is often met by identifying trade-offs between quality of service and performance. To adaptively choose the operational settings for better utility, we propose an autonomous and scalable queueing theory based methodology to control the performance of a hierarchical network of distributed agents

A Framework for Performance Control of Distributed Autonomous Agents

We propose an autonomous and scalable queueing theory-based methodology to control the performance of a hierarchical network of distributed agents. Multi-agent systems (MAS) such as supply chains functioning in highly dynamic environments need to achieve maximum overall utility during operation. Hence, the objective of the control framework is to identify the trade-offs between quality and performance and adaptively choose the operational settings to posture the MAS for better utility. By formulating the MAS as an open queueing network with multiple classes of traffic we evaluate the performance and subsequently the utility, from which we identify the control alternative for a localized, multi-tier zone

Reliable MAS Performance Prediction Using Queueing Models

In this paper, we model a multi-agent system (MAS) in military logistics based on the systemic specifications of the capabilities and attributes of individual agents (TechSpecs). Assuring the survivability of the MAS that implements distributed planning and execution is a significant design-time and run-time challenge. Dynamic battlefield stresses in military logistics range from heavy computational loads (information warfare) to being destructive to infrastructure. In order to sustain and recover from damages to continuously deliver performance, a mechanism that distributes knowledge about the capabilities and strategies of the system is crucial. Using a queueing model to represent the network of distributed agents, strategies are developed for a prototype military logistics system. The TechSpecs contain the capabilities of the agents, playbooks or rules, quantities to monitor, types of information flow (input/output), measures of performance (Quality of Service) and their computation methods, measurement points, defenses against stresses and configuration details (to reflect command and control structure as well as task flow). With these details, models could be dynamically developed and analyzed in real-time for fine-tuning the system. Using a Cougaar (DARPA Agent Framework) based model for initial parameter estimation and analysis, we obtain an analytical and a simulation model and extract generic results. Results indicate strong correlation between experimental and actual events in the agent society. Keywords: Multi-agent systems, Survivability, Queueing network models, Technical specifications 1

Improving the Character of Optical Character Recognition (OCR): iDigBio Augmenting OCR Working Group Seeks Collaborators and Strategies to Improve OCR Output and Parsing of OCR Output ...

There are an estimated 2 – 3 billion museum specimens world – wide (OECD 1999, Ariño 2010). In an effort to increase the research value of their collections, institutions across the U. S. have been seeking new ways to cost effectively transcribe the label information associated with these specimen collections. Current digitization methods are still relatively slow, labor-intensive, and therefore expensive. New methods, such as optical character recognition (OCR), natural language processing, and human-in-the-loop assisted parsing are being explored to reduce these costs. The National Science Foundation (NSF), through the Advancing Digitization of Biodiversity Collections (ADBC) program, funded Integrated Digitized Biocollections (iDigBio) in 2011 to create a Home Uniting Biodiversity Collections (HUB) cyberinfrastructure to aggregate and collectively integrate specimen data and find ways to digitize specimen data faithfully and faster and disseminate the knowledge of how to achieve this. The iDigBio Augmenting OCR Working Group is part of this national effort. - speed up the overall digitization process, - lower the cost, - improve overall efficiency, - assure digitized data is fit-for-use (NIBA 2010, Chapman 2005), and - provide the resulting digitized data records to researchers more quickly. The iDigBio Augmenting OCR (A-OCR) working group is actively engaged in identifying opportunities for collaboration to leverage OCR tools and technologies that are successful (both within and outside of the biology digitization domain) and disseminate these tools to the public or seek funding for development. "published or submitted for publicationis peer reviewe