In this paper, we present an approach to detecting novel cyber attacks though a form of program diversification, similar to the use of n-version programming for fault tolerant systems. Building on extensive previous and ongoing work by others on the use of code clones in a wide variety of areas, our Functionally Equivalent Variants using Information Synchronization (FEVIS) system automatically generates program variants to berun in parallel, seeking to detect attacks through divergence in behavior. Unlike approaches to diversification that only change program memory layout and behavior, FEVIS can detect attacks exploiting vulnerabilities in execution timing, string processing, and other logic errors. We are in the early stages of research and development for this approach, but have made sufficient progress to provide a proof of concept and some lessons learned. In this paper we describe FEVIS and its application to diversifying an open-source webserver, with results on several different example classes of attack which FEVIS will detect.

Abstract: In this paper, we describe a set of software tools called the PRIDE ONTOlogy Editor (PRONTOE) and a methodology that allows system operators and domain experts to build and maintain ontologies of their sys-tems with no explicit understanding of the underlying ontology representation. We present two case studies: one using NASA flight controllers, and another using the DARPA Robotic Challenge. 1

Computer Go presents a challenging problem for machine learning agents. With the number of possible board states estimated to be larger than the number of hydrogen atoms in the universe, learning effective policies or board evaluation functions is extremely difficult. In this paper we describe Cortigo, a system that efficiently and autonomously learns useful generalizations for large state-space classification problems such as Go. Cortigo uses a hierarchical generative model loosely related to the human visual cortex to recognize Go board positions well enough to suggest promising next moves. We begin by briefly describing and providing motivation for research in the computer Go domain. We describe Cortigo’s ability to learn predictive models based on large subsets of the Go board and demonstrate how using Cortigo’s learned models as additive knowledge in a state-of-the-art computer Go player (Fuego) significantly improves its playing strength.

PDDL was originally conceived and constructed as a lingua franca for the International Planning Competition. PDDL2.1 embodies a set of extensions intended to support the expression of something closer to ``real planning problems.'' This objective has only been partially achieved, due in large part to a deliberate focus on not moving too far from classical planning models and solution methods.

Selman and Kautz's paper on the logic of model-preference defaults introduced a new language and tractability results. This paper assesses the contributions of that work, including the properties of the logic itself, its relationship to some previous