Cybersecurity professionals often speak of tactics, techniques and procedures (TTPs) when describing the activities of threat actors, yet these terms are not as well defined in cybersecurity as in military doctrine. Systems that use artificial intelligence (AI) and machine learning (ML) to address cybersecurity problems could better determine adversarial intent and future actions by connecting sequences of actions to threat actor intent. In this paper, we define TTPs in relation to cybersecurity and present a model of TTPs for cyberspace operations that is useful to both humans and synthetic agents. We then describe how these can be applied to real-world cyberspace operations, using advanced persistent threat (APT) 28's Pawn Storm campaign as an exemplar. Finally, we show how we've approached the development of ML algorithms to provide predictive analytics based on large security datasets.

Patterns of life (POL) are emergent properties of com- plex social systems such as neighborhoods or even cities. Accurately modeling POL is not only an academic pursuit; complex training and analysis efforts rely upon these models. Computational POL models will benefit academic researchers by giving them new tools to generate and validate theories about complex sociocultural patterns. POL presents an interesting computational modeling challenge for research in many AI fields because of its complex, multi-level interaction. POL modeling differs from standard agent-based modeling in that not only individual behaviors, but also their collective emergent patterns, are targets of study. At the same time, the behavior of any single individual is potentially important.

This paper describes Eureka, a problem-solving architecture that operates under strong constraints on its memory and processes. Most significantly, Eureka does not assume free access to its entire long-term memory. That is, failures in problem solving may arise not only from missing knowledge, but from the (possibly temporary) inability to retrieve appropriate existing knowledge from memory. Additionally, the architecture does not include systematic backtracking to recover from fruitless search paths. These constraints significantly impact Eureka's design. Humans are also subject to such constraints, but are able to overcome them to solve problems effectively. In Eureka's design, we have attempted to minimize the number of additional architectural commitments, while staying faithful to the memory constraints. Even under such minimal commitments, Eureka provides a qualitative account of the primary types of learning reported in the literature on human problem solving. Further commitments to the architecture would refine the details in the model, but the approach we have taken de-emphasizes highly detailed modeling to get at general root causes of the observed regularities. Making minimal additional commitments to Eureka's design strengthens the case that many regularities in human learning and problem solving are entailments of the need to handle imperfect memory.

This paper presents a course in the design and implementation of computer games, offered as an upper-division computer science course at Colby College during the winter semester, 1999. The paper describes the material, topics, and projects included in the course. More generally, I argue that this course provides an ideal environment for students to integrate a wide base of computer knowledge and skills. The paper supports this argument by presenting the variety of computer science concepts covered in the course, as well as pointing out potential areas of variation in future courses, depending on the tastes and priorities of the instructor.

In recent years, there has been a fair amount of research directed toward the goal of developing virtual, human-like characters for simulation environments. Much of this work has focused on creating high-fidelity graphical animations that represent realistic human forms and movement. We are approaching the same goal from a different angle, focusing on the real-time generation of autonomous, intelligent behaviors that a virtual human must use to attain its goals in a complex environment. Because the emphasis of our work is on high-level behavior rather than visual representation, our current work is geared toward non-visual simulation environments. TacAir-Soar is a system that generates intelligent behavior for flying missions in simulated fixed-wing aircraft for military training simulations. The system has been in development for over five years, and has participated in multiple military training exercises. This paper presents many of the lessons we have learned in developing an autonomous, real-time system, together with suggestions for how these lessons might apply to the development of a full real-time, autonomous, virtual human that also incorporates realistic visual representation and movement.