This paper describes potential applications of Biologically Inspired Cognitive Architectures to Intelligence Analysis. The focus of our efforts is on higher level reasoning rather than low level perception. We will never have enough human analysts to read, filter and make sense of all the text data out there. Can some form of BICA help? In this paper we discuss issues related to knowledge acquisition, natural language processing and cognitive architectures that we have encountered in an ongoing project to apply the Sandia Cognitive Framework to analysis problems. We believe that studying intelligence analysis will lead to new insights into BICA.
We view sensemaking in threat analysis as abducing stories that explain the current data and make verifiable predictions about future data. We have developed a preliminary system, called STAB, that abduces multiple stories from the VAST-2006 dataset. STAB uses the TMKL knowledge representation language to represent skeletal story plots as plans with goals and states, and to organize the plans in goal-plan-subgoal abstraction hierarchies.
Over the past several years, pilot-oriented mobile applications have seen widespread adoption among recreational pilots. Pilots have reported they provide significant workload savings by eliminating the need to manage paper charts, manuals, and checklists in the cockpit. The pilot, nonetheless, still must go looking for the information when it is required, increasing accident risk by diverting attention away from control of the aircraft. In this paper, we provide an overview of a cognitive assistant that determines when information is required based on flight context and automatically provides it to the pilot at the appropriate time. In addition to an overview of the concept, a recent evaluation is discussed alongside future plans to evaluate the safety of the Digital Copilot.
Decision analysis and knowledge-based expert systems share some common goals. Both technologies are designed to improve human decision making; they attempt to do this by formalizing human expert knowledge so that it is amenable to mechanized reasoning. However, the technologies are based on rather different principles. Decision analysis is the application of the principles of decision theory supplemented with insights from the psychology of judgment. Expert systems, at least as we use this term here, involve the application of various logical and computational techniques of AI to the representation of human knowledge for automated inference.
Decision analysis and expert systems are technologies intended to support human reasoning and decision making by formalizing expert knowledge so that it is amenable to mechanized reasoning methods. Despite some common goals, these two paradigms have evolved divergently, with fundamental differences in principle and practice. Recent recognition of the deficiencies of traditional AI techniques for treating uncertainty, coupled with the development of belief nets and influence diagrams, is stimulating renewed enthusiasm among AI researchers in probabilistic reasoning and decision analysis. We present the key ideas of decision analysis and review recent research and applications that aim toward a marriage of these two paradigms. This work combines decision-analytic methods for structuring and encoding uncertain knowledge and preferences with computational techniques from AI for knowledge representation, inference, and explanation. We end by outlining remaining research issues to fully develop the potential of this enterprise.