Observability of user-interfaces for linear hybrid systems under collaborative control

Human interaction with automation is ubiquitous, occurring in many cyberphysical systems such as cell phones, automobiles, and commercial aircraft. When interacting with such systems, human users are only exposed to a simplified representation the complex system structure in the form of an interface. The human can observe system outputs and make control inputs via this interface. Problems with human-automation interaction occur when the interface does not provide enough information or provides misinformation about the underlying system, such that the human cannot determine the current state of the automation. The user\u27s knowledge of the current system state and prediction of the next system state is required for effective operation of an automated system. In this work, formal methods are employed to analyze user-interfaces of such cyberphysical systems in order to reveal state observability problems. The cyberphysical systems are modeled as hybrid systems, for which continuous behavior emerges from the laws of physics and discrete behavior results from logical conditions and rules governing the automation. Hybrid systems with LTI continuous dynamics under collaborative control are considered, where collaborative control indicates that some events and inputs are controlled by a human operator while other events and inputs are controlled by the automation. The human user is assumed to be a special type of state observer, with additional requirements beyond a standard (automated) state observer. To reflect these additional requirements, sufficient conditions for user-observability and user-predictability of linear hybrid systems under collaborative control are developed. Algorithms are generated to evaluate a user-interface based on these conditions for user-observability and user-predictability. Then, the algorithms are applied to a hybrid system model abstraction of the longitudinal dynamics of an aircraft flight management system

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence