Novelty detection in open worlds is a valuable endeavor for improving the robustness of autonomous systems. Open worlds are characterized by a lack of constraints on the types of novelties that might occur. Here we describe recent progress on detecting novelties in open-world numerical properties in our OpenMIND agent, a domain-independent planning-based AI architecture. Our approach has three elements: (1) Feature construction by crossing a tractable number of domain-dependent sensors with some basic domainindependent statistical derivations, (2) online, univariate changepoint detection on each signal, and (3) negative feature selection by screening for false positives in non-novel scenarios. We report on the effectiveness of this approach in a single-blind evaluation in which OpenMIND plays a game and encounters novelties never observed by it or its developers.

PDDL+ planning involves reasoning about mixed discrete-continuous change over time. Nearly all PDDL+ planners assume that continuous change is linear. We present a new technique that accommodates nonlinear change by encoding problems as nonlinear hybrid systems. Using this encoding, we apply a Satisfiability Modulo Theories (SMT) solver to find plans. We show that it is important to use novel planning- specific heuristics for variable and value selection for SMT solving, which is inspired by recent advances in planning as SAT. We show the promising performance of the resulting solver on challenging nonlinear problems.

We are developing autonomous control systems for mission-critical domains that require hard real-time performance guarantees. To automatically build reactive plans that meet these requirements, we use formal verification (model checking) techniques to assess the quality of plans as they are built. The verification process uses precise timed automaton models of the executive that will run the resulting reactive plan. This reflexive modeling allows our system to formally verify not just that its plans are correct, but that they will be executed correctly.

Concerns the development of autonomous, flexible control systems for mission-critical applications such as unmanned aerial vehicles (UAV) and deep space probes. These applications require hybrid real-time control systems, capable of effectively managing both discrete and continuous controllable parameters to maintain system safety and achieve system goals. Using the CIRCA architecture and its state space planner (SSP) for adaptive real-time control systems, these controllers are synthesized automatically and dynamically, online, while the platform is operating. Unlike many other AI planning systems, CIRCA's automatically-generated control plans have strong temporal semantics and provide safety guarantees, ensuring that the controlled system will avoid all forms of mission-critical failure. This paper is intended to convey an intuitive, qualitative understanding of the way CIRCA verifies its plans using model checking techniques.

To be truly useful, mobile robots need to be fairly autonomous and easy to control. This is especially true in situations where multiple robots are used, due to the increase in sensory information and the fact that the robots can interfere with one another. The paper describes a system that integrates autonomous navigation, a task executive, task planning, and an intuitive graphical user interface to control multiple, heterogeneous robots. We have demonstrated a prototype system that plans and coordinates the deployment of teams of robots. Testing has shown the effectiveness and robustness of the system, and of the coordination strategies in particular.

Most research into applying AI techniques to real-time control problems has limited the power of AI methods or embedded reactivity in an AI system. An alternative, cooperative architecture is presented. It uses separate AI and real-time subsystems to address the problems for which each is designed. A structured interface allows the subsystems to communicate without compromising their respective performance goals. By reasoning about its own bounded reactivity, cooperative intelligent real-time control architecture (CIRCA) can guarantee that it will meet hard deadlines while still using unpredictable AI methods. With its abilities to guarantee or trade off the timeliness, precision, confidence, and completeness of its output, CIRCA provides more flexible performance than previous systems.>

While researchers strive to develop new systems to enhance the cooperative document editing process, many authors already collaborate, using existing text processing systems to produce papers and reports. Using these tools, one of the most time-consuming and error-prone collaboration tasks is maintaining a consistent shared bibliography. We have designed and implemented the BIBDB system to simplify collaborative authoring by providing a shared, cooperatively maintained bibliographicdatabase. BIBDB uses existing networking technology and merges seamlessly into the LTEX/BIBTEX text processing system [5]. The contributions of BIBDB include a set of user interface policies and software implementation techniques that support cooperative database maintenance.