The Fourth International Conference on Knowledge Capture was held October 28-31, 2007, in Whistler, British Columbia. K-CAP 2007 included two invited talks, technical papers, posters, and demonstrations. Topics included knowledge engineering and modeling methodologies, knowledge engineering and the semantic web, mixed-initiative planning and decision-support tools, acquisition of problem-solving knowledge, knowledge-based markup techniques, knowledge extraction systems, knowledge acquisition tools, and advice-taking systems. This was the fourth in a series of meetings; the first was held in Victoria, British Columbia, in 2001; the second was collocated with the ISWC meeting and was held on Sanibel Island, Florida, in October 2003; and the third meeting was held in Banff, Alberta, in October 2005. The conference was held at the Fairmont Chateau in Whistler.

SPARK'08 was the first edition of a workshop series designed to provide a stable, longterm forum where researchers could discuss the applications of planning and scheduling techniques to real problems. Animated discussion characterized the workshop, which was collocated with the 18th International Conference on Automated Planning and Scheduling (ICAPS-08) held in Sydney, Australia, in September 2008. What keeps the fine advances in this field made over recent years hidden? The international Scheduling and Planning Applications Workshop (SPARK) was established to help address this issue. Building on precursory events, SPARK'08 was the first workshop designed to provide a stable, long-term forum where researchers could discuss the applications of planning and scheduling (P&S) techniques to real problems.

More specifically, we demonstrate how to transfer the domain-dependent heuristics acquired by one planner into a second planner. Our motivation is to improve the efficiency and the efficacy of the second planner by allowing it to use the transferred heuristics to capture domain regularities that it would not otherwise recognize. Our experimental results show that the transferred knowledge does improve the second planner's performance on novel tasks over a set of seven benchmark planning domains. Recently, the artificial intelligence community has attempted to model this transfer in an effort to improve learning on new tasks by using knowledge from related tasks. For example, classification and inference algorithms have been extended to support transfer of conceptual knowledge (for a survey see Torrey and Shavlik [2009]).

Such systems move away from a fixed factory line executing an unchanging set of operations and toward the goal of an adaptable factory structure. The logical next challenge in this area is that of online reconfigurability. With this capability, machines can reconfigure while running, enable or disable capabilities in real time, and respond quickly to changes in the system or the environment (including faults). We propose an approach to achieving online reconfigurability based on a high level of system modularity supported by integrated, model-based planning and control software. Our software capitalizes on many advanced techniques from the artificial intelligence research community, particularly in model-based domain-independent planning and scheduling, heuristic search, and temporal resource reasoning.

This was the eighth in a regular series that started in 1997. There have been eight workshops in the series. At this year's workshop held March 25-26, 2013, at the NASA Ames Research Center, Moffett Field, California, there were 26 technical papers and two invited talks on a wide range of topic areas relating to planning and scheduling, including: techniques and algorithms, applications to space or aerospace, planning, scheduling, plan execution, knowledge acquisition for planning and scheduling systems, embedded planning and execution systems, and other general topic areas. Applications included International Space Station payloads, space mission operations, human space flight, space observatories, planning for communications, airborne operations, and Earth observation scheduling. International researchers from space agencies, academia, and industry from Europe, America, Canada, and South America participated.

After creating a public repository of grid-based path-planning problems I created the Grid-Based Path-Planning Competition (GPPC) to facilitate these comparisons. This article describes the motivation and design of the competition, as well as plans for the future of the competition. These papers contain a wide variety of techniques and operate under a wide variety of constraints. All of them offer significant improvement over and beyond a basic A* search. But there has been no unified study comparing techniques and measuring the tradeoffs implicit in the approaches.

This, coupled with limited bandwidth and latencies, motivates on-board autonomy that ensures the quality of the science data return. Increasing quality of the data requires better sample selection, data validation, and data reduction. Robotic studies in Mars-like desert terrain have advanced autonomy for long-distance exploration and seeded technologies for planetary rover missions. In these field experiments the remote science team uses a novel control strategy that intersperses preplanned activities with autonomous decision making. The robot performs automatic data collection, interpretation, and response at multiple spatial scales.

Mission planning for space is challenging because of the mixture of goals and constraints. Every space mission tries to squeeze all of the capacity possible out of the spacecraft. For Orbital Express, this means performing as many experiments as possible, while still keeping the spacecraft safe. Keeping the spacecraft safe can be very challenging because we need to maintain the correct thermal environment (or batteries might freeze), we need to avoid pointing cameras and sensitive sensors at the sun, we need to keep the spacecraft batteries charged, and we need to keep the two spacecraft from colliding... made more difficult as only one of the spacecraft had thrusters. Because the mission was a technology demonstration, pertinent planning information was learned during actual mission execution.

The exploration of space is a testament to human curiosity and the desire to understand the universe that we inhabit. As many space agencies around the world design and deploy missions, it is apparent that there is a need for intelligent, exploring systems that can make decisions on their own in remote, potentially hostile environments. At the same time, the monetary cost of operating missions, combined with the growing complexity of the instruments and vehicles being deployed, make it apparent that substantial improvements can be made by the judicious use of automation in mission operations. Stringent communications constraints are present, including limited communication windows, long communication latencies, and limited bandwidth. Additionally, limited access and availability of operators, limited crew availability, system complexity, and many other factors often preclude direct human oversight of many functions.

Modern multicore computers provide an opportunity to parallelize plan-recognition algorithms to decrease run time. Viewing plan recognition as parsing based on a complete breadth first search, makes ELEXIR (engine for lexicalized intent recognition) (Geib 2009, Geib and Goldman 2011) particularly suited for parallelization. This article documents the extension of ELEXIR to utilize such modern computing platforms. We will discuss multiple possible algorithms for distributing work between parallel threads and the associated performance wins. We will show that the best of these algorithms provides close to linear speedup (up to a maximum number of processors), and that features of the problem domain have an impact on the achieved speedup.