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.

This article presents new algorithms for inferring users' activities in a class of flexible and open-ended educational software called exploratory learning environments (ELEs). Such settings provide a rich educational environment for students, but challenge teachers to keep track of students' progress and to assess their performance. This article presents techniques for recognizing students' activities in ELEs and visualizing these activities to students. It describes a new plan-recognition algorithm that takes into account repetition and interleaving of activities. This algorithm was evaluated empirically using two ELEs for teaching chemistry and statistics used by thousands of students in several countries.

IPC-2014 was held in three separate parts to assess the state of the art in three prominent areas of planning research: the deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic part (IPPC). Each part evaluated planning systems in ways that pushed the edge of existing planner performance by introducing new challenges, novel tasks, or both. The competition surpassed again the number of competitors that participated in its predecessor, highlighting the competition's central role in shaping the landscape of ongoing developments in evaluating planning systems. Actions are usually expressed in terms of preconditions and effects. Preconditions indicate the requirements that must hold to apply the action, while effects are the consequence (including the cost) of applying the action to the state of the world.

Intentions in Communication is the outgrowth of an interdisciplinary workshop on the role of intention in theories of communication. Attending the workshop were researchers in computer science, linguistics, philosophy, and psychology. The resulting book contains edited versions of 14 papers (13 of which were presented at the workshop), commentaries, and an introduction. The topics of these papers range from philosophical analyses of the concept of intention to algorithms for recognizing plans, from logical formalizations of speech acts to analyses of intonational contours in discourse. The idea of relating intentions to the use of language is an outgrowth of speech act theory.

We describe the application of plan-recognition techniques to support human intelligence analysts in processing national security alerts. Our approach is designed to take the noisy results of traditional data-mining tools and exploit causal knowledge about attacks to relate activities and uncover the intent underlying them. Identifying intent enables us to both prioritize and explain alert sets to analysts in a readily digestible format. Our empirical evaluation demonstrates that the approach can handle alert sets of as many as 20 elements and can readily distinguish between false and true alarms. We discuss the important opportunities for future work that will increase the cardinality of the alert sets to the level demanded by a deployable application.