In contingent planning under partial observability with sensing actions, agents actively use sensing to discover meaningful facts about the world. For this class of problems the solution can be represented as a plan tree, branching on various possible observations. Recent successful approaches translate the partially observable contingent problem into a non-deterministic fully observable problem, and then use a planner for non-deterministic planning. While this approach has been successful in many domains, the translation may become very large, encumbering the task of the non-deterministic planner. In this paper we suggest a different approach - using an online contingent solver repeatedly to construct a plan tree. We execute the plan returned by the online solver until the next observation action, and then branch on the possible observed values, and replan for every branch independently. In many cases a plan tree can be exponential in the number of state variables, but still, the tree has a structure that allows us to compactly represent it using a directed graph. We suggest a mechanism for tailoring such a graph that reduces both the computational effort and the storage space. Furthermore, unlike recent state of the art offline planners, our approach is not bounded to a specific class of contingent problems, such as limited problem width, or simple contingent problems. We present a set of experiments, showing our approach to scale better than state of the art offline planners.

We describe a new sound and complete method forcompiling contingent planning problems with sensingactions into classical planning. Our method encodesconditional plans within a linear, classicalplan. This allows our planner, MPSR, to reasonabout multiple future outcomes of sensing actions,and makes it less susceptible to dead-ends. MPRS,however, generates very large classical planningproblems. To overcome this, we use an incompletevariant of the method, based on state sampling,within an online replanner. On most currentdomains, MPSR finds plans faster, although itsplans are often longer. But on a new challengingvariant of Wumpus with dead-ends, it finds smallerplans, faster, and scales much better.

While team action recognition has a relatively extended literature, less attention has been given to the detailed realtime analysis of the internal structure of the team actions.  This includes recognizing the current state of the action, predicting the next state, recognizing deviations from the standard action model, and handling ambiguous cases. The underlying probabilistic reasoning model has a major impact on the type of data it can extract, its accuracy, and the computational cost of the reasoning process. In this paper we are using Cascading Hidden Markov Models (CHMM) to analyze Bounding Overwatch, an important team action in military tactics. The team action is represented in the CHMM as a plan tree. Starting from real-world recorded data, we identify the subteams through clustering and extract team oriented discrete features. In an experimental study, we investigate whether the better scalability and the more structured information provided by the CHMM comes with an unacceptable cost in accuracy. We find the a properly parametrized CHMM estimating the current goal chain of the Bounding Overwatch plan tree comes very close to a flat HMM estimating only the overall Bounding Overwatch state (a subset of the goal chain) at a respective overall state accuracy of 95% vs 98%, making the CHMM a good candidate for deployed systems.