Plan synthesis often requires complete domain models and initial states as input. In many real world applications, it is difficult to build domain models and provide complete initial state beforehand. In this paper we propose to turn to the crowd for help before planning. We assume there are annotators available to provide information needed for building domain models and initial states. However, there might be a substantial amount of discrepancy within the inputs from the crowd. It is thus challenging to address the planning problem with possibly noisy information provided by the crowd. We address the problem by two phases. We first build a set of Human Intelligence Tasks (HITs), and collect values from the crowd. We then estimate the actual values of variables and feed the values to a planner to solve the problem.

Multi-Agent Plan Recognition (MAPR) seeks to identify the dynamic team structures and team behaviors from the observed activity sequences (team traces) of a set of intelligent agents, based on a library of known team activity sequences (team plans). Previous MAPR systems require that team traces and team plans are fully observed. In this paper we relax this constraint, i.e., team traces and team plans are allowed to be partial. This is an important task in applying MAPR to real-world domains, since in many applications it is often difficult to collect full team traces or team plans due to environment limitations, e.g., military operation. This is also a hard problem since the information available is limited. We propose a novel approach to recognizing team plans from partial team traces and team plans. We encode the MAPR problem as a satisfaction problem and solve the problem using a state-of-the-art weighted MAX-SAT solver. We empirically show that our algorithm is both effective and efficient.

Though with the rapid development of economic globalization and informationization, Chinese e-government development is still in its infancy with a long way ahead. Based on some resent research on today's e-government in Guangdong, we propose a special model to describe business process in e-government affair operations. The model extends finite state machine model by message passing, business logic and several kinds of constraint check. Using this model to represent governmental business process helps to capture the exact status of each process step, standardize service procedure, describe complex constraint in the process and bring flexibility into its customization, which eventually results in more efficient delivery of services and facilitates greater transparency in the administration of government. Furthermore, it offers new thoughts in representing business process.

Concurrency control strategy is important and necessary to Collaborative Authoring System (CAS) which allows several users to edit a document simultaneously. Locking algorithm adopted in traditional CAS can't achieve excellent concurrency with high performance. In this paper we propose an adaptive-granularity locking algorithm, which determines lock granularity by means of user's operations dynamically. The experiments we have carried out show that our algorithms achieve the concurrency of fine-granularity locking with lower cost.