Probabilistic Interval XML EDWARD HUNG Hong Kong Polytechnic University and

Interest in XML databases has been expanding rapidly over the last few years. In this paper, we study the problem of incorporating probabilistic information into XML databases. We propose the Probabilistic Interval XML (PIXML for short) data model in this paper. Using this data model, users can express probabilistic information within XML markups. In addition, we provide two alternative formal model-theoretic semantics for PIXML data. The first semantics is a “global ” semantics which is relatively intuitive, but is not directly amenable to computation. The second semantics is a “local ” semantics which supports efficient computation. We prove several correspondence results between the two semantics. To our knowledge, this is the first formal model theoretic semantics for probabilistic interval XML. We then provide an operational semantics that may be used to comput

Utilizing volatile external information during planning

There are many practical planning situations in which planners may need information from external sources during the planning process. We describe the following: . Wrappers that may be placed around conventional (isolated) planners. The wrapper replaces some of the planner's memory accesses with queries to external information sources. When appropriate, the wrapper will automatically backtrack the planner to a previous point in its operation. . Query-management strategies for wrappers. These dictate when to issue queries, and when/how to backtrack the planner. . Mathematical analysis and experimental tests. Our results show conditions under which different query management strategies are preferable, and demonstrate that certain kinds of planning paradigms are more suited than others for planning with volatile information

Distributed algorithms for dynamic survivability of multiagent systems

Though multiagent systems (MASs) are being increasingly used, few methods exist to ensure survivability of MASs. All existing methods suffer from two flaws. First, a centralized survivability algorithm (CSA) ensures survivability of the MAS – unfortunately, if the node on which the CSA exists goes down, the survivability of the MAS is questionable. Second, no mechanism exists to change how the MAS is deployed when external factors trigger a re-evaluation of the survivability of the MAS. In this paper, we present three algorithms to address these two important problems. Our algorithms can be built on top of any CSA. Our algorithms are completely distributed and can handle external triggers to compute a new deployment. We report on experiments assessing the efficiency of these algorithms

Temporal Probabilistic Object Bases

There are numerous applications where we have to deal with temporal uncertainty associated with objects. The ability to automatically store and manipulate time, probabilities, and objects is important. We propose a data model and algebra for temporal probabilistic object bases (TPOBs), which allows us to specify the probability with which an event occurs at a given time point. In explicit TPOB-instances, the sets of time points along with their probability intervals are explicitly enumerated. In implicit TPOB-instances, sets of time points are expressed by constraints and their probability intervals by probability distribution functions. Thus, implicit object base instances are succinct representations of explicit ones; they allow for an efficient implementation of algebraic operations, while their explicit counterparts make defining algebraic operations easy. We extend the relational algebra to both explicit and implicit instances and prove that the operations on implicit instances correctly implement their counterpart on explicit instances