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The importance of validating and reproducing the outcome of computational processes is fundamental to many application domains. Assuring the provenance of workflows will likely become even more important with respect to the incorporation of human tasks to standard workflows by emerging standards such as WS-HumanTask. This paper addresses this trend by an actor-based workflow approach that actively support provenance. It proposes a framework to track and store provenance information automatically that applies for various workflow management systems. In particular, the introduced provenance framework supports the documentation of workflows in a legally binding way. The authors therefore use the concept of layered XML documents, i.e. history-tracing XML. Furthermore, the proposed provenance framework enables the executors (actors) of a particular workflow task to attest their operations and the associated results by integrating digital XML signatures.
We propose a formalism for reasoning about actions based on multi-modal logic which allows for expressing observations as first-class objects. We introduce a new modal operator, namely [o |α], which allows us to capture the notion of perceiving an observation given that an action has taken place. Formulae of the type [o |α]ϕ mean ’after perceiving observation o, given α was performed, necessarily ϕ’. In this paper, we focus on the challenges concerning sensing with explicit observations, and acting with nondeterministic effects. We present the syntax and semantics, and a correct and decidable tableau calculus for the logic
The high-level decision making process of an autonomous robot can be seen as an hierarchically organised entity, where strategical decisions are made on the topmost layer, while the bottom layer serves as driver for the hardware. In between is a layer with monitoring and reporting functionality. In this paper we propose a behaviour engine for this middle layer which, based on formalism of hybrid state machines (HSMs), bridges the gap between high-level strategic decision making and low-level actuator control. The behaviour engine has to execute and monitor behaviours and reports status information back to the higher level. To be able to call the behaviours or skills hierarchically, we extend the model of HSMs with dependencies and sub-skills. These Skill-HSMs are implemented in the lightweight but expressive Lua scripting language which is well-suited to implement the behaviour engine on our target platform, the humanoid robot Nao.