Despite a tremendous amount of work in the literature and in the commercial sectors, current approaches to multi-modal trip planning still fail to consistently generate plans that users deem optimal in practice. We believe that this is due to the fact that current planners fail to capture the true preferences of users, e.g., their preferences depend on aspects that are not modeled. An example of this could be a preference not to walk through an unsafe area at night. We present a novel multi-modal trip planner that allows users to upload auxiliary geographic data (e.g., crime rates) and to specify temporal constraints and preferences over these data in combination with typical metrics such as time and cost. Concretely, our planner supports the modes walking, biking, driving, public transit, and taxi, uses linear temporal logic to capture temporal constraints, and preferential cost functions to represent preferences. We show by examples that this allows the expression of very interesting preferences and constraints that, naturally, lead to quite diverse optimal plans.

This article describes our application of AI planning to the problem of automated process planning for machining parts, given raw stock and a CAD file describing the desired part geometry. We have found that existing planners from the AI community were falling short on several requirements, most importantly regarding the expressivity of state and action representations, and the ability to exploit domain-specific knowledge to prune the search space. In this article we describe the requirements we had in this application and what kind of results from the planning community helped us most. Overall, in this project as well as others, we found that even significant results from domain-independent planning may not be relevant in practice.

A large portion of today's industrial manufacturing relies on At Palo Alto Research Center (PARC), researchers recognized the potential business value to designers as well as manufacturers, and this value proposition was validated during project execution by presenting early prototypes of the software to potential users. The objective of PARC's uFab project hence was to create a software tool that, given just a CAD file and a representation of available machines and tools, generates a process plan in real time. While work in this area had been done in the 1980s under the name computer-aided process planning (CAPP) (Alting and Zhang 1989), none of the approaches that were pursued then resulted in a fully automated solution. A major shortcoming of these systems was their reliance on features, recognizable configurations of faces on a part such as pockets, slots, and holes, in order to represent states and actions. Any advances that This reliance on feature-based representations to these domain-specific needs, implementing are specific to domain-independent hindered their broad applicability the actual search used for planning in PDDL, such as the powerful to parts that could not be easily planning was the easy part.

Generating optimal plans in highly dynamic environments is challenging. Plans are predicated on an assumed initial state, but this state can change unexpectedly during plan generation, potentially invalidating the planning effort. In this paper we make three contributions: (1) We propose a novel algorithm for generating optimal plans in settings where frequent, unexpected events interfere with planning. It is able to quickly distinguish relevant from irrelevant state changes, and to update the existing planning search tree if necessary. (2) We argue for a new criterion for evaluating plan adaptation techniques: the relative running time compared to the "size" of changes. This is significant since during recovery more changes may occur that need to be recovered from subsequently, and in order for this process of repeated recovery to terminate, recovery time has to converge. (3) We show empirically that our approach can converge and find optimal plans in environments that would ordinarily defy planning due to their high dynamics.

To assist scientists in data analysis tasks, we have developed semantic workflow representations that support automatic constraint propagation and reasoning algorithms to manage constraints among the individual workflow steps. Semantic constraints can be used to represent requirements of input datasets as well as best practices for the method represented in a workflow. We demonstrate how the Wings workflow system uses semantic workflows to assist users in creating workflows while validating that the workflows comply with the requirements of the software components and datasets. Wings reasons over semantic workflow representations that consist of both a traditional dataflow graph as well as a network of constraints on the data and components of the workflow.

We present a formal approach for combining programming by demonstration (PbD) with programming by instruction (PbI) — a largely unsolved problem. Our solution is based on the integration of two successful formalisms: version space algebras and the logic programming language Golog. Version space algebras have been successfully applied to programming by demonstration. Intuitively, a version space describes a set of candidate procedures and a learner filters this space as necessary to be consistent with all given demonstrations of the target procedure. Golog, on the other hand, is a logical programming language defined in the situation calculus that allows for the specification of non-deterministic programs. While Golog was originally proposed as a means for integrating programming and automated planning, we show that it serves equally well as a formal framework for integrating PbD and PbI. Our approach is the result of two key insights: (a) Golog programs can be used to define version spaces, and (b) with only a minor augmentation, the existing Golog semantics readily provides the update-function for such version spaces, given demonstrations. Moreover, as we will show, two or more programs can be symbolically synchronized, resulting in the intersection of two, possibly infinite, version spaces. The framework thus allows for a rather flexible integration of PbD and PbI, and in addition establishes a new connection between two active research areas, enabling cross-fertilization.

While there is a plethora of mechanisms to ensure lawful access to privacy-protected data, additional research is required in order to reassure individuals that their personal data is being used for the purpose that they consented to. This is particularly important in the context of new data mining approaches, as used, for instance, in biomedical research and commercial data mining. We argue for the use of computational workflows to ensure and enforce appropriate use of sensitive personal data. Computational workflows describe in a declarative manner the data processing steps and the expected results of complex data analysis processes such as data mining (Gil et al. 2007b; Taylor et al. 2006). We see workflows as an artifact that captures, among other things, how data is being used and for what purpose. Existing frameworks for computational workflows need to be extended to incorporate privacy policies that can govern the use of data.

We define the robustness of a sequential plan as the probability that it will execute successfully despite uncertainty in the execution environment. We consider a rich notion of uncertainty over continuous domains that includes stochastic action effects, and changes to state variables due to unpredictable exogenous events. Given a characterization of this uncertainty in terms of probability distributions (e.g., Gaussian) our contributions are two-fold: First, we describe a novel approach to computing the robustness of a plan in the situation calculus, which (a) separates the projection problem from the problem of reasoning about probability, and (b) explicitly reveals the relevance and statistical independence of random variables and events (i.e., conditions that contain random variables). Then, building on this approach, we describe a forward search based planner that generates maximally robust plans, exploiting the revealed structure for speed-up. Preliminary empirical results demonstrate that our approach can realize exponential savings in both time and space compared to the classical sampling approach.