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Register a new userMixed-Initiative Interaction = Mixed Computation
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Added by
Naren Ramakrishnan
Publication date
2001
fields
Informatics Engineering
and research's language is
English
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We show that partial evaluation can be usefully viewed as a programming model for realizing mixed-initiative functionality in interactive applications. Mixed-initiative interaction between two participants is one where the parties can take turns at any time to change and steer the flow of interaction. We concentrate on the facet of mixed-initiative referred to as `unsolicited reporting and demonstrate how out-of-turn interactions by users can be modeled by `jumping ahead to nested dialogs (via partial evaluation). Our approach permits the view of dialog management systems in terms of their native support for staging and simplifying interactions; we characterize three different voice-based interaction technologies using this viewpoint. In particular, we show that the built-in form interpretation algorithm (FIA) in the VoiceXML dialog management architecture is actually a (well disguised) combination of an interpreter and a partial evaluator.
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For mixed-initiative control between cyber-physical systems (CPS) and its users, it is still an open question how machines can safely hand over control to humans. In this work, we propose a concept to provide technological support that uses formal methods from AI -- description logic (DL) and automated planning -- to predict more reliably when a hand-over is necessary, and to increase the advance notice for handovers by planning ahead of runtime. We combine this with methods from human-computer interaction (HCI) and natural language generation (NLG) to develop solutions for safe and smooth handovers and provide an example autonomous driving scenario. A study design is proposed with the assessment of qualitative feedback, cognitive load and trust in automation.
Modeling complex systems is a time-consuming, difficult and fragmented task, often requiring the analyst to work with disparate data, a variety of models, and expert knowledge across a diverse set of domains. Applying a user-centered design process, we developed a mixed-initiative visual analytics approach, a subset of the Causemos platform, that allows analysts to rapidly assemble qualitative causal models of complex socio-natural systems. Our approach facilitates the construction, exploration, and curation of qualitative models bringing together data across disparate domains. Referencing a recent user evaluation, we demonstrate our approachs ability to interactively enrich user mental models and accelerate qualitative model building.
The ability to engage in mixed-initiative interaction is one of the core requirements for a conversational search system. How to achieve this is poorly understood. We propose a set of unsupervised metrics, termed ConversationShape, that highlights the role each of the conversation participants plays by comparing the distribution of vocabulary and utterance types. Using ConversationShape as a lens, we take a closer look at several conversational search datasets and compare them with other dialogue datasets to better understand the types of dialogue interaction they represent, either driven by the information seeker or the assistant. We discover that deviations from the ConversationShape of a human-human dialogue of the same type is predictive of the quality of a human-machine dialogue.
The original paper on Mixed Sessions introduce the side A of the tape: there is an encoding of classical sessions into mixed sessions. Here we present side B: there is a translation of (a subset of) mixed sessions into classical session types. We prove that the translation is a minimal encoding, according to the criteria put forward by Kouzapas, Perez, and Yoshida.
Proving only over source code that programs do not leak sensitive data leaves a gap between reasoning and reality that can only be filled by accounting for the behaviour of the compiler. Furthermore, software does not always have the luxury of limiting itself to single-threaded computation with resources statically dedicated to each user to ensure the confidentiality of their data. This results in mixed-sensitivity concurrent programs, which might reuse memory shared between their threads to hold data of different sensitivity levels at different times; for such programs, a compiler must preserve the value-dependent coordination of such mixed-sensitivity reuse despite the impact of concurrency. Here we demonstrate, using Isabelle/HOL, that it is feasible to verify that a compiler preserves noninterference, the strictest kind of confidentiality property, for mixed-sensitivity concurrent programs. First, we present notions of refinement that preserve a concurrent value-dependent notion of noninterference that we have designed to support such programs. As proving noninterference-preserving refinement can be considerably more complex than the standard refinements typically used to verify semantics -- preserving compilation, our notions include a decomposition principle that separates the semantics -- from the security-preservation concerns. Second, we demonstrate that these refinement notions are applicable to verified secure compilation, by exercising them on a single-pass compiler for mixed-sensitivity concurrent programs that synchronise using mutex locks, from a generic imperative language to a generic RISC-style assembly language. Finally, we execute our compiler on a nontrivial mixed-sensitivity concurrent program modelling a real-world use case, thus preserving its source-level noninterference properties down to an assembly-level model automatically. (See paper for complete abstract.)
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