The proof-procedures to implement the operational models are the SCIFF [3] for interaction verification and CIFF [62] for temporal reasoning and planning, both of them extensions of Fung and Kowalski’s IFF abductive proof-procedure [78], and Gorgias [103] for preference reasoning and deliberation, based on LPwNF [50].
Sample text:```phi is a meta-program interpreted in [112, 24] as a second-order predicate which applies to the predicate p.```
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Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
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Sample text:```In addition, [5] and [6] executed the researches about XACML, access control language which can be managed in XML documents.```
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Sample text:```Following past work, we show that such basis models can be learned from 2D annotations using techniques adapted from nonrigid structure from motion (SFM) [20, 19].```
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Sample text:```have been proposed as potential alternatives to the Turing Test, specifically targeting its potential weaknesses and inadequacy [1].```
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Sample text:```The present observations support and extend earlier contentions that Parkinson patients prefer to adopt a closedloop control mode, adjusting their movements continuously on the basis of available visual information (Flowers 1976).```
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Sample text:```Note that in their pioneering paper Calvet and Fisher (2002) proposed the simplified version of the construction of Mannersalo et al (2002).```
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Sample text:```For example, Kawato et al. (1990) suggested that movement trajectories can be projected using critical via-points through which the trajectory has to pass at a certain time, rather than in terms of the moment-by-moment dynamics of the implemented movement trajectory. Optimal trajectories can then be learnt by applying local optimising principles for getting from one via-point to the next. In this way, impoverished predictions can be used to monitor rich implementations. In the same way, language users might predict particularly crucial aspects of an utterance, but the aspects that they predict will depend on the circumstances. More generally, Meyer & Hagoort question the value of predicting one’s own utterance. They argue that prediction is useful when it is likely to differ from the actual event. This is the case when predicting another person’s behaviour or when the result of one’s action is uncertain (e.g., moving in a strong wind). But they argue that people are confident about their own speech. Meyer & Hagoort admit that they will tend to be less confident in dialogue, and we agree. But more important, we argue that the behaviour of the production implementer is not fully determined by the production command, because the complex processes involved in production are subject to internal (“neural”) noise or priming (i.e., influences that may not be a result of the production command). Assuming that these sources of noise do not necessarily affect forward modelling as well, predicted speech may differ from actual speech. In addition, prediction is useful even if the behaviour is fully predictable, because it allows the actor to plan future behaviour on the basis of the prediction. In fact, we made such a proposal in relation to the order of heavy and light phrases (see sect. 3.1, target article). Hartsuiker claims that our account incorrectly predicts an early competitor effect in Huettig and Hartsuiker (2010). His claim is based on the assumption that Response/Pickering & Garrod: An integrated theory of language production and comprehension```
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Sample text:```Our results confi rm previous studies that reached the same conclusion (Boushey et al 1994; Vermeulen et al 2000; Till et al 2005; Marcucci et al 2005).```
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Sample text:```Following past work, we show that such basis models can be learned from 2D annotations using techniques adapted from nonrigid structure from motion (SFM) [20, 19].```
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Sample text:```…methods rely on some additional and often heuristic step to generate an initial solution, such as adversarially-trained neural networks [Conneau et al., 2018, Zhang et al., 2017a,b], robust self-learning [Artetxe et al., 2018b] or
5This can be easily verified by noting that the…```
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Sample text:```…with several action-based models of selective action that suggest turning one’s visual attention to an object or location in space may be the same process as preparing a movement to that object/location (e.g., Rizzolatti et al.1994; Tipper et al.1992; Welsh and Elliott 2004b; Welsh et al.1999).```
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Sample text:```5 concentrations were computed for each participant using a hierarchical spatiotemporal model fit with a pragmatic estimation procedure described elsewhere [30,31].```
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Sample text:```have been proposed as potential alternatives to the Turing Test, specifically targeting its potential weaknesses and inadequacy [1].```
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Sample text:```The present observations support and extend earlier contentions that Parkinson patients prefer to adopt a closedloop control mode, adjusting their movements continuously on the basis of available visual information (Flowers 1976).```
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Sample text:```…between output and input, CNF achieves almost 10% relative improvement over CRF. CNF also outperforms other methods including SVMpro (Hua and Sun, 2001), SVMpsi (Kim and Park, 2003), YASSPP (Karypis, 2006), PSIPRED (Jones, 1999), SPINE (Dor and Zhou, 2007) and TreeCRFpsi (Dietterich et al., 2004).```
Sample intent: background