Exclamative clauses at the syntax-semantics interface

Exclamative clauses exhibit a structural diversity which raises the question of whether they form a clause type in the sense of Sadock & Zwicky (1985). Based on data from English, Italian, and Paduan, we argue that the class of exclamatives is syntactically characterizable in terms of a pair of abstract syntactic properties. Moreover, we propose that these properties encode two components of meaning which uniquely define the semantics and pragmatics of exclarnatives. Overall, our paper is a contribution to the study of the syntaxlsemantics interface and offers a new perspective on the notion of clause type

New Polynomial Classes for Logic-Based Abduction

We address the problem of propositional logic-based abduction, i.e., the problem of searching for a best explanation for a given propositional observation according to a given propositional knowledge base. We give a general algorithm, based on the notion of projection; then we study restrictions over the representations of the knowledge base and of the query, and find new polynomial classes of abduction problems

Probabilistic Knowledge-Based Programs

International audienceWe introduce Probabilistic Knowledge-Based Programs (PKBPs), a new, compact representation of policies for factored partially observable Markov decision processes. PKBPs use branching conditions such as if the probability of ϕ is larger than p, and many more. While similar in spirit to value-based policies, PKBPs leverage the factored representation for more compactness. They also cope with more general goals than standard state-based rewards, such as pure information-gathering goals. Compactness comes at the price of reactivity, since evaluating branching conditions on-line is not polynomial in general. In this sense, PKBPs are complementary to other representations. Our intended application is as a tool for experts to specify policies in a natural, compact language, then have them verified automatically. We study succinctness and the complexity of verification for PKBPs

Datives, Data and Dialect Syntax in American English

In this paper, we present a detailed case study of a number of dative constructions that vary across speakers of American English. We show how geographical maps of acceptability judgments can be used to shed light on the syntactic structures underlying those judgments. Those structures can then be used to refine our understanding of syntax more generally, in this case relating to the features of argument-introducing heads. We provide novel support for the low applicative analysis of the Personal Dative construction, on the grounds that this analysis falls in line with a general, somewhat surprising conclusion about Southern American English: that ApplP may occur not just as the complement of a verb, but also as the subject of a small clause or the complement of a preposition. We propose that this wider distribution follows from a featural difference between ApplP in Northern and Southern varieties: that low ApplP in Southern American English is not categorially distinct from ordinary DPs. We then show that even though Personal Datives have spread outside of the South, they have not taken this basic structure with them. Instead, Northern varieties adopting the Personal Dative have made a minimal modi cation to their existing Appl heads, to accommodate the Personal Dative without adopting the full range of dative constructions found in the South

Illusory Modification of Lightness in "Anomalous" Figures

Illusory modification of the enhanced lightness connected to the appearance of "anomalous" figures, with illusory contours, is investigated in Ebbinghaus like displays. This record was migrated from the OpenDepot repository service in June, 2017 before shutting down

Wood-Based Composites: Innovation towards a Sustainable Future

The term wood-based composite (WBC) is used to define a wide range of products in which wood is bonded together with other wooden or non-wooden materials [...

Knowledge-Based Programs as Plans: Succinctness and the Complexity of Plan Existence

Knowledge-based programs (KBPs) are high-level protocols describing the course of action an agent should perform as a function of its knowledge. The use of KBPs for expressing action policies in AI planning has been surprisingly overlooked. Given that to each KBP corresponds an equivalent plan and vice versa, KBPs are typically more succinct than standard plans, but imply more on-line computation time. Here we make this argument formal, and prove that there exists an exponential succinctness gap between knowledge-based programs and standard plans. Then we address the complexity of plan existence. Some results trivially follow from results already known from the literature on planning under incomplete knowledge, but many were unknown so far.Comment: 10 pages, Contributed talk at TARK 2013 (arXiv:1310.6382) http://www.tark.or

Génération de plans à base de connaissances

National audienceLes Knowledge-Based Programs (KBPs) associent représentation des connaissances et pla-nification. Il s'agit de protocoles décrivant les actions à effectuer par un agent, en fonction de son état de connaissance, afin d'atteindre un but donné. Ces plans possèdent une grande expressivité, grâce à l'uti-lisation de la logique modale S5, et une plus grande compacité que les plans classiques. La question de la génération de tels plans n'a été que peu étudiée. Notre objectif est de combler ce manque. Nous pro-posons des algorithmes permettant de générer des KBPs à partir de la spécification d'un état initial, d'un but et d'un ensemble d'actions disponibles. Deux types d'algorithmes sont présentés, par progression, c'est-à-dire en partant de l'état initial pour aller vers le but, et par régression, en partant cette fois-ci du but. Dans les deux cas, un algorithme de recherche en largeur est décrit, ayant la propriété de fournir un plan optimal en nombre d'actions à effectuer dans le pire cas. De même, nous exposons dans les deux cas des algorithmes de recherche en profondeur, dans lesquels une action est choisie pour chaque état de connaissance, par le biais de différentes fonctions heuristiques dont nous montrons certaines propriétés. Nous proposons également des benchmarks adaptés, pour lesquels la planification classique est moins précise, et testons nos algorithmes sur ces problèmes