bluuebunny/crossref_metadata_2025_split huggingface dataset:
LES STRUCTURES COOPÉRATIVES SÉNÉGALAISES FACE AUX MUTATIONS INSTITUTIONNELLES
Authors: GAYE Matar 
Published: April 2007

Le cadre institutionnel dans lequel Bvoluent les structures co‐operatives sbnkgalaises a connu de profondes mutations au cows de ces dernihres annees. La reforme du systeme s'est traduite par la mise en place de nouvelles entites qui devront fonctionner de plus en plus comme des entreprises privbes. Toutefois, le niveau actuel des resources humaines, financiiires et materielles ne leur permet pas encore d'asseoir une reelle autonomie.La presence de nouveaux concurrents encourages par les pouvoirs publics suscite quelques inquietudes chez les dirigeants. Malgre un penchant relativement favorable aux coopbratives, la plupart des adherents simples ne souhaitent pas l'eviction des commerFants prives. Les attitudes des uns e t des autres par rapport a divers aspects de la politique oficielle varient selon leur position dans le systhme.Avec I'intervention de la Caisse Nationale de Credit Agricole du Senegal (CNCAS) qui vient de s'implanter, le principe des apports personnels tend B marginaliser les plus nkcessiteux. Par ailleurs, le contenu initialement donne au concept fon damental de responsabilisation se trouve profondement modifie.
Recovering from execution errors in SIPE
Authors: Wilkins David E. 
Published: April 2007

In real‐world domains (e.g., a mobile robot environment), things do not always proceed as planned, so it is important to develop better execution‐monitoring techniques and replanning capabilities. This paper describes these capabilities in the SIPE (System for Interactive Planning and Execution Monitoring) planning system. The motivation behind SIPE is to place enough limitations on the representation so that planning can be done efficiently, while retaining sufficient power to still be useful. This work assumes that new information given to the execution monitor is in the form of predicates, thus avoiding the difficult problem of how to generate these predicates from information provided by sensors.The replanning module presented here takes advantage of the rich structure of SIPE plans and is intimately connected with the planner, which can be called as a subroutine. This allows the use of SIPE's capabilities to determine efficiently how unexpected events affect the plan being executed and, in many cases, to retain most of the original plan by making changes in it to avoid problems caused by these unexpected events. SIPE is also capable of shortening the original plan when serendipitous events occur. A general set of replanning actions is presented along with a general replanning capability that has been implemented by using these actions.
Defeat among arguments: a system of defeasible inference
Authors: Loui R. P. 
Published: April 2007

This paper presents a system of nonmonotonic reasoning with defeasible rules. The advantage of such a system is that many multiple extension problems can be solved without additional explicit knowledge; ordering competing extensions can be done in a natural and defeasible way, via syntactic considerations. The objectives closely resemble Poole's objectives.But the logic is different from Poole's. The most important difference is that this system allows the kind of chaining that many other nonmonotonic systems allow. Also, the form in which the inference system is presented is quite unusual. It mimics an established system of inductive logic, and it treats defeat in the way of the epistemologist‐philosophers.The contributions are both of content and of form: (content) the kinds of defeat that are considered, and (form) the way in which defeat is treated in the rules of inference.
Comments on Peter Cheeseman's An inquiry into computer understanding
Authors: Aleliunas Romas 
Published: April 2007

AbstractI agree that probability in some, possibly disguised, form is a necessary component of practical inference. Our goal, therefore, must be to find how to make probability practical. Normatively motivated thinking (which includes textbook Bayesianism) is, however, mute on two topics that are crucial to practicality: the choice of a limited but useful set of initial hypotheses and the choice of decision rules. The best way of reducing the computational demands of probability is to use the smallest and simplest set of hypotheses, and the crudest decision rules that are compatible with your application's goals. Learning how to make these choices is an empirical, not a normative, endeavour.
Localized event‐based reasoning for multiagent domain1
Authors: Lansky Amy L. 
Published: April 2007

This paper presents the GEM concurrency model and GEMPLAN, a multiagent planner based on this model. Unlike standard state‐based AI representations, GEM is unique in its explicit emphasis on events and domain structure. In particular, a world domain is modeled as a set of regions composed of interrelated events. Event‐based temporal‐logic constraints are then associated with each region to delimit legal domain behavior. The GEMPLAN planner directly reflects this emphasis on domain structure and constraints. It can be viewed as a general‐purpose constraint satisfaction facility which constructs a network of interrelated events (a “plan”) that is subdivided into regions (“subplans”), satisfies all applicable regional constraints, and also achieves some stated goal.GEMPLAN extends and generalizes previous planning architectures in the range of constraint forms it handles and in the flexibility of its constraint satisfaction search strategy. One critical aspect of our work has been an emphasis on localized reasoning—techniques that make explicit use of domain structure. For example, GEM localizes the applicability of domain constraints and imposes additional “locality constraints” on the basis of domain structure. Together, constraint localization and locality constraints provide semantic information that can be used to alleviate several aspects of the frame problem for multiagent domains. The GEMPLAN planner reflects the use of locality by subdividing its constraint satisfaction search space into regional planning search spaces. Utilizing constraint and property localization, GEMPLAN can pinpoint and rectify interactions among these regional search spaces, thus reducing the burden of “interaction analysis” ubiquitous to most planning systems. Because GEMPLAN is specifically geared towards parallel, multiagent domains, we believe that its natural application areas will include scheduling and other forms of organizational coordination.
Constraint satisfaction algorithms1
Authors: Nadel Bernard A. 
Published: April 2007

AbstractConstraint satisfaction problems are ubiquitous in artificial intelligence and many algorithms have been developed for their solution. This paper provides a unified survey of some of these, in terms of three classes: (i) tree search, (ii) arc consistency (AC), and (iii) hybrid tree search/arc consistency algorithms. It is shown that several important algorithms, when slightly rearranged, are of the latter hybrid form, but with arc consistency components that do not necessarily achieve full arc consistency at the tree nodes. Accordingly, we define several newpartialAC procedures, AC1/5, AC1/4, AC1/3, and AC½, analogous to the well‐knownfullAC algorithms which Mackworth has called AC1, AC2, and AC3. The fractional suffixes on our AC algorithms are roughly proportional to the degree of partial arc consistency they achieve. Unlike traditional versions, our AC algorithms (full and partial) are presented in a parameterized form to allow them to be embedded efficiently at the nodes of a tree search process. Algorithm complexities are compared empirically, using then‐queens problem and a new version called confusedn‐queens. Gaschnig's Backmarking (a tree search algorithm) and Haralick's Forward Checking (a hybrid algorithm) are found to be the most efficient. For the hybrid algorithms, we find that it pays to do little arc consistency processing at the nodes, incurring more nodes, but sufficiently reducing the work per node so as to obtain less work over the whole tree. The unified view taken here suggests several new algorithms. Preliminary results show one of these to be the best algorithm so far.
Using crucial literals to select better theories
Authors: SATTAR ABDUL , GOEBEL RandY 
Published: April 2007

When Horn clause theories are combined with integrity constraints to produce potentially refutable theories, Seki and Takeuchi have shown how crucial literals can be used to discriminate two mutually incompatible theories. A literal is crucial with respect to two theories if only one of the two theories supports the derivation of that literal. In other words, actually determining the truth value of the crucial literal will refute one of the two incompatible theories.This paper presents an integration of the idea of crucial literal with Theorist, a logic‐based system for hypothetical reasoning. Theorist is a goal‐directed nonmonotonic reasoning system that classifies logical formulas as possible hypotheses, facts, and observations. As Theorist uses full clausal logic, it does not require Seki and Takeuchi's notion of integrity constraint to define refutable theories. In attempting to deduce observation sentences, Theorist identifies instances of possible hypotheses as nomological explanations: consistent sets of hypothesis instances required to deduce observations. As multiple and mutually incompatible explanations are possible, the notion of crucial literal provides the basis for proposing experiments that distinguish competing explanations.We attempt to make three contributions. First, we adapt Seki and Takeuchi's method for Theorist. To do so, we incrementally use crucial literals as experiments, whose results are used to reduce the total number of explanations generated for a given set of observations. Next, we specify an extension which incrementally constructs a table of all possible crucial literals for any pair of theories. This extension is more efficient and provides the user with greater opportunity to conduct experiments to eliminate falsifiable theories. A prototype is implemented in CProlog, and several examples of diagnosis are considered to show its empirical efficiency. Finally, we point out that assumption‐based truth maintenance systems (ATMS), as used in the multiple fault diagnosis system of de Kleer and Williams, are interesting special cases of this more general method of distinguishing explanatory theories.
Logic, probability theory, and artificial intelligence ‐ Part I: the probabilistic foundations of logic
Authors: MORGAN CHARLES G. 
Published: April 2007

Many AI researchers have come to be dissatisfied with approaches to their discipline based on formal logic. Various alternatives are often suggested, including probability theory. This paper investigates the intimate connection between probability theory and various logics. We show that probability theory, broadly conceived, may be used as a formal semantics for virtually any monotonic logic. Thus, rather than being seen as competing, it is more appropriate to view formal logics as very special cases of probability theory, usually special cases that are computationally more tractable than the more general theory. Thus, probability theory and logic should be seen as complementary. Viewing probability theory in this abstract way may help to shed light on various recalcitrant problems in AI.De nombreux chercheurs dans le domaine de l'intelligence artificielle manifestent une certaine insatisfaction vis‐à‐vis certaines approches basées sur la logique formelle. Diverses solutions sont souvent proposées, y compris la théorie des probabilityés. Cet article analyse la relation intime entre la théorie des probabilités et diverses logiques. Il est démontré que la théorie des probabilityés, conçue de manière générale, peut ětre utilisée comme une sémantique formelle pour presque toute logique monotonique. Au lieu de percevoir les logiques formelles comme étant en opposition, il est plus approprié de les considérer comme des cas trés spéciaux de la théorie des probabilityés, habituellement plus traitables au niveau calcul que la théorie plus générate. Par conséquent, la théorie des probabilityés et la logique doivent ětre percues comme des éléments complémentaires. Le fait de considérer la théorie des probabilityés d'une manière abstraite peut contribuer à la compréhension de divers problèmes ardus dans le domaine de l'intelligence artificielle.
A spectrum of logical definitions of model‐based diagnosis1
Authors: CONSOLE LUCA , TORASSO PIETRO 
Published: April 2007

In this paper, we analyze the logical definitions of model‐based diagnosis recently presented in the literature, and we propose a unified framework (based on the integration of abductive and consistency‐based reasoning) in which most of such definitions can be captured. This allows us to single out the existence of a spectrum of alternatives in the logical definition of diagnosis. A lot of attention in the paper is devoted to analyzing the differences among the definitions in the spectrum. In particular, we show that the definitions can be compared on the basis of their restrictive‐ness and we relate such a restrictiveness with the completeness of the model of the system to be diagnosed.Dans cet article, les auteurs analysent les définitions logiques de diagnostics basés sur un modèle dont il a été question récemment dans certains ouvrages. Ils proposent un cadre unifié (basé sur l'intégration du raisonnement abductif et du raisonnement basé sur la consistance) à l'interieur duquel la plupart de ces définitions peuvent ětre regroupées. Cette particularité permet de mettre en lumière l'existence d'un spectre d'alternatives dans la définition logique du diagnostic. Cet article accorde une attention toute particulière à l'analyse des différences entre les définitions du spectre. En outre, les auteurs démontrent que les définitions peuvent ětre compareées en fonction de leur caractère restrictif, qui est ensuite mis en relation avec la complétude du modèle du système faisant l'objet d'un diagnostic.
Capturing high‐level structure of naturally occurring, extended explanations using bottom‐up strategies
Authors: Mooney David J. , Carberry Sandra , McCoy Kathleen F. 
Published: April 2007

This paper notes some structural features of naturally occurring, extended explanations and argues that current generation methodologies are inadequate for determining high‐level structure. We then present a computational model for generating high‐level structure, which is based on the hypothesis that high‐level structure is distinct from text structure at lower levels and is determined by the material to be conveyed rather than by independent, recursive text structuring devices. We propose that high‐level structure can be determined by bottom‐up processes that attempt to satisfy speaker, listener, and compositional goals, after which top‐down strategies can be used to organize the material about the selected framework.Cet article relève certaines caractéristiques structurales ?explications étendues à occurrence naturelle et soutient que les méthodes de génération courantes ne permettent pas de déterminer adéquatement une stratégie de haut niveau. Les auteurs présentent ensuite un modèle computationnel de géneration de structures de haut niveau qui est basé sur ľhypothèse qu'une structure de haut niveau est différente ?une structure de texte aux nivaux inféiieurs, et qu'elle est déterminée par les données qui doivent ětre transmises plut&#x006f;&#x030c;t que par des mécanismes indépendants et récursifs de structure de texte. lis soutiennent qu'une structure de haut niveau peut ětre déterminée àľaide ?'un processus ascendant qui essaie de rencontrer les objectifs du locuteur, de ľauditeur et de composition, et que des stratégies descendantes peuvent ensuite ětre utilisées pour organiser les données a propos du cadre choisi.
CONCEPTUAL SCAFFOLDING: A SPATIALLY FOUNDED MEANING REPRESENTATION FOR METAPHOR COMPREHENSION
Authors: Veale Tony , Keane Mark T. 
Published: April 2007

AbstractOnce viewed as a rhetorical and superficial language phenomenon, metaphor is now recognized to serve a fundamental role in our conceptual structuring and language comprehension processes. In particular, it is argued that certain experiential metaphors based upon intuitions of spatial relations are inherent in the conceptual organization of our most abstract thoughts. In this paper we present a two‐stage computational model of metaphor interpretation which employs a spatially founded semantics to broadly characterize the meaning carried by a metaphor in terms of a conceptual scaffolding, an interim meaning structure around which a fuller interpretation is fleshed out over time. We then present a semantics for the construction of conceptual scaffolding which is based upon core metaphors of collocation, containment and orientation. The goal of this scaffolding is to maintain the intended association of ideas even in contexts in which system knowledge is insufficient for a complete interpretation. This two‐stage system of scaffolding and elaboration also models the common time lapse between initial metaphor comprehension and full metaphor appreciation. Several mechanisms for deriving elaborative inference from scaffolding structures, particularly in cases of novel or creative metaphor, are also presented. While the system developed in this paper has significant practical application, it also demonstrates that core spatial metaphors clearly play a central role in metaphor comprehension.
MERGING SEPARATELY GENERATED PLANS WITH RESTRICTED INTERACTIONS
Authors: Yang Qiang , Nau Dana S. , Hendler James 
Published: April 2007

Generating action sequences to achieve a set of goals is a computationally difficult task. When multiple goals are present, the problem is even worse. Although many solutions to this problem have been discussed in the literature, practical solutions focus on the use of restricted mechanisms for planning or the application of domain dependent heuristics for providing rapid solutions (i.e., domain‐dependent planning). One previously proposed technique for handling multiple goals efficiently is to design a planner or even a set of planners (usually domain‐dependent) that can be used to generate separate plans for each goal. The outputs are typically either restricted to be independent and then concatenated into a single global plan, or else they are merged together using complex heuristic techniques. In this paper we explore a set of limitations, less restrictive than the assumption of independence, that still allow for the efficient merging of separate plans using straightforward algorithmic techniques.In particular, we demonstrate that for cases where separate plans can be individually generated, we can define a set of limitations on the allowable interactions between goals that allow efficient plan merging to occur. We propose a set of restrictions that are satisfied across a significant class of planning domains. We present algorithms that are efficient for special cases of multiple plan merging, propose a heuristic search algorithm that performs well in a more general case (where alternative partially ordered plans have been generated for each goal), and describe an empirical study that demonstrates the efficiency of this search algorithm.
A SEMANTIC HEAD‐DRIVEN GENERATION SYSTEM FOR FEATURE STRUCTURE TREE‐ADJOINING GRAMMARS
Authors: Kikui Gen‐ichiro 
Published: April 2007

This paper describes a natural language generation system developed for a spoken language translation system. Our system employs Feature‐structure‐based Tree Adjoining Grammar (FTAG) for generation knowledge representation. Each elementary tree of our grammar is paired with a semantic feature structure which is consistent with semantics defined in Head‐driven Phrase Structure Grammar. Feature structures attached to nodes of elementary trees are unrestricted. Thus our formalism allows HPSG style phrase structure description as well as TAG style description. The advantage of our generation knowledge representation is the ability of incorporating HPSG style “core” grammar and TAG style case‐based grammar.The system generates a syntactic tree by combining elementary trees so as to satisfy an input semantic structure. The generation algorithm is an application of a semantic head‐driven generation. To carry out an adjoining operation. an elementary tree with an adjunction node is dynamically split at the adjunction node during generation.
KNOWLEDGE‐BASED FEATURE DISCOVERY FOR EVALUATION FUNCTIONS
Authors: Fawcett Tom E. 
Published: April 2007

Since Samuel's work on checkers over thirty years ago, much effort has been devoted to learning evaluation functions. However, all such methods are sensitive to the feature set chosen to represent the examples. If the features do not capture aspects of the examples significant for problem solving, the learned evaluation function may be inaccurate or inconsistent. Typically, good feature sets are carefully handcrafted and a great deal of time and effort goes into refining and tuning them. This paper presents an automatic knowledge‐based method for generating features for evaluation functions. The feature set is developed iteratively: features are generated, then evaluated, and this information is used to develop new features in turn. Both the contribution of a feature and its computational expense are considered in determining whether and how to develop it further.This method has been applied to two problem‐solving domains: the Othello board game and the domain of telecommunications network management. Empirical results show that the method is able to generate many known features and several novel features and to improve concept accuracy in both domains.
A PLANNING APPROACH TO DECLARER PLAY IN CONTRACT BRIDGE
Authors: Smith S. J. J. , Nau D. S. , Throop T. A. 
Published: April 2007

Although game‐tree search works well in perfect‐information games, it is less suitable for imperfect‐information games such as contract bridge. The lack of knowledge about the opponents’ possible moves gives the game tree a very large branching factor, making it impossible to search a significant portion of this tree in a reasonable amount of time.This paper describes our approach for overcoming this problem. We represent information about bridge in a task network extended to represent multi‐agency and uncertainty. Our game‐playing procedure uses this task network to generate game trees in which the set of alternative choices is determined not by the set of possible actions, but by the set of available tactical and strategic schemes.We have tested this approach on declarer play in the game of bridge, in an implementation called Tignum 2. On 5000 randomly generated notrump deals, Tignum 2 beat the strongest commercially available program by 1394 to 1302, with 2304 ties. These results are statistically significant at the α= 0.05 level. Tignum 2 searched an average of only 8745.6 moves per deal in an average time of only 27.5 seconds per deal on a Sun SPARCstation 10. Further enhancements to Tignum 2 are currently underway.