In this paper, we present a novel approach NL2OCL to translate English specification of constraints to formal constraints such as OCL (Object Constraint language). In the used approach, input English constraints are syntactically and semantically analyzed to generate a SBVR (Semantics of Business Vocabulary and Rules) based logical representation that is finally mapped to OCL. During the syntactic and semantic analysis we have also addressed various syntactic and semantic ambiguities that make the presented approach robust. The presented approach is implemented in Java as a proof of concept. A case study has also been solved by using our tool to evaluate the accuracy of the presented approach. The results of evaluation are also compared to the pattern based approach to highlight the significance of the used approach.

Maritime threat detection is a challenging problem because maritime environments can involve a complex combination of concurrent vessel activities, and only a small fraction of these may be irregular, suspicious, or threatening. Previous work on this task has been limited to analyses of single vessels using simple rule-based models that alert watchstanders when a proximity threshold is breached. We claim that Probabilistic Graphical Models (PGMs) can be used to more effectively model complex maritime situations. In this paper, we study the performance of PGMs for detecting (small boat) maritime attacks. We describe three types of PGMs that vary in their representational expressiveness and evaluate them on a threat recognition task using track data obtained from force protection naval exercises involving unmanned sea surface vehicles. We found that the best-performing PGMs can outperform the deployed rule-based approach on these tasks, though some PGMs require substantial engineering and are computationally expensive.

Bill Swartout Introduced by Alan Kay at XEROX PARC in the 1970's, the desktop metaphor, which was later adopted in the Macintosh and Windows operating systems, has become the primary way we think about interacting with computers. Over the last decade, we have been developing sophisticated virtual humans at the USC Institute for Creative Technologies.

Chairs and committees of the FLAIRS-25 conference.

Officers of the Florida AI Research Society.

Special tracks are a vital part of the FLAIRS Thanks go to the authors of both accepted and rejected conferences, with 11 held at FLAIRS-25. Over 90 papers; the special track coordinator Chutima percent of the papers were reviewed by four or Boonthum-Denecke and all the special track organizers; more reviewers, and all papers were reviewed by at the program committees and their reviewers; least three. These were coordinated by the program the invited speakers; Chad Lane for organizing committees of the general conference and the special the conference; Jean Gerber for administering the tracks. The accepted submissions include 74 conference; the Florida Artificial International Research full papers (19 from the general conference and 55 Society for maintaining the conference series; from the special tracks), 27 short papers presented the Association for the Advancement of Artificial as posters (6 from the general conference and 21 Intelligence for its cooperation with the conference; from the special tracks), and 20 poster abstracts Mike Hamilton for organizing the publication that appear in these proceedings. of the proceedings; and EasyChair for hosting the review process. The program included five invited talks: Bill Swartout, the Director of Technology and Research Professor at the University of Southern California's

This work discusses the problem of sparse signal recovery when there is correlation among the values of non-zero entries. We examine intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector model, as well as their combination. Algorithms based on the sparse Bayesian learning are presented and the benefits of incorporating correlation at the algorithm level are discussed. The impact of correlation on the limits of support recovery is also discussed highlighting the different impact intra-vector and inter-vector correlations have on such limits.

The regularization path of the Lasso can be shown to be piecewise linear, making it possible to "follow" and explicitly compute the entire path. We analyze in this paper this popular strategy, and prove that its worst case complexity is exponential in the number of variables. We then oppose this pessimistic result to an (optimistic) approximate analysis: We show that an approximate path with at most O(1/sqrt(epsilon)) linear segments can always be obtained, where every point on the path is guaranteed to be optimal up to a relative epsilon-duality gap. We complete our theoretical analysis with a practical algorithm to compute these approximate paths.

Interpreting neural spike trains, compressing video, identifying features in DNA microarrays, and recognizing particles in high energy physics all rely upon the ability to find and model complex structure in a high dimensional space. Despite their great promise, high dimensional probabilistic models are frequently computationally intractable to work with in practice. In this thesis I develop solutions to overcome this intractability, primarily in the context of energy based models. A common cause of intractability is that model distributions cannot be analytically normalized. Probabilities can only be computed up to a constant, making training exceedingly difficult. To solve this problem I propose'minimum probability flow learning', a variational technique for parameter estimation in such models.

In this paper we describe COLIN, a forward-chaining heuristic search planner, capable of reasoning with COntinuous LINear numeric change, in addition to the full temporal semantics of PDDL. Through this work we make two advances to the state-of-the-art in terms of expressive reasoning capabilities of planners: the handling of continuous linear change, and the handling of duration-dependent effects in combination with duration inequalities, both of which require tightly coupled temporal and numeric reasoning during planning. COLIN combines FF-style forward chaining search, with the use of a Linear Program (LP) to check the consistency of the interacting temporal and numeric constraints at each state. The LP is used to compute bounds on the values of variables in each state, reducing the range of actions that need to be considered for application. In addition, we develop an extension of the Temporal Relaxed Planning Graph heuristic of CRIKEY3, to support reasoning directly with continuous change. We extend the range of task variables considered to be suitable candidates for specifying the gradient of the continuous numeric change effected by an action. Finally, we explore the potential for employing mixed integer programming as a tool for optimising the timestamps of the actions in the plan, once a solution has been found. To support this, we further contribute a selection of extended benchmark domains that include continuous numeric effects. We present results for COLIN that demonstrate its scalability on a range of benchmarks, and compare to existing state-of-the-art planners.