Non-linear continuous change is common in real-world problems, especially those that model physical systems. We present an algorithm which builds upon existent temporal planning techniques based on linear programming to approximate non-linear continuous monotonic functions. These are integrated through a semantic attachment mechanism, allowing external libraries or functions that are difficult to model in native PDDL to be evaluated during the planning process. A new planning system implementing this algorithm was developed and evaluated. Results show that the addition of this algorithm to the planning process can enable it to solve a broader set of planning problems.

In the contemporary autonomous systems the role of complex interactions such as (possibly relaxed) dialogues is increasing significantly. In this paper we provide a paraconsistent and paracomplete implementation of inquiry dialogue under realistic assumptions regarding availability and quality of information. Various strategies for dealing with unsure and inconsistent information are analyzed. The corresponding dialogue outcomes are further evaluated against the (paraconsistent and paracomplete) distributed beliefs of the group. A specific 4-valued logic underpins the presented framework. Thanks to the qualities of the implementation tool: a rule-based query language 4QL, our solution is both expressive and tractable.

While a variety of persuasion agents have been created and applied in different domains such as marketing, military training and health industry, there is a lack of a model which can provide a unified framework for different persuasion strategies. Specifically, persuasion is not adaptable to the individuals' personal states in different situations. Grounded in the Elaboration Likelihood Model (ELM), this paper presents a computational model called Model for Adaptive Persuasion (MAP) for virtual agents. MAP is a semi-connected network model which enables an agent to adapt its persuasion strategies through feedback. We have implemented and evaluated a MAP-based virtual nurse agent who takes care and recommends healthy lifestyle habits to the elderly. Our experimental results show that the MAP-based agent is able to change the others' attitudes and behaviors intentionally, interpret individual differences between users, and adapt to user's behavior for effective persuasion.

A popular technique for the analysis of web query languages relies on the translation of queries into logical formulas. These formulas are then solved for satisfiability using an off-the-shelf satisfiability solver. A critical aspect in this approach is the size of the obtained logical formula, since it constitutes a factor that affects the combined complexity of the global approach. We present logical combinators whose benefit is to provide an exponential gain in succinctness in terms of the size of the logical representation. This opens the way for solving a wide range of problems such as satisfiability and containment for expressive query languages in exponential-time, even though their direct formulation into the underlying logic results in an exponential blowup of the formula size, yielding an incorrectly presumed two-exponential time complexity. We illustrate this from a practical point of view on a few examples such as numerical occurrence constraints and tree frontier properties which are concrete problems found with semi-structured data.

Argumentation is an inherently dynamic process. Given that argumentation can be viewed as a process as well Consequently, recent years have witnessed tremendous as a product, recent years have seen an increasing number of research efforts towards an understanding of studies on different problems in the dynamics of argumentation how the seminal AGM theory of belief change can frameworks [Baumann, 2012; Bisquert et al., 2011; 2013; be applied to argumentation, in particular for Dung's Boella et al., 2009; Booth et al., 2013; Cayrol et al., 2010; abstract argumentation frameworks (AFs). However, Doutre et al., 2014; Kontarinis et al., 2013; Krümpelmann et none of the attempts has yet succeeded in handling al., 2012; Nouioua and Würbel, 2014; Sakama, 2014]. The the natural situation where the revision of an AF is problem we tackle here is how to revise an AF when some new guaranteed to be representable by an AF as well.

We consider the scenario of ontology-based query answering. It is generally accepted that true scalability in this setting can only be achieved via query rewriting, which in turn allows for the exploitation of standard RDBMSs. In this work, we close two open fundamental questions related to query rewriting. We establish that linear existential rules are polynomially combined rewritable, while full linear rules are polynomially (purely) rewritable; in both cases, the target query language consists of first-order or non-recursive Datalog queries. An immediate consequence of our results is that DLR-Lite_R, the extension of DL-Lite_R with n-ary roles, is polynomially combined rewritable.

Craig Knoblock (University of Southern California, USA) Hiroaki Kitano (Sony Computer Science Laboratories, Inc., Japan) Sebastian run (Stanford University, USA) Raj Reddy (Carnegie Mellon University, USA) Ramasamy Uthurusamy (General Motors Corporation, retired) Erik Sandewall (Linköping Universit...

Hashing, as a popular approximate nearest neighbor search, has been widely used for large-scale similarity search. Recently, a spectrum of machine learning methods are utilized to learn similarity-preserving binary codes. However, most of them directly encode the explicit features, keywords, which fail to preserve the accurate semantic similarities in binary code beyond keyword matching, especially on short texts. Here we propose a novel text hashing framework with convolutional neural networks. In particular, we first embed the keyword features into compact binary code with a locality preserving constraint. Meanwhile word features and position features are together fed into a convolutional network to learn the implicit features which are further incorporated with the explicit features to fit the pre-trained binary code. Such base method can be successfully accomplished without any external tags/labels, and other three model variations are designed to integrate tags/labels. Experimental results show the superiority of our proposed approach over several state-of-the-art hashing methods when tested on one short text dataset as well as one normal text dataset.

Hashing is one of the effective techniques for fast Approximate Nearest Neighbour (ANN) search. Traditional single-bit quantization (SBQ) in most hashing methods incurs lots of quantization error which seriously degrades the search performance. To address the limitation of SBQ, researchers have proposed promising multi-bit quantization (MBQ) methods to quantize each projection dimension with multiple bits. However, some MBQ methods need to adopt specific distance for binary code matching instead of the original Hamming distance, which would significantly decrease the retrieval speed. Two typical MBQ methods Hierarchical Quantization and Double Bit Quantization retain the Hamming distance, but both of them only consider the projection dimensions during quantization, ignoring the neighborhood structure of raw data inherent in Euclidean space. In this paper, we propose a multi-bit quantization method named Hamming Compatible Quantization (HCQ) to preserve the capability of similarity metric between Euclidean space and Hamming space by utilizing the neighborhood structure of raw data. Extensive experiment results have shown our approach significantly improves the performance of various state-of-the-art hashing methods while maintaining fast retrieval speed.

The sentential decision diagram (SDD) has been recently proposed as a new  tractable representation of Boolean functions that generalizes the influential  ordered binary decision diagram (OBDD).  Empirically, compiling CNFs into SDDs  has yielded significant improvements in both time and space over compiling  them into OBDDs, using a bottom-up compilation approach. In this work, we present a top-down CNF to SDD compiler that is based on techniques  from the SAT literature. We compare the presented compiler empirically to the state-of-the-art, bottom-up SDD compiler, showing orders-of-magnitude improvements in compilation time.