An intelligent system of the future should make its user feel comfortable, which is impossible without understanding context they coexist in. However, our past research did not treat language information as a part of the context a robot works in, and data about reasons why the user had made his decisions was not obtained. Therefore, we decided to utilize the Web as a knowledge source to discover context information that could suggest a robot's behavior when it acquires verbal information from its user or users. By comparing user utterances (blogs, Twitter or Facebook entries, not direct orders) with other people's written experiences (mostly blogs), a system can judge whether it is a situation in which the robot can perform or improve its performance. In this paper we introduce several methods that can be applied to a simple floor-cleaning robot. We describe basic experiments showing that text processing is helpful when dealing with multiple users who are not willing to give rich feedback. For example, we describe a method for finding usual reasons for cleaning on the Web by using Okapi BM25 to extract feature words from sentences retrieved by the query word "cleaning". Then, we introduce our ideas for dealing with conflicts of interest in multiuser environments and possible methods for avoiding such conflicts by achieving better situation understanding. Also, an emotion recognizer for guessing user needs and moods and a method to calculate situation naturalness are described.

Documents that people write to communicate with other people are rarely as precise as a formal logic. Yet people can read those documents and relate them to formal notations for science, mathematics, and computer programming. They can derive whatever information they need, reason about it, and apply it at an appropriate level of precision. Unlike theorem provers, people rely on analogies for their reasoning. Even mathematicians use analogies to discover their theorems and formal proofs to verify and codify their discoveries. This article shows how a high-speed analogy engine is used to analyze natural language texts and relate the results to both structured and unstructured representations. The degree of precision in the results depends more on the precision in the knowledge sources used to analyze the documents than on the precision of the language in the documents themselves.

Spoken language is an important and natural way for people to communicate with computers. Nonetheless, habitable, reliable, and efficient human-machine dialogue remains difficult to achieve. This paper describes a multi-threaded semi-synchronous architecture for spoken dialogue systems. The focus here is on its utterance interpretation module. Unlike most architectures for spoken dialogue systems, this new one is designed to be robust to noisy speech recognition through earlier reliance on context, a mixture of rationales for interpretation, and fine-grained use of confidence measures. We report here on a pilot study that demonstrates its robust understanding of users’ objectives, and we compare it with our earlier spoken dialogue system implemented in a traditional pipeline architecture. Substantial improvements appear at all tested levels of recognizer performance.

We examine the performance of Total Variation (TV) smoothing for processing of noisy Electrocardiogram (ECG) recorded by an ambulatory device. The TV smoothing is compared with traditionally-used band-pass filtering using ECG with artificially added noise, as well as with real-world noise obtained during physiological monitoring. The fundamental difference between TV smoothing and traditional band-pass filtering is that TV smoothing allow preserving sharp slopes in the ECG, while traditional smoothing dampens them. Since the QRS complex represents a feature with steep slopes, the TV smoothing is a better choice ECG filtering. We found that TV smoothing outperforms traditional filtering on ECG signals recorded under different conditions and can be used as effective filtering tool in popular QRS detection algorithms.

Sustainable design is as an important movement in design. Biologically inspired design is a major paradigm for sustainable design. In this paper, we analyze a corpus of biologically inspired design projects in terms of sustainability. We then describe a case study of analogical design of a fog harvesting net, and abstract from it the patterns of Hydrophobia and Hydrophilia. We indicate how these two function-mechanism design patterns occur in several design projects in our corpus. This analysis indicates how biologically inspired sustainable design can be analyzed in terms of cross-domain analogical transfer of design patterns.

Buildings designed around occupant A sustainable home is more than a green building: it is also intelligence will provide flexible, adaptive task a living experience that encourages occupants to use fewer environments, refined control zones and technologies that resources more effectively. Research has shown that small maximize occupants' access to adaptive opportunities changes in behaviour in how we use our homes, such as (Cole & Brown, 2009). Architects, engineers and system turning off lights, reducing heat and uncovering or designers are faced with the challenge of reframing design covering windows, or shortening showers, can result in strategies as a co-evolution of human and building substantial energy and water savings. But changing the intelligence that will encourage as well as underpin way we use resources is proving challenging.

In this paper, we present a novel approach of translating natural languages specification to SBVR business rules. The business rules constraint business structure or control behaviour of a business process. In modern business modelling, one of the important phases is writing business rules. Typically, a business rule analyst has to manually write hundreds of business rules in a natural language (NL) and then manually translate NL specification of all the rules in a particular rule language such as SBVR, or OCL, as required. However, the manual translation of NL rule specification to formal representation as SBVR rule is not only difficult, complex and time consuming but also can result in erroneous business rules. In this paper, we propose an automated approach that automatically translates the NL (such as English) specification of business rules to SBVR (Semantic Business Vocabulary and Rules) rules. The major challenge in NL to SBVR translation was complex semantic analysis of English language. We have used a rule based algorithm for robust semantic analysis of English and generate SBVR rules. Automated generation of SBVR based Business rules can help in improved and efficient constrained business aspects in a typical business modelling.

The challenges of effective health risk communication are well known. This paper provides pointers to the health communication literature that discuss these problems. Tailoring printed information, visual displays, and interactive multimedia have been proposed in the health communication literature as promising approaches. On-line risk communication applications are increasing on the internet. However, potential effectiveness of applications using conventional computer technology is limited. We propose that use of artificial intelligence, building upon research in Intelligent Tutoring Systems, might be able to overcome these limitations.

It is now time to begin the study of personal genome services based on the interdisciplinary theories and technologies of genomics and artificial intelligence (AI). Although recently much attention has been given to personal genome services for realizing personal medicine, little systematic research has been done on their communication and computational aspects for intelligent wellness service in AI communities. We believe that the intelligent personal genome services of the future need to include an understanding of how the knowledge of genetic risk influences people's behavior. This paper proposes the concept of MyFinder, a new framework for realizing an intimate personal genome service with AI technologies. This paper also describes the grand challenge problems of personal genome services that the AI and genomics communities should tackle jointly.

This article presents our work on building a virtual coach agent, called Dr. Vicky, and training environment (called the Virtual BNI Trainer, or VBT) for learning how to correctly talk with medical patients who have substance abuse issues. This work focuses on how to effectively design menu-based dialogue interactions for conversing with a virtual patient within the context of learning how to properly engage in such conversations according to the brief negotiated interview techniques we desire to train. Dr. Vicky also employs a model of student knowledge to influence the mediation strategies used in personalizing the training experience and guidance offered. The VBT is a prototype training application that will be used by medical students and practitioners within the Yale medical community in the future.