WS98-10-027.pdf

AAAI Conferences

Objectives and Requirements Our objective is to design an agent architecture which will serve as a reference model for agent implementations. Additionally, we aim at devising an appropriate methodology for the implementation of agents that fit in the proposed agent architecture. The architecture should be general enough to support different kinds of agents: from purely reactive agents to deliberative utility-oriented agents mainly in the telecommunications domain. Examples of agent applications include: personal agents for mediating interaction between users and services, marketing agents for introducing new services to users, network management agents for monitoring network operation and taking corrective automated actions when network failure is detected, network resources allocation agents for ensuring that users get enough resources for the quality of service they require, pricing agents and various agents for different services available to users such as weather forecast, stock, etc. All the above agents will act in a complex and nondeterministic environment.


Product Regulatory Engineer - IoT BigData Jobs

#artificialintelligence

Job Description The successful candidate will own all aspects of regulatory compliance processes/practices programs (including driving continuous improvement) within Intel's Internet of Things (IoT) Group. Product Regulatory Engr will support growth through expanded footprint in key verticals such as transportation, industrial/energy, retail, home/buildings etc through development and implementation of product regulatory test plans to ensure that the system development platforms (or products) meet regulatory requirements for countries where the products/platforms will be shipped. Responsibilities include: supporting design teams on product safety, functional safety, connectivity (WiFi-BT, Zigbee, Cellular, RFID, NFC), and EMC related issues.; Minimum QualificationsBS in Electrical Engineering, Physics or related field.• 3 years with regulatory certifications in product safety, EMC and/or RF/wireless• 3 years Rf/ Wireless regulatory certifications – FCC, PTCRB, experience with any of the carriers – ATT, Verizon, Nokia, Siemens, etc• 3 years EMC regulatory certification – FCC, CE, CISPRPreferred qualifications: – MS or PhD in electrical engineering or physics or related field preferred.- Unrestricted right to work in the US without sponsorship- Global product regulatory knowledge; inclusive of wireless safety EMC- Experience with wireless RF test methods and equipment- Experience in the design of wireless/RF systems including antennas is highly desired.-


Davis

AAAI Conferences

In recent years, numerous methods to aid designers in conceptualizing new products have been developed. These methods intend to give structure to a process that was, at one time, considered to be a purely creative exercise. Resulting from the study, implementation, and refinement of design methodologies is the notion that both the structure of the development process and the structure of the developed product are key factors in creating value in a firm's product line. With respect to the latter key factor, product architecture, but more specifically, modular product architecture has been the subject of much study. This research is focused on two tasks: advancing the notion of a modular product architecture in which modules can be incorporated into a product'post-market,' and creating a method that aids designers leverage knowledge of natural symbiotic relationships to synthesize these post-market modules. It adds to prior work by first, defining the terms'derivative product' and'host product' to describe the post-market module and the product that the module augments, respectively. Second, by establishing three guidelines that are used to assess the validity of potential derivative products, giving the newly termed host and derivative product space defined boundaries. And lastly, by developing a 7-step, biomimetic-based methodology that can be used to create derivative product concepts (post-market modules). By using this methodology, the engineered products are designed on symbiotic principles found in nature.


Functional Ontology for Functional Understanding

AAAI Conferences

Recently, much attention has been paid on ontology aiming at a basis for modeling knowledge. It is an explicit specification of conceptualization (Gruber 1993) and provides primitive vocabulary for knowledge-based systems (Mizoguchi and Ikeda 1997). The importance explicit conceptualization for reusability of knowledge has been widely recognized (Abu-Hanna and Jansweijer 1994, Gruber 1993, Mars 1995, Mizoguchi and Ikeda 1997). This research is an attempt to establish a functional ontology which consists of functional concepts representing function of artifacts. In general, an ontology should satisfy the following requirements: Sophisticated articulation of the target object or world Explicit definition of concepts and relations among them Generality and Comprehensiveness Therefore, our goal here is to identify a finite number of meaningful and generic concepts representing functions of artifacts and then define each concept explicitly. A lot of research has been carried out on functional representation of artifacts (Chandrasekaran and Josephson 1996, de Kleer 1984, Lind 1994, Price and Pugh 1996, Sasajima et ai.


Constrained variable clustering and the best basis problem in functional data analysis

arXiv.org Machine Learning

Functional data analysis involves data described by regular functions rather than by a finite number of real valued variables. While some robust data analysis methods can be applied directly to the very high dimensional vectors obtained from a fine grid sampling of functional data, all methods benefit from a prior simplification of the functions that reduces the redundancy induced by the regularity. In this paper we propose to use a clustering approach that targets variables rather than individual to design a piecewise constant representation of a set of functions. The contiguity constraint induced by the functional nature of the variables allows a polynomial complexity algorithm to give the optimal solution.