Collaborating Authors

Finding the Odd-One-Out in Fleets of Mechatronic Systems using Embedded Intelligent Agents

AAAI Conferences

With the introduction of low-cost wireless communication many new applications have been made possible; applications where systems can collaboratively learn and get wiser without human supervision. One potential application is automated monitoring for fault isolation in mobile mechatronic systems such as commercial vehicles. The paper proposes an agent design that is based on uploading software agents to a fleet of mechatronic systems. Each agent searches for interesting state representations of a system and reports them to a central server application. The states from the fleet of systems can then be used to form a consensus from which it can be possible to detect deviations and even locating a fault.

A Unified Implicit Dialog Framework for Conversational Commerce

AAAI Conferences

We propose a unified Implicit Dialog framework for goal-oriented, information seeking tasks of Conversational Commerce applications. It aims to enable the dialog interactions with domain data without replying on the explicitly encoded rules but utilizing the underlying data representation to build the components required for the interactions, which we refer as Implicit Dialog in this work. The proposed framework consists of a pipeline of End-to-End trainable modules. It generates a centralized knowledge representation to semantically ground multiple sub-modules. The framework is also integrated with an associated set of tools to gather end users' input for continuous improvement of the system. This framework is designed to facilitate fast development of conversational systems by identifying the components and the data that can be adapted and reused across many end-user applications. We demonstrate our approach by creating conversational agents for several independent domains.

Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-level Artificial Intelligence

Microservice-based architectures enable different aspects of web applications to be created and updated independently, even after deployment. Associated technologies such as service mesh provide application-level fault resilience through attribute configurations that govern the behavior of request-response service -- and the interactions among them -- in the presence of failures. While this provides tremendous flexibility, the configured values of these attributes -- and the relationships among them -- can significantly affect the performance and fault resilience of the overall application. Furthermore, it is impossible to determine the best and worst combinations of attribute values with respect to fault resiliency via testing, due to the complexities of the underlying distributed system and the many possible attribute value combinations. In this paper, we present a model-based reinforcement learning workflow towards service mesh fault resiliency. Our approach enables the prediction of the most significant fault resilience behaviors at a web application-level, scratching from single service to aggregated multi-service management with efficient agent collaborations.

The 1997 AAAI Fall Symposia

AI Magazine

The Association for the Advancement of Artificial Intelligence held its 1997 Fall Symposia Series on 7 to 9 November in Cambridge, Massachusetts. This article contains summaries of the six symposia that were conducted: (1) Communicative Action in Humans and Machines, (2) Context in Knowledge Representation and Natural Language, (3) Intelligent Tutoring System Authoring Tools, (4) Model-Directed Autonomous Systems, (5) Reasoning with Diagrammatic Representations II, and (6) Socially Intelligent Agents.

Communicative Action in Humans and Machines

AI Magazine

This symposium reexamined the view (proposed by Austin and developed by Searle and others) of communication as action rather than transmission of information. Such a view has become popular as a characterization of language use, and it plays a central role in the dialogue-management components of many systems that communicate with human users or other agents. An abstract level of representation such as speech acts is also useful as a media-independent characterization of the function of communication. Current work that was presented and discussed at the symposium included both extensions to classical speech-act theory as well as attempts at standardization of speech-act labels. The extensions included accounts of dialogue phenomena other than classical illocutionary acts, such as turn taking, feedback, problem solving, and persuasion as well as the importance of social phenomena such as rights, roles, and obligations.