Contract-net-based learning in a user-adaptive interface agency

Lenzmann B, Wachsmuth I. Contract-net-based learning in a user-adaptive interface agency. In: Weiss G, ed. Distributed Artificial Intelligence Meets Machine Learning - Learning in Multi-Agent Environments. LNAI. Vol 1221. Berlin: Springer-Verlag; 1997: 202-222.This paper describes a multi-agent learning approach to adaptation to users' preferences realized by an interface agency. Using a contract-net-based negotiation technique, agents as contractors as well as managers negotiate with each other to pursue the overall goal of dynamic user adaptation. By learning from indirect user feedback, the adjustment of internal credit vectors and the assignment of contractors that gained maximal credit with respect to the user's current preferences, the preceding session, and current situational circumstances can be realized. In this way,user adaptation is achieved without accumulating explicit user models but by the use of implicit, distributed user models

MACS: Multi-agent COTR system for Defense Contracting

The field of intelligent multi-agent systems has expanded rapidly in the recent past. Multi-agent architectures and systems are being investigated and continue to develop. To date, little has been accomplished in applying multi-agent systems to the defense acquisition domain. This paper describes the design, development, and related considerations of a multi-agent system in the area of procurement and contracting for the defense acquisition community

Biologically Inspired Approaches to Automated Feature Extraction and Target Recognition

Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically cornbine botton-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from incorrect labels that are simultaneously inconsistent and correct. The CNS Vision and Technology Labs (cns.bu.edulvisionlab and cns.bu.edu/techlab) are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution.Air Force Office of Scientific Research (F40620-01-1-0423); National Geographic-Intelligence Agency (NMA 201-001-1-2016); National Science Foundation (SBE-0354378; BCS-0235298); Office of Naval Research (N00014-01-1-0624); National Geospatial-Intelligence Agency and the National Society of Siegfried Martens (NMA 501-03-1-2030, DGE-0221680); Department of Homeland Security graduate fellowshi

Unattended network operations technology assessment study. Technical support for defining advanced satellite systems concepts

The results are summarized of an unattended network operations technology assessment study for the Space Exploration Initiative (SEI). The scope of the work included: (1) identified possible enhancements due to the proposed Mars communications network; (2) identified network operations on Mars; (3) performed a technology assessment of possible supporting technologies based on current and future approaches to network operations; and (4) developed a plan for the testing and development of these technologies. The most important results obtained are as follows: (1) addition of a third Mars Relay Satellite (MRS) and MRS cross link capabilities will enhance the network's fault tolerance capabilities through improved connectivity; (2) network functions can be divided into the six basic ISO network functional groups; (3) distributed artificial intelligence technologies will augment more traditional network management technologies to form the technological infrastructure of a virtually unattended network; and (4) a great effort is required to bring the current network technology levels for manned space communications up to the level needed for an automated fault tolerance Mars communications network

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Mechanisms to improve integrative research at the science-policy interface for sustainable catchment management

Greater integration between researchers and policy makers is required to provide an evidence base that is transparent, integrated, and adaptive to support the complexities of sustainable catchment management. Opening-up and closing-down mechanisms are equally important in creating and establishing such an evidence base. We provide examples of both types based on our recent research and knowledge-transfer activities at the science-policy interface. Through our coordination role for the United Kingdom government we provide opening up physical and virtual forums for researchers and government science and policy staff to learn about and assess the gaps and uncertainties of the evidence base. Closing-down mechanisms are vital to policy cycles, in that they distil what is known and what is unknown. The Diffuse Pollution User Manual provides a valuable tool for policy and catchment management staff to assess the potential effectiveness of different combinations of remedial diffuse-pollution mitigation methods. It is vital that that opening-up and closing-down mechanisms are iteratively linked given the complexity and uncertainty of the science and policy cycles. Advances in integrative research at the science-policy interface are vital if there is to be a move to more deliberative policy making

Aerospace Medicine and Biology: a Continuing Bibliography with Indexes (Supplement 328)

This bibliography lists 104 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during September, 1989. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections

The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn without prior extraction of features fits well in response to the rapid iteration of Android malware. The traditional solution for detecting Android malware requires continuous learning through pre-extracted features to maintain high performance of identifying the malware. In order to reduce the manpower of feature engineering prior to the condition of not to extract pre-selected features, we have developed a coloR-inspired convolutional neuRal networks (CNN)-based AndroiD malware Detection (R2-D2) system. The system can convert the bytecode of classes.dex from Android archive file to rgb color code and store it as a color image with fixed size. The color image is input to the convolutional neural network for automatic feature extraction and training. The data was collected from Jan. 2017 to Aug 2017. During the period of time, we have collected approximately 2 million of benign and malicious Android apps for our experiments with the help from our research partner Leopard Mobile Inc. Our experiment results demonstrate that the proposed system has accurate security analysis on contracts. Furthermore, we keep our research results and experiment materials on http://R2D2.TWMAN.ORG.Comment: Verison 2018/11/15, IEEE BigData 2018, Seattle, WA, USA, Dec 10-13, 2018. (Accepted