An Intelligent, Multi-Agent Environment for Concurrent and Collaborative Configuration of Personal Computers

2002 ◽  
Vol 10 (2) ◽  
pp. 143-151 ◽  
Author(s):  
Y.C. Ng ◽  
K.S. Tey ◽  
K.R. Subramanian ◽  
S.B. Tor ◽  
L.P. Khoo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
System Architecture ◽  
Software Agents ◽  
Disk Drive ◽  
Collaborative Engineering ◽  
Intelligent Software ◽  
Intelligent Software Agents ◽  
Multi Agent ◽  
And Performance ◽  
Implementation Problems

Although Concurrent and Collaborative Engineering (CCE) has enjoyed widespread acceptance in industry, many implementation problems remain. With the advent of more powerful artificial intelligence techniques, CCE can be further improved. This paper demonstrates how intelligent software agents may be deployed to facilitate concurrent, collaborative engineering. A system architecture, Java Agent Alive!, is presented as a multi-agent environment. A case study of configuring a personal computer (PC) from its processor, memory and hard disk drive is discussed to highlight the power of software agents in negotiating for the PC configuration with the best price and performance. A software agent is created and assigned to each of the PC components. These agents attend two levels of agent conferences, viz. the bidding conference and the PC component vendor's conference. At both conferences, each agent strives to offer components with the best performance and the lowest price. The agents were ascribed artificial intelligence through the Java Expert System Shell (JESS). At the end of the negotiations, five PC configurations were finalised that met the expectations of the user, who is informed of the outcome via e-mail. The strengths and limitations of the system architecture and the domain application of PC assembly, as well as means to enhance security, are also discussed. Some recommendations to further improve the limitations of Java Agent Alive! and the PC Assembly application are made.

Intelligent Software Agents

2011 ◽  
pp. 155-172
Author(s):  
Stefan Kirn ◽  
Mathias Petsch ◽  
Brian Lees
Keyword(s):  
Intelligent Agents ◽  
New Technology ◽  
Software Agents ◽  
Security Architecture ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Security Issues ◽  
Intelligent Software ◽  
Intelligent Software Agents ◽  
Multi Agent

For a new technology, such as that offered by intelligent agents, to be successful and widely accepted, it is necessary for systems, based on that technology, to be capable of maintaining security and consistency of operation when integrated into the existing infrastructure of an organisation. This paper explores some of the security issues relating to application of intelligent agents and the integration of such systems into existing organisations. First, existing information security issues for enterprises are considered. Then, a short introduction to the new technology of agents and agent systems is given. Following this, the special security problems of the new technology of software agents and the emerging risks for software and enterprises are discussed. Finally, a new security architecture for multi-agent systems is proposed, together with an explanation of how this multilevel architecture can help to improve the security of agent systems.

Intelligent Software Agents with Applications in Focus

2011 ◽  
pp. 950-955
Author(s):  
Mario Jankovic-Romano ◽  
Milan Stankovic ◽  
Uroš Krcadinac
Keyword(s):  
Traffic Control ◽  
Software Agents ◽  
Real Life ◽  
Object Oriented Programming ◽  
Mobile Systems ◽  
Multi Agent Systems ◽  
Information Recovery ◽  
Intelligent Software ◽  
Intelligent Software Agents ◽  
Multi Agent

Most people are familiar with the concept of agents in real life. There are stock-market agents, sports agents, real-estate agents, etc. Agents are used to filter and present information to consumers. Likewise, during the last couple of decades, people have developed software agents, that have the similar role. They behave intelligently, run on computers, and are autonomous, but are not human beings. Basically, an agent is a computer program that is capable of performing a flexible and independent action in typically dynamic and unpredictable domains (Luck, McBurney, Shehory, & Willmott, 2005). Agents are capable of performing actions and making decisions without the guidance of a human. Software agents emerged in the IT because of the ever-growing need for information processing, and the problems concerning dealing and working with large quantities of data. Especially important is how agents act with other agents in the same environment, and the connections they form to find, refine and present the information in a best way. Agents certainly can do tasks better if they perform together, and that is why the multi-agent systems were developed. The concept of an agent has become important in a diverse range of sub-disciplines of IT, including software engineering, networking, mobile systems, control systems, decision support, information recovery and management, e-commerce, and many others. Agents are now used in an increasingly wide number of applications — ranging from comparatively small systems such as web or e-mail filters to large, complex systems such as air-traffic control, that have a large dependency on fast and precise decision making. Undoubtedly, the main contribution to the field of intelligent software agents came from the field of artificial intelligence (AI). The main focus of AI is to build intelligent entities and if these entities sense and act in some environment, then they can be considered agents (Russell & Norvig, 1995). Also, object-oriented programming (Booch, 2004), concurrent object-based systems (Agha, Wegner, and Yonezawa, 1993), and human- computer interaction (Maes, 1994) are fields that constantly drive forward the development of agents.

COORDINATION OF MULTIPLE INTELLIGENT SOFTWARE AGENTS

1996 ◽  
Vol 05 (02n03) ◽  
pp. 181-211 ◽  
Author(s):  
KATIA SYCARA ◽  
DAJUN ZENG
Keyword(s):  
System Architecture ◽  
Information Needs ◽  
Software Agents ◽  
User Preferences ◽  
Organizational Decision Making ◽  
Interface Agents ◽  
Intelligent Software ◽  
Information Agents ◽  
Agent Collaboration ◽  
Guide System

We are investigating techniques for developing distributed and adaptive collections of information agents that coordinate to retrieve, filter and fuse information relevant to the user, task and situation, as well as anticipate user's information needs. In our system of agents, information gathering is seamlessly integrated with decision support. The task for which particular information is requested of the agents does not remain in the user's head but it is explicitly represented and supported through agent collaboration. In this paper we present the distributed system architecture, agent collaboration interactions, and a reusable set of software components for structuring agents. The system architecture has three types of agents: Interface agents interact with the user receiving user specifications and delivering results. They acquire, model, and utilize user preferences to guide system coordination in support of the user's tasks. Task agents help users perform tasks by formulating problem solving plans and carrying out these plans through querying and exchanging information with other software agents. Information agents provide intelligent access to a heterogeneous collection of information sources. We have implemented this system framework and are developing collaborating agents in diverse complex real world tasks, such as organizational decision making, investment counseling, health care and electronic commerce.

scholarly journals An Analysis of the Interaction Between Intelligent Software Agents and Human Users

2018 ◽  
Vol 28 (4) ◽  
pp. 735-774 ◽  
Author(s):  
Christopher Burr ◽  
Nello Cristianini ◽  
James Ladyman
Keyword(s):  
Software Agents ◽  
Intelligent Software ◽  
Intelligent Software Agents

scholarly journals Artificial intelligence-based conversational agent to support medication prescribing

JAMIA Open ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 225-232 ◽  
Author(s):  
Anita M Preininger ◽  
Brett South ◽  
Jeff Heiland ◽  
Adam Buchold ◽  
Mya Baca ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Natural Language ◽  
Subject Matter ◽  
System Architecture ◽  
Keyword Search ◽  
Conversational Agent ◽  
Subject Matter Experts ◽  
Rater Agreement ◽  
And Performance ◽  
Keyword Searches

Abstract Objective This article describes the system architecture, training, initial use, and performance of Watson Assistant (WA), an artificial intelligence-based conversational agent, accessible within Micromedex®. Materials and methods The number and frequency of intents (target of a user’s query) triggered in WA during its initial use were examined; intents triggered over 9 months were compared to the frequency of topics accessed via keyword search of Micromedex. Accuracy of WA intents assigned to 400 queries was compared to assignments by 2 independent subject matter experts (SMEs), with inter-rater reliability measured by Cohen’s kappa. Results In over 126 000 conversations with WA, intents most frequently triggered involved dosing (N = 30 239, 23.9%) and administration (N = 14 520, 11.5%). SMEs with substantial inter-rater agreement (kappa = 0.71) agreed with intent mapping in 247 of 400 queries (62%), including 16 queries related to content that WA and SMEs agreed was unavailable in WA. SMEs found 57 (14%) of 400 queries incorrectly mapped by WA; 112 (28%) queries unanswerable by WA included queries that were either ambiguous, contained unrecognized typographical errors, or addressed topics unavailable to WA. Of the queries answerable by WA (288), SMEs determined 231 (80%) were correctly linked to an intent. Discussion A conversational agent successfully linked most queries to intents in Micromedex. Ongoing system training seeks to widen the scope of WA and improve matching capabilities. Conclusion WA enabled Micromedex users to obtain answers to many medication-related questions using natural language, with the conversational agent facilitating mapping to a broader distribution of topics than standard keyword searches.

Intelligent Multi-Agent Systems

2009 ◽  
pp. 464-469
Author(s):  
Uros Krcadinac ◽  
Milan Stankovic ◽  
Vitomir Kovanovic ◽  
Jelena Jovanovic
Keyword(s):  
Autonomous Agents ◽  
Research Community ◽  
Object Oriented Programming ◽  
Multi Agent Systems ◽  
Autonomous Action ◽  
Agent Systems ◽  
Intelligent Software ◽  
Intelligent Software Agents ◽  
Object Based ◽  
Multi Agent

Since the AAAI (http://www.aaai.org) Spring Symposium in 1994, intelligent software agents and agentbased systems became one of the most significant and exciting areas of research and development (R&D) that inspired many scientific and commercial projects. In a nutshell, an agent is a computer program that is capable of performing a flexible, autonomous action in typically dynamic and unpredictable domains (Luck, McBurney, Shehory, & Willmott, 2005). Agents emerged as a response of the IT research community to the new data-processing requirements that traditional computing models and paradigms were increasingly incapable to deal with (e.g., the huge and ever-increasing quantities of available data). Agent-oriented R&D has its roots in different disciplines. Undoubtedly, the main contribution to the field of autonomous agents came from artificial intelligence (AI) which is focused on building intelligent artifacts; and if these artifacts sense and act in some environment, then they can be considered agents (Russell & Norvig, 1995). Also, object-oriented programming (Booch, 2004), concurrent object-based systems (Agha, Wegner, & Yonezawa, 1993), and human-computer interaction (Maes, 1994) are fields that have constantly driven forward the agent R&D in the last few decades.

Intelligent Software Agents Analysis in E-Commerce II

2009 ◽  
pp. 1452-1457
Author(s):  
Xin Luo ◽  
Somasheker Akkaladevi
Keyword(s):  
Parallel Processing ◽  
Cognitive Ability ◽  
Software Agents ◽  
Limited Resources ◽  
Information Complexity ◽  
Intelligent Software ◽  
Intelligent Software Agents ◽  
Processing Abilities ◽  
Search For Information

Cowan et al. (2002) argued that the human cognitive ability to search for information and to evaluate their usefulness is extremely limited in comparison to those of computers. In detail, it’s cumbersome and time-consuming for a person to search for information from limited resources and to evaluate the information’s usefulness. They further indicated that while people are able to perform several queries in parallel and are good at drawing parallels and analogies between pieces of information, advanced systems that embody ISA architecture are far more effective in terms of calculation power and parallel processing abilities, particularly in the quantities of material they can process (Cowan et al. 2002). According to Bradshaw (1997), information complexity will continue to increase dramatically in the coming decades. He further contended that the dynamic and distributed nature of both data and applications require that software not merely respond to requests for information but intelligently anticipate, adapt, and actively seek ways to support users.

Mobile Workforce Management in a Service-Oriented Enterprise

2009 ◽  
pp. 283-302
Author(s):  
Dickson K.W. Chiu ◽  
S.C. Cheun ◽  
Ho-Fung Leung
Keyword(s):  
Domain Knowledge ◽  
Software Agents ◽  
Mobile Technologies ◽  
Workforce Management ◽  
Intelligent Software ◽  
Intelligent Software Agents ◽  
Service Oriented ◽  
Business Functions ◽  
Telecommunication Enterprise ◽  
Phased Development

In a service-oriented enterprise, the professional workforce such as salespersons and support staff tends to be mobile with the recent advances in mobile technologies. There are increasing demands for the support of mobile workforce management (MWM) across multiple platforms in order to integrate the disparate business functions of the mobile professional workforce and management with a unified infrastructure, together with the provision of personalized assistance and automation. Typically, MWM involves tight collaboration, negotiation, and sophisticated business-domain knowledge, and thus can be facilitated with the use of intelligent software agents. As mobile devices become more powerful, intelligent software agents can now be deployed on these devices and hence are also subject to mobility. Therefore, a multiagent information-system (MAIS) infrastructure provides a suitable paradigm to capture the concepts and requirements of an MWM as well as a phased development and deployment. In this book chapter, we illustrate our approach with a case study at a large telecommunication enterprise. We show how to formulate a scalable, flexible, and intelligent MAIS with agent clusters. Each agent cluster comprises several types of agents to achieve the goal of each phase of the workforce-management process, namely, task formulation, matchmaking, brokering, commuting, and service.

Sign in / Sign up

Export Citation Format

Share Document