The agent building and learning environment

Bigus, Joseph P.

doi:10.1145/336595.337066

Cited by 13 publications

(11 citation statements)

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“…Three agents were setup using the ABLE framework [3]. A Retrieval Intermediary Agent (RIA) implements functions of the retrieval intermediary (SOS) and the two benchmarking methods, SRS and RMHC.…”

Section: Methodology and Parametersmentioning

confidence: 99%

“…in agent-based home automation systems [14]. Although DM-integrated agent frameworks and platforms to realize this extraction exist (e.g., ABLE [3] and Agent Academy [22]), provisionings to deal with data size in presence of noise based on agent's available resources are not available. Unfortunately smaller (agent-affordable) data size is crucial in achieving lightweight KE.…”

Section: Mining-based Agent Learningmentioning

confidence: 99%

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Numerosity Reduction for Resource Constrained Learning

Kalegele

Takahashi

Sveholm

et al. 2013

Journal of Information Processing

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When coupling data mining (DM) and learning agents, one of the crucial challenges is the need for the Knowledge Extraction (KE) process to be lightweight enough so that even resource (e.g., memory, CPU etc.) constrained agents are able to extract knowledge. We propose the Stratified Ordered Selection (SOS) method for achieving lightweight KE using dynamic numerosity reduction of training examples. SOS allows for agents to retrieve differentsized training subsets based on available resources. The method employs ranking-based subset selection using a novel Level Order (LO) ranking scheme. We show representativeness of subsets selected using the proposed method, its noise tolerance nature and ability to preserve KE performance over different reduction levels. When compared to subset selection methods of the same category, the proposed method offers the best trade-off between cost, reduction and the ability to preserve performance.

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Section: Methodology and Parametersmentioning

confidence: 99%

Section: Mining-based Agent Learningmentioning

confidence: 99%

Numerosity Reduction for Resource Constrained Learning

Kalegele

Takahashi

Sveholm

et al. 2013

Journal of Information Processing

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“…The translator was implemented in TXL. 5 The TXL processor receives as input the grammar of the normative language, the norm description, and a set of transformation rules. The TXL processor internally generates a parser tree of the norm by using the grammar and then applies to this three the transformation rules.…”

Section: Automatically Generating the Jess Rulesmentioning

confidence: 99%

“…Since several platforms for implementing multi-agent systems [4,5,26,35] are written in Java, to provide a system that could be accessed by a Java program is important. Jess is one of the alternatives that provide an API to access the knowledge base and execute the engine from Java applications.…”

Section: The Use Of Jessmentioning

confidence: 99%

From the specification to the implementation of norms: an automatic approach to generate rules from norms to govern the behavior of agents

Silva¹

2008

Auton Agent Multi-Agent Syst

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Open multi-agent systems composed of heterogeneous, autonomous and independently designed agents are usually governed by a set of norms. The established norms regulate the behavior of the agents by pointing out their permissions, prohibitions and obligations. This paper presents a normative language to specify norms and proposes the implementation of such norms by using a rule-based system. The implementation is achieved by automatically transforming the specification of each norm of the system into a set of rules used to govern the behavior of the agents according to the norm. The governance system is able to activate and deactivate norms, to point out the norms violations and fulfillments and to inform about punishments and rewards.

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“…The Agent Building and Learning Environment (ABLE) is a Java**-based toolkit for developing and deploying hybrid intelligent agent applications. 19 It provides a comprehensive library of intelligent reasoning and learning components packaged as Java beans (known as AbleBeans) and a lightweight Java agent framework to construct intelligent agents (known as AbleAgents). The AbleBean Java interface defines a set of common attributes (name, comment, state, etc.)…”

Section: Server Self-tuning With Autotune Agentsmentioning

confidence: 99%

Managing Web server performance with AutoTune agents

et al. 2003

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Managing the performance of e-commerce sites is challenging. Site content changes frequently, as do customer interests and business plans, contributing to dynamically varying workloads. To maintain good performance, system administrators must tune their information technology environment on an ongoing basis. Unfortunately, doing so requires considerable expertise and increases the total cost of system ownership. In this paper, we propose an agent-based solution that not only automates the ongoing system tuning but also automatically designs an appropriate tuning mechanism for the target system. We illustrate this in the context of managing a Web server. There we study the problem of controlling CPU and memory utilization of an Apache ® Web server using the application-level tuning parameters MaxClients and KeepAlive, which are exposed by the server. Using the AutoTune agent framework under the Agent Building and Learning Environment (ABLE), we construct agents to fully automate a control-theoretic methodology that involves model building, controller design, and run-time feedback control. Specifically, we design (1) a modeling agent that builds a dynamic system model from the controlled server run data, (2) a controller design agent that uses optimal control theory to derive a feedback control algorithm customized to that server, and (3) a run-time control agent that deploys the feedback control algorithm in an on-line realtime environment to automatically manage the Web server. The designed autonomic feedback control system is able to handle the dynamic and interrelated dependencies between the tuning parameters and the performance metrics with guaranteed stability from control theory. The effectiveness of the AutoTune agents is demonstrated through experiments involving variations in workload, server capacity, and business objectives. The results also serve as a validation of the ABLE toolkit and the AutoTune agent framework.The increasing complexity of computing systems and applications demands a correspondingly larger human effort for system configuration and performance management. This manual effort can be timeconsuming and error-prone, and requires highly skilled personnel, making it costly. Autonomic computing 1 uses the analogy of the human autonomic nervous system to suggest the use of a higher level of automation and self-management capability in computing systems.The complexity and importance of developing autonomic computing systems has attracted research

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The agent building and learning environment

Cited by 13 publications

References 0 publications

Numerosity Reduction for Resource Constrained Learning

Numerosity Reduction for Resource Constrained Learning

From the specification to the implementation of norms: an automatic approach to generate rules from norms to govern the behavior of agents

Managing Web server performance with AutoTune agents

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