Benito Mendoza

Bidding algorithms for a distributed combinatorial auction

Distributed allocation and multiagent coordination problems can be solved through combinatorial auctions. However, most of the existing winner determination algorithms for combinatorial auctions are centralized. The PAUSE auction is one of a few efforts to release the auctioneer from having to do all the work (it might even be possible to get rid of the auctioneer). It is an increasing price combinatorial auction that naturally distributes the problem of winner determination amongst the bidders in such a way that they have an incentive to perform the calculation. It can be used when we wish to distribute the computational load among the bidders or when the bidders do not wish to reveal their true valuations unless necessary. PAUSE establishes the rules the bidders must obey. However, it does not tell us how the bidders should calculate their bids. We have developed a couple of bidding algorithms for the bidders in a PAUSE auction. Our algorithms always return the set of bids that maximizes the bidders utility. Since the problem is NP-Hard, run time remains exponential on the number of items, but it is remarkably better than an exhaustive search. In this paper we present our bidding algorithms, discuss their virtues and drawbacks, and compare the solutions obtained by them to the revenue-maximizing solution found by a centralized winner determination algorithm.

A multi-agent model for fault diagnosis in petrochemical plants

Petrochemical plants are extremely complex systems with many dynamically interconnected components. Traditional approaches to fault detection and diagnosis of these complex systems follow a centralized design in which huge and sophisticated models (e.g., first principle models) are constructed to process sensor data acquired from the entire plant. These systems are very difficult to design due to their complexity. Maintaining such a system to reflect any plant changes (e.g., equipment replacement), is also very challenging. In this article, we introduce a multi-agent model for fault detection and diagnosis which exploits the concept of leadership; that is, when a fault is detected one agent emerges as leader and coordinates the fault classification process. The proposed model is flexible, modular, decentralized, and portable. Our experimental results show that even using simple detection and diagnosis methods, the model can achieve comparable results to those from sophisticated centralized approaches.

Behavioral Queries for Service Selection: An Agile Approach to SOC

Automatic service discovery and selection is the key aspect for composing Web services dynamically in service-oriented computing (SOC). Current approaches to automating discovery and selection make use of only structural and functional aspects of the Web services. We believe that behavioral selection of Web services should be used to provide more precise results. Service behavior is difficult to specify prior to service execution and instead is better described based on experience with the service execution. In this paper, we propose a novel approach to service selection and maintenance-inspired by agile software development techniques-that is based on behavioral queries specified as test cases. Behavior is evaluated through the analysis of execution values of functional and non-functional parameters.

On bidding algorithms for a distributed combinatorial auction

Combinatorial auctions CAs are a great way to solve complex resource allocation and coordination problems. However, CAs require a central auctioneer who receives the bids and solves the winner determination problem, an NP-hard problem. Unfortunately, a centralized auction is not a good fit for real world situations where the participants have proprietary interests that they wish to remain private or when it is difficult to establish a trusted auctioneer. The work presented here is motivated by the vision of distributed CAs; incentive compatible peer-to-peer mechanisms to solve the allocation problem, where bidders carry out the needed computation. For such a system to exist, both a protocol that distributes the computational task amongst the bidders and strategies for bidding behavior are needed. PAUSE is combinatorial auction mechanism that naturally distributes the computational load amongst the bidders, establishing the protocol or rules the participants must follow. However, it does not provide bidders with bidding strategies. This article revisits and reevaluates a set of bidding algorithms that represent different bidding strategies that bidders can use to engage in a PAUSE auction, presenting a study that analyzes them with respect to the number of goods, bids, and bidders. Results show that PAUSE, along with the aforementioned heuristic bidding algorithms, is a viable method for solving combinatorial allocation problems without a centralized auctioneer.

Agile Plant Management Using Agents and Mobile Devices: Enhancing Collaboration and Information Integration in Large-Scale

In this paper, we introduce a mobile device enabled multi-agent system that aims at more effective information integration for large-scale plant management. In our approach, different agents are in charge of individual plant units, plant activities (e.g., scheduling), and human-computer interface. Agents residing in mobile devices enable operators to access critical plant information, process data, and make decisions in a remote and around-the-clock fashion. The effectiveness of the system has been demonstrated with an implementation to manage a virtual plan.

A Multi-agent Model with Dynamic Leadership for Fault Diagnosis in Chemical Plants

Timely fault detection and diagnosis are critical matters for modern chemical plants and refineries. Traditional approaches to fault detection and diagnosis of those complex systems produce centralized models that are very difficult to maintain. In this article, we introduce a biologically inspired multi-agent model which exploits the concept of leadership; that is, when a fault is detected one agent emerges as leader and coordinates the fault classification process. The proposed model is flexible, modular, decentralized, and portable. Our experimental results show that even using simple detection and diagnosis methods, the model can achieve comparable results to those from sophisticated centralized approaches.

On the Use of Semantic-Based AIG to Automatically Generate Programming Exercises

In introductory programming courses, proficiency is typically achieved through substantial practice in the form of relatively small assignments and quizzes. Unfortunately, creating programming assignments and quizzes is both, time-consuming and error-prone. We use Automatic Item Generation (AIG) in order to address the problem of creating numerous programming exercises that can be used for assignments or quizzes in introductory programming courses. AIG is based on the use of test-item templates with embedded variables and formulas which are resolved by a computer program with actual values to generate test-items. Thus, hundreds or even thousands of test-items can be generated with a single test-item template. We present a semantic-based AIG that uses linked open data (LOD) and automatically generates contextual programming exercises. The approach was incorporated into an existing self-assessment and practice tool for students learning computer programming. The tool has been used in different introductory programming courses to generate a set of practice exercises different for each student, but with the same difficulty and quality.