Alberto Sardinha

CMieux: Adaptive strategies for competitive supply chain trading

Supply chains are a central element of todays global economy. Existing management practices consist primarily of static interactions between established partners. Global competition, shorter product life cycles and the emergence of Internet-mediated business solutions create an incentive for exploring more dynamic supply chain practices. The supply chain trading agent competition (TAC SCM) was designed to explore approaches to dynamic supply chain trading between automated software agents. TAC SCM pits trading agents developed by teams from around the world against one another. Each agent is responsible for running the procurement, planning and bidding operations of a PC assembly company, while competing with others for both customer orders and supplies under varying market conditions. This paper presents Carnegie Mellon Universitys 2005 TAC SCM entry, the CMieux supply chain trading agent. CMieux implements a novel approach for coordinating supply chain bidding, procurement and planning, with an emphasis on the ability to rapidly adapt to changing market conditions. We present empirical results based on 200 games involving agents entered by 25 different teams during what can be seen as the most competitive phase of the 2005 tournament. Not only did CMieux perform among the top five agents, it significantly outperformed these agents in procurement while matching their bidding performance. We also simulated 40 games against the best publicly available agent binaries. Our results show CMieux has significantly better average overall performance than any of these agents.

CMieux: adaptive strategies for competitive supply chain trading

Supply chains are a central element of todays global economy. Existing management practices consist primarily of static interactions between established partners. Global competition, shorter product life cycles and the emergence of Internet-mediated business solutions create an incentive for exploring more dynamic supply chain practices. The Supply Chain Trading Agent Competition (TAC SCM) was designed to explore approaches to dynamic supply chain trading. TAC SCM pits against one another trading agents developed by teams from around the world. Each agent is responsible for running the procurement, planning and bidding operations of a PC assembly company, while competing with others for both customer orders and supplies under varying market conditions. This paper presents Carnegie Mellon Universitys 2005 TAC SCM entry, the CMieux supply chain trading agent. CMieux implements a novel approach to coordinating supply chain bidding, procurement and planning, with an emphasis on the ability to rapidly adapt to changing market conditions. We present empirical results based on 200 games involving agents entered by 25 different teams during what can be seen as the most competitive phase of the 2005 tournament. Not only did CMieux perform among the top five agents, it significantly outperformed these agents in procurement while matching their bidding performance.

EA-Analyzer: automating conflict detection in a large set of textual aspect-oriented requirements

One of the aims of Aspect-Oriented Requirements Engineering is to address the composability and subsequent analysis of crosscutting and non-crosscutting concerns during requirements engineering. A composition definition explicitly represents interdependencies and interactions between concerns. Subsequent analysis of such compositions helps to reveal conflicting dependencies that need to be resolved in requirements. However, detecting conflicts in a large set of textual aspect-oriented requirements is a difficult task as a large number of explicitly defined interdependencies need to be analyzed. This paper presents EA-Analyzer, the first automated tool for identifying conflicts in aspect-oriented requirements specified in natural-language text. The tool is based on a novel application of a Bayesian learning method. We present an empirical evaluation of the tool with three industrial-strength requirements documents from different domains and a fourth academic case study used as a de facto benchmark in several areas of the aspect-oriented community. This evaluation shows that the tool achieves up to 93.90 % accuracy regardless of the documents chosen as the training and validation sets.

A meta-control architecture for orchestrating policy enforcement across heterogeneous information sources

There is increasing demand from both organizations and individuals for technology capable of enforcing sophisticated, context-sensitive policies, whether security and privacy policies, corporate policies or policies reflecting various regulatory requirements. In open environments, enforcing such policies requires the ability to reason about the policies themselves as well as the ability to dynamically identify and access heterogeneous sources of information. This article introduces a semantic web framework and a meta-control model to orchestrate policy reasoning with the identification and access of relevant sources of information. Specifically, sources of information are modeled as web services with rich semantic profiles. Policy Enforcing Agents rely on meta-control strategies to dynamically interleave semantic web reasoning and service discovery and access. Meta-control rules can be customized to best capture the requirements associated with different domains and different sets of policies. This architecture has been validated in the context of different environments, including a collaborative enterprise domain as well as several mobile and pervasive computing applications deployed on Carnegie Mellons campus. We show that, in the particular instance of access control policies, the proposed framework can be viewed as an extension of the XACML architecture, in which Policy Enforcing Agents offer a particularly powerful way of implementing XACMLs Policy Information Point (PIP) and Context Handler functionality. At the same time, our proposed architecture extends to a much wider range of policies and regulations. Empirical results suggest that the semantic framework introduced in this article scales favorably on problems with up to hundreds of services and tens of service directories.

EA-tracer: identifying traceability links between code aspects and early aspects

Early aspects are crosscutting concerns that are identified and addressed at the requirements and architecture level, while code aspects are crosscutting concerns that manifest at the code level. Currently, there are many approaches to address the identification and modularization of these cross-cutting concerns at each level, but very few techniques try to analyze the relationship between early aspects and code aspects. This paper presents a tool for automating the process of identifying traceability links between requirements-level aspects and code aspects, which is a first step towards an in-depth analysis. We also present an empirical evaluation of the tool with a real-life Web-based information system and a software product line for handling data on mobile devices. The results show that we can identify traceability links between early aspects and code aspects with a high accuracy.

EA-Analyzer: Automating Conflict Detection in Aspect-Oriented Requirements

One of the aims of Aspect-Oriented Requirements Engineering is to address the composability and subsequent analysis of crosscutting and non-crosscutting concerns during requirements engineering. Composing concerns may help to reveal conflicting dependencies that need to be identified and resolved. However, detecting conflicts in a large set of textual aspect-oriented requirements is an error-prone and time-consuming task. This paper presents EA-Analyzer, the first automated tool for identifying conflicts in aspect-oriented requirements specified in natural-language text. The tool is based on a novel application of a Bayesian learning method that has been effective at classifying text. We present an empirical evaluation of the tool with three industrial-strength requirements documents from different real-life domains. We show that the tool achieves up to 92.97% accuracy when one of the case study documents is used as a training set and the other two as a validation set.

Ad hoc teamwork by learning teammates' task

This paper addresses the problem of ad hoc teamwork, where a learning agent engages in a cooperative task with other (unknown) agents. The agent must effectively coordinate with the other agents towards completion of the intended task, not relying on any pre-defined coordination strategy. We contribute a new perspective on the ad hoc teamwork problem and propose that, in general, the learning agent should not only identify (and coordinate with) the teammates’ strategy but also identify the task to be completed. In our approach to the ad hoc teamwork problem, we represent tasks as fully cooperative matrix games. Relying exclusively on observations of the behavior of the teammates, the learning agent must identify the task at hand (namely, the corresponding payoff function) from a set of possible tasks and adapt to the teammates’ behavior. Teammates are assumed to follow a bounded-rationality best-response model and thus also adapt their behavior to that of the learning agent. We formalize the ad hoc teamwork problem as a sequential decision problem and propose two novel approaches to address it. In particular, we propose (i) the use of an online learning approach that considers the different tasks depending on their ability to predict the behavior of the teammate; and (ii) a decision-theoretic approach that models the ad hoc teamwork problem as a partially observable Markov decision problem. We provide theoretical bounds of the performance of both approaches and evaluate their performance in several domains of different complexity.

Using information gain to analyze and fine tune the performance of supply chain trading agent

The Supply Chain Trading Agent Competition (TAC SCM) was designed to explore approaches to dynamic supply chain trading. During the course of each years competition historical data is logged describing more than 800 games played by different agents from around the world. In this paper, we present analysis that is focused on determining which features of agent behavior, such as the average lead time requested for supplies or the average selling price offered on finished products, tend to differentiate agents that win from those that do not. We present a visual inspection of data from 16 games played in one bracket of the 2006 TAC SCM semi-final rounds. Plots of data from these games help isolate behavioral features that distinguish top performing agents in this bracket. We then introduce a metric based on information gain to provide a more complete analysis of the 80 games played in the 2006 TAC SCM quarter-final, semi-final and final rounds. The metric captures the amount of information that is gained about an agents performance by knowing its value for each of 20 different behavioral features. Using this metric we find that, in the final rounds of the 2006 competition, winning agents distinguished themselves by their procurement decisions, rather than their customer bidding decisions. We also discuss how we used the analysis presented in this paper to improve our entry for the 2007 competition, which was one of the six finalists that year.

VITHEA-Kids: a Platform for Improving Language Skills of Children with Autism Spectrum Disorder

In this work, we present a platform designed for children with Autism Spectrum Disorder to develop language and generalization skills, in response to the lack of applications tailored for the unique abilities, symptoms, and challenges of the autistic children. This platform allows caregivers to build customized multiple choice exercises while taking into account specific needs/characteristics of each child. We also propose a module for the automatic generation of exercises, aiming to ease the task of exercise creation for caregivers.

Conflict Identification with EA-Analyzer

Conflict identification in Aspect-Oriented Requirements Engineering (AORE) is an integral step toward resolving conflicting dependencies between requirements at an early stage of the software development. However, to date there has been no work supporting detection of conflicts in a large set of textual requirements without converting texts into an alternative representation (such as models or formal specification) or direct stakeholder involvement. Here, we present EA-Analyzer, an automated tool for identifying conflicts directly in aspect-oriented requirements specified in natural language text. This chapter is centered on a case study-based discussion of the accuracy of the tool. EA-Analyzer is applied to the Crisis Management System, a case study used as an established benchmark in several areas of aspect-oriented research.