Cesar Augusto Tacla

Agent supported portals and knowledge management in complex R&D projects

This paper is concerned with organizing knowledge management in complex R&D projects where time is the prime factor. We argue that specific portals developed using groupware technology and products should be augmented by agents in order to increase the overall system reactivity and to achieve the global objective, namely to save time. We describe a portal we have developed using a groupware approach, we give the structure of a system of cognitive agents, and discuss the possibility of bringing the two technologies together.

A multi-agent system for acquiring and sharing lessons learned

This paper presents a multi-agent system for knowledge management (KM) in research and development (R&D) projects. R&D teams have no time to organize project information, nor to articulate the rationale behind the actions that generated the information. Our aim is to provide a system for helping team members to explicit knowledge, and to allow them to share their experiences, i.e. lessons learned (LL), without asking them too much extra-work. The article focuses on how we intend to help the team members to feed the system with LL, using the operations they perform on desktop computers, and how we intend to exploit the LL by using a case-based reasoning engine. We have been developing a prototype of such a KM system for a cooperative project.

An architecture for supporting small collocated teams in cooperative software development

Most CSCW and groupware systems focus the activities of distributed teams involved in large projects by means of tools for communication and awareness. The activities of small collocated teams are often neglected. Analyzing preliminary requirements of small teams, it is possible to observe the need of tools to help the elaboration of project documentation. This paper presents a multi-agent system architecture to support software development in small collocated teams. The architecture proposed in this paper tackles the problem of elaborating documentation, by facilitating the elaboration of the Small Project Management Plan [1]. Tools used in the software development are encapsulated by agents that extract and organize useful information for the elaboration of such a document.

Supporting small teams in cooperatively building application domain models

Building application domain models is a time-consuming activity in software engineering. In small teams, it is an activity that involves almost all participants, including developers and domain experts. In our approach, we support the knowledge engineering activity by reusing tagging done by team participants when they search information on the Web about the applications domain. Team participants collaborate implicitly when they do tagging because their individually created tags are collected and form a folksonomy. This folksonomy reflects their knowledge about the domain and it is the base for eliciting domain model elements in the knowledge acquisition and conceptualization tasks in a consensual way. Experiments provide evidence that our approach helps team participants to build richer domain models than if they do not use our software tool. The tool allows the reuse of simple annotations as long as users learn about the applications domain.

A Multi-agent Architecture for Evolving Memories

This paper concerns a multi-agent system for knowledge management (KM) in research and development (R&D) projects. R&D teams have no time to organize project information, or to articulate the rationale behind the actions that generated the information. Our aim is to provide a system for helping team members to make knowledge explicit, and to allow them to share their experiences without asking them too much extra-work. The article focuses on how we intend to build personal memories by continuously capturing the day-to-day objects the team members deal with on desktop computers, and how personal memories are progressively connected to form a group memory as individuals exchange and reuse other individuals’ knowledge. The main contribution of this paper is a multi-agent architecture that prescribes the processes for building the personal and the group memories. A prototype has been developed and at the current stage the processes for building the personal memories are implemented.

Planning transport of crude oil derivatives with simultaneous auctions

In supply chains of the petroleum industry, maintaining a balance between production and consumption of petroleum derivatives is a crucial issue. Basically, this kind of chain has several elements like producer bases, consumer bases, intermediary terminals that are linked by a transport network. These elements should cooperate to reach the global balance of the system with a minimum transportation cost. In this context, this paper proposes and compares two solutions based on auctions carried out by agents, which represent the elements of the aforementioned chain. These solutions are characterized respectively by the execution of sequential auctions and simultaneous auctions in order to consumers bid for batches of crude oil derivatives. In the comparative tests, the solution based on simultaneous auctions is better than the sequential one mainly because of the intense cooperation among agents.

LEARNING NEGOTIATION POLICIES USING ENSEMBLE-BASED DRIFT DETECTION TECHNIQUES

In this work we compare drift detection techniques and we show how they can improve the performance of trade agents in multi-issue bilateral dynamic negotiations. In a dynamic negotiation the utility values and functions of trade agents can change on the fly. Intelligent trade agents must identify and take such drift in the competitors into account changing also the offer policies to improve the global utility throughout the negotiation. However, traditional learning mechanisms disregard possible changes in a competitors offer/counter-offer policy. In that case, the agent performance may decrease drastically. In our approach, a trade agent has a staff of weighted learner agents used to predict interesting offers. The staff uses the Dynamic Weighted Majority (DWM) algorithm to adapt itself creating, deleting and adapting staff members. The results obtained with the IB3 (Instance-based) learners and IB3-DWM learners show that ensemble methods like DWM are suitable for correctly identifying changes in agent negotiations.

MODUS-SD: User modeling in collaborative software development

During the software development cycle, artifacts (source-code, documentation, user manuals, etc.) are written, most of them, cooperatively. Each participant in a software development team plays a specific role, but may write an artifact cooperatively with participants playing different roles. In large and distributed teams the roles are well-defined and mainly respected. In small teams however, a participant may perform different roles simultaneously. We have already proposed the architecture of a system (called CSCW-SD) to support small collocated teams developing software. We are now adding to CSCW-SD a module, called MODUS-SD, capable of modeling users. In this paper we present this module, how it was implemented and present a case study, with preliminary results, in the context of modeling Java developers in a collaborative software development team.

A semi-automatic source code documentation method for small software development teams

Software developers often face the task of documenting source code. For many of them, documenting code development is a boring task. However, source code documentation is an important task, especially when dealing with groups of developers. An updated documentation allows group members to have greater visibility on what has been and is being developed, allowing the reuse of source code. This research aims at designing, developing and validating a semi-automatic documentation method for source code from the existing design documentation on a particular project being developed by a small team, as well as updating this documentation from information gathered from the source code under development. It is understood as design documentation, those documents or parts of documents that are linked directly to the code under construction.

Partial and dynamic ontology mapping model in dialogs of agents

This paper describes a partial and dynamic ontology mapping model for agents to achieve an agreement about meaning of concepts used in the content part of messages during a dialog. These agents do not share an ontology. The proposed model prescribes phases to cluster and to select the clusters with background knowledge in an ontology, operations to interpret the content of a message based on syntactic, semantic approaches, and the dialog between agents according to the difficulty in finding similar concepts in both ontologies. A case study is presented.