Agent-oriented Modeling for Collaborative Learning Environments: A Peer-to-Peer Helpdesk Case Study

In this paper, we present the analysis and modelling of Help&Learn, an agent-based peer-to-peer helpdesk system to support extra-class interactions among students and teachers. Help&Learn expands the student’s possibility of solving problems, getting involved in a cooperative learning experience that transcends the limits of classrooms. To model Help&Learn, we have used Agent-Object-Relationship Modeling Language (AORML), an UML extension for agent-oriented information systems modeling. The aim of this research is two-fold. On one hand, we aim at modeling the variety of roles and the complexity of their interactions and activities within the Help&Learn system. On the other hand, we aim at showing the expressive power and the modeling strengths of AORML

Agent-oriented constructivist knowledge management

In Ancient Times, when written language was introduced, books and manuscripts were often considered sacred. During these times, only a few persons were able to read and interpret them, while most people were limited in accepting these interpretations. Then, along with the industrial revolution of the XVIII and XIX centuries and especially boosted by the development of the press, knowledge slowly became available to all people. Simultaneously, people were starting to apply machines in the development of their work, usually characterized by repetitive processes, and especially focused in the production of consuming goods, such as furniture, clocks, clothes and so on. Following the needs of this new society, it was finally through science that new processes emerged to enable the transmission of knowledge from books and instructors to learners. Still today, people gain knowledge based on these processes, created to fulfill the needs of a society in its early stages of industrialization, thus not being compatible with the needs of the information society. In the information society, people must deal with an overloading amount of information, by the means of the media, books, besides different telecommunication and information systems technology. Furthermore, people’s relation to work has been influenced by profound changes, for instance, knowledge itself is now regarded as a valuable work product and, thus, the workplace has become an environment of knowledge creation and learning. Modifications in the world economical, political and social scenarios led to the conclusion that knowledge is the differential that can lead to innovation and, consequently, save organizations, societies, and even countries from failing in achieving their main goals. Focusing on these matters is the Knowledge Management (KM) research area, which deals with the creation, integration and use of knowledge, aiming at improving the performance of individuals and organizations. Advances in this field are mainly motivated by the assumption that organizations should focus on knowledge assets (generally maintained by the members of an organization) to remain competitive in the information society’s market. This thesis argues that KM initiatives should be targeted based on a constructivist perspective. In general, a constructivist view on KM focuses on how knowledge emerges, giving great importance to the knowledge holders and their natural practices. With the paragraph above, the reader may already have an intuition of how this work faces and targets Knowledge Management, however, let us be more precise. Research in Knowledge Management has evolved substantially in the past 30 years, coming from a centralized view of KM processes to a distributed view, grounded in organizational and cognitive sciences studies that point out the social, distributed, and subjective nature of knowledge. The first Knowledge Management Systems (KMSs) were centrally based and followed a top-down design approach. The organization managers, supported by knowledge engineers, collected and structured the contents of an organizational memory as a finished product at design time (before the organizational memory was deployed) and then disseminated the product, expecting employees to use it and update it. However, employees often claimed that the knowledge stored in the repository was detached from their real working practices. This led to the development of evolutionary methods, which prescribe that the basic KM system is initially developed and evolves proactively in an on-going fashion. However, most of the initiatives are still based on building central repositories and portals, which assume standardized vocabularies, languages, and classification schemes. Consequently, employees’ lack of trust and motivation often lead to dissatisfaction. In other words, workers resist on sharing knowledge, since they do not know who is going to access it and what is going to be done with it. Moreover, the importance attributed to knowledge may give an impression that these central systems take away a valuable asset from his or her owner, without giving appreciable benefits in return. The problems highlighted in the previous paragraph may be attenuated or even solved if a top-down/bottom-up strategy is applied when proposing a KM solution. This means that the solution should be sought with aim at organizational goals (top-down) but at the same time, more attention should be given to the knowledge holders and on the natural processes they already use to share knowledge (bottom-up). Being active agency such an important principle of Constructivism, this work recognizes that the Agent Paradigm (first defined by Artificial Intelligence and more recently adopted by Software Engineering) is the best approach to target Knowledge Management, taking a technological and social perspective. Capable of modeling and supporting social environments, agents is here recognized as a suitable solution for Knowledge Management especially by providing a suitable metaphor used for modeling KM domains (i.e. representing humans and organizations) and systems. Applying agents as metaphors on KM is mainly motivated by the definition of agents as cognitive beings having characteristics that resemble human cognition, such as autonomy, reactivity, goals, beliefs, desires, and social-ability. Using agents as human abstractions is motivated by the fact that, for specific problems, such as software engineering and knowledge management process modeling, agents may aid the analyst to abstract away from some of the problems related to human complexity, and focus on the important issues that impact the specific goals, beliefs and tasks of agents of the domain. This often leads to a clear understanding of the current situation, which is essential for the proposal of an appropriate solution. The current situation may be understood by modeling at the same time the overall goals of the organization, and the needs and wants of knowledge holders. Towards facilitating the analysis of KM scenarios and the development of adequate solutions, this work proposes ARKnowD (Agent-oriented Recipe for Knowledge Management Systems Development). Systems here have a broad definition, comprehending both technology-based systems (e.g. information system, groupware, repositories) and/or human systems, i.e. human processes supporting KM using non-computational artifacts (e.g. brain stormings, creativity workshops). The basic philosophical assumptions behind ARKnowD are: a) the interactions between human and system should be understood according to the constructivist principle of self-construction, claiming that humans and communities are self-organizing entities that constantly construct their identities and evolve throughout endless interaction cycles. As a result of such interactions, humans shape systems and, at the same time, systems constrain the ways humans act and change; b) KM enabling systems should be built in a bottom-up approach, aiming at the organizational goals, but understanding that in order to fulfill these goals, some personal needs and wants of the knowledge holders (i.e. the organizational members) need to be targeted; and c) there is no “silver bullet��? when pursuing a KM tailoring methodology and the best approach is combining existing agent-oriented approaches according to the given domain or situation. This work shows how the principles above may be achieved by the integration of two existing work on agent-oriented software engineering, which are combined to guide KM analysts and system developers when conceiving KM solutions. Innovation in our work is achieved by supporting topdown/bottom-up approaches to KM as mentioned above. The proposed methodology does that by strongly emphasizing the earlier phases of software development, the so-called requirement analysis activity. In this way, we consider all stakeholders (organizations and humans) as agents in our analysis model, and start by understanding their relations before actually thinking of developing a system. Perhaps the problem may be more effectively solved by proposing changes in the business processes, rather than by making use of new technology. And besides, in addition to humans and organizations, existing systems are also included in the model from start, helping the analyst and designer to understand which functionalities are delegated to these so-called artificial agents. In addition to that, benefits as a result of the application of ARKnowD may be also attributed to our choice of using the proper agent cognitive characteristics in the different phases of the development cycle. With the main purpose of exemplifying the use of the proposed methodology, this work presents a socially-aware recommender agent named KARe (Knowledgeable Agent for Recommendations). Recommender Systems may be defined by those that support users in selecting items of their need from a big set of items, helping users to overcome the overwhelming feeling when facing a vast information source, such as the web, an organizational repository or the like. Besides serving as a case for our methodology, this work also aims at exploring the suitability of the KARe system to support KM processes. Our choice for supporting knowledge sharing through questioning and answering processes is again supported by Constructivism proponents, who understand that social interaction is vital for active knowledge building. This assumption is also defended by some KM theories, claiming that knowledge is created through cycles of transformation between two types of knowledge: tacit and explicit knowledge. Up to now, research on KM has paid much attention to the formalization and exchange of explicit knowledge, in the form of documents or other physical artifacts, often annotated with metadata, and classified by taxonomies or ontologies. Investigations surrounding tacit knowledge have been so far scarce, perhaps by the complexity of the tasks of capturing and integrating such kind of knowledge, defined as knowledge about personal experience and values, usually confined on people’s mind. Taking a flexible approach on supporting this kind of knowledge conversion, KARe relies on the potential of social interaction underlying organizational practices to support knowledge creation and sharing. The global objective of this work is to support knowledge creation and sharing within an organization, according to its own natural processes and social behaviors. In other words, this work is based on the assumption that KM is better supported if knowledge is looked at from a constructivist perspective. To sum up, this thesis aims at: 1) Providing an agent-oriented approach to guide the creation and evolvement of KM initiatives, by analyzing the organizational potentials, behaviors and processes concerning knowledge sharing; 2) Developing the KARe recommender system, based on a semantically enriched Information Retrieval technique for recommending knowledge artifacts, supporting users to ask and answer to each others’ questions. These objectives are achieved as follows: - Defining the principles that characterize a Constructivist KM supporting environment and understanding how they may be used to support the creation of more effective KM solutions; - Providing an agent-oriented approach to develop KM systems. This approach is based on the integration of two different agent-oriented software engineering works, profiting from their strengths in providing a comprehensive methodology that targets both analysis and design activities; - Proposing and designing a socially aware agent-oriented recommender system both to exemplify the application of the proposed approach and to explore its potential on supporting knowledge creation and sharing. - Implementing an Information Retrieval algorithm to support the previously mentioned system in generating recommendations. Besides describing the algorithm, this thesis brings experimental results to prove its effectiveness

A platform-independent domain-specific modeling language for multiagent systems

Associated with the increasing acceptance of agent-based computing as a novel software engineering paradigm, recently a lot of research addresses the development of suitable techniques to support the agent-oriented software development. The state-of-the-art in agent-based software development is to (i) design the agent systems basing on an agent-based methodology and (ii) take the resulting design artifact as a base to manually implement the agent system using existing agent-oriented programming languages or general purpose languages like Java. Apart from failures made when manually transform an abstract specification into a concrete implementation, the gap between design and implementation may also result in the divergence of design and implementation. The framework discussed in this dissertation presents a platform-independent domain-specific modeling language for MASs called Dsml4MAS that allows modeling agent systems in a platform-independent and graphical manner. Apart from the abstract design, Dsml4MAS also allows to automatically (i) check the generated design artifacts against a formal semantic specification to guarantee the well-formedness of the design and (ii) translate the abstract specification into a concrete implementation. Taking both together, Dsml4MAS ensures that for any well-formed design, an associated implementation will be generated closing the gap between design and code.Aufgrund wachsender Akzeptanz von Agentensystemen zur Behandlung komplexer Problemstellungen wird der Schwerpunkt auf dem Gebiet der agentenorientierten Softwareentwicklung vor allem auf die Erforschung von geeignetem Entwicklungswerkzeugen gesetzt. Stand der Forschung ist es dabei das Agentendesign mittels einer Agentenmethodologie zu spezifizieren und die resultierenden Artefakte als Grundlage zur manuellen Programmierung zu verwenden. Fehler, die bei dieser manuellen Überführung entstehen, machen insbesondere das abstrakte Design weniger nützlich in Hinsicht auf die Nachhaltigkeit der entwickelten Softwareapplikation. Das in dieser Dissertation diskutierte Rahmenwerk erörtert eine plattformunabhängige domänenspezifische Modellierungssprache für Multiagentensysteme namens Dsml4MAS. Dsml4MAS erlaubt es Agentensysteme auf eine plattformunabhängige und graphische Art und Weise darzustellen. Die Modellierungssprache umfasst (i) eine abstrakte Syntax, die das Vokabular der Sprache definiert, (ii) eine konkrete Syntax, die die graphische Darstellung spezifiziert sowie (iii) eine formale Semantik, die dem Vokabular eine präzise Bedeutung gibt. Dsml4MAS ist Bestandteil einer (semi-automatischen) Methodologie, die es (i) erlaubt die abstrakte Spezifikation schrittweise bis hin zur konkreten Implementierung zu konkretisieren und (ii) die Interoperabilität zu alternativen Softwareparadigmen wie z.B. Dienstorientierte Architekturen zu gewährleisten

Corroborating Emotion Theory with Role Theory and Agent Technology: a Framework for Designing Emotional Agents as Motivational Tutoring Entities

Nowadays, more and more applications require systems that can interact with humans. Agents can be perceived as computing services that humans, or even other agents, can request in order to accomplish their tasks. Some services may be simple and others rather complex. A way to determine the best agents (services) to be implemented is to identify who the actors are in the object of study, which roles they play, and (if possible) what kind of knowledge they use. Socially Intelligent Agents (SIAs) are agent systems that are able to connect and interface with humans, i.e. robotic or computational systems that show aspects of human-style social intelligence. In addition to their relevance in application areas such as e-commerce and entertainment, building artefacts in software and hardware has been recognized as a powerful tool for establishing a science of social minds which is a constructive approach toward understanding social intelligence in humans and other animals. Social intelligence in humans and other animals has a number of fascinating facets and implications for the design of SIAs. Human beings are biological agents that are embodied members of a social environment and are autobiographic agents who have a unique personality. They are situated in time and space and interpret new experiences based on reconstructions of previous experiences. Due to their physical embodiment, they have a unique perspective on the world and a unique history: an autobiography. Also, humans are able to express and recognize emotions, that are important in regulating individual survival and problem-solving as well as social interactions. Like artificial intelligence research trend, SIA research trend can be pursued with different goals in mind. A deep AI approach seeks to simulate real social intelligence and processes. A shallow AI approach, which will be highlighted also within this thesis, aims to create artefacts that are not socially intelligent per se, but rather appear socially intelligent to a given user. The shallow approach does not seek to create social intelligence unless it is meaningful social intelligence vis-à-vis some user situation In order to develop believable SIAs we do not have to know how beliefs-desires and intentions actually relate to each other in the real minds of the people. If one wants to create the impression of an artificial social agent driven by beliefs and desires, it is enough to draw on investigations on how people with different cultural background, develop and use theories of mind to understand the behaviours of others. Therefore, SIA technology needs to model the folk-theory reasoning rather than the real thing. To a shallow AI approach, a model of mind based on folk-psychology is as valid as one based on cognitive theory. Distance education is understood as online learning that is technology-based training which encompasses both computer-assisted and Web-based training. These systems, which appear to offer something for everyone at any time, in any place, do not always live up to the great promise they offer. The usage of social intelligent agents in online learning environments can enable the design of “enhanced-learning environments” that allow for the development and the assessment of social competences as well as the common professional competences. Within this thesis it is shown how to corroborate affective theory with role theory with agent technology in a synchronous virtual environment in order to overcome several inconveniences of distance education systems. This research embraces also the shallow approach of SIA and aims to provide the first steps of a method for creating a believable life-like tutor agent which can partially replace human-teachers and assist the students in the process of learning. The starting point for this research came from the fact: anxious, angry or depressed students do not learn; people in these conditions do not absorb information efficiently, consequentially it is an illusion to think that learning environments that do not consider motivational and emotional factors are adequate

Ontological foundations for structural conceptual models

In this thesis, we aim at contributing to the theory of conceptual modeling and ontology representation. Our main objective here is to provide ontological foundations for the most fundamental concepts in conceptual modeling. These foundations comprise a number of ontological theories, which are built on established work on philosophical ontology, cognitive psychology, philosophy of language and linguistics. Together these theories amount to a system of categories and formal relations known as a foundational ontolog

Semantics and Verification of UML Activity Diagrams for Workflow Modelling

This thesis defines a formal semantics for UML activity diagrams that is suitable for workflow modelling. The semantics allows verification of functional requirements using model checking. Since a workflow specification prescribes how a workflow system behaves, the semantics is defined and motivated in terms of workflow systems. As workflow systems are reactive and coordinate activities, the defined semantics reflects these aspects. In fact, two formal semantics are defined, which are completely different. Both semantics are defined directly in terms of activity diagrams and not by a mapping of activity diagrams to some existing formal notation. The requirements-level semantics, based on the Statemate semantics of statecharts, assumes that workflow systems are infinitely fast w.r.t. their environment and react immediately to input events (this assumption is called the perfect synchrony hypothesis). The implementation-level semantics, based on the UML semantics of statecharts, does not make this assumption. Due to the perfect synchrony hypothesis, the requirements-level semantics is unrealistic, but easy to use for verification. On the other hand, the implementation-level semantics is realistic, but difficult to use for verification. A class of activity diagrams and a class of functional requirements is identified for which the outcome of the verification does not depend upon the particular semantics being used, i.e., both semantics give the same result. For such activity diagrams and such functional requirements, the requirements-level semantics is as realistic as the implementation-level semantics, even though the requirements-level semantics makes the perfect synchrony hypothesis. The requirements-level semantics has been implemented in a verification tool. The tool interfaces with a model checker by translating an activity diagram into an input for a model checker according to the requirements-level semantics. The model checker checks the desired functional requirement against the input model. If the model checker returns a counterexample, the tool translates this counterexample back into the activity diagram by highlighting a path corresponding to the counterexample. The tool supports verification of workflow models that have event-driven behaviour, data, real time, and loops. Only model checkers supporting strong fairness model checking turn out to be useful. The feasibility of the approach is demonstrated by using the tool to verify some real-life workflow models