3,485 research outputs found
Designing minimal effective normative systems with the help of lightweight formal methods
Normative systems (i.e., a set of rules) are an important approach to achieving effective coordination among (often an arbitrary number of) agents in multiagent systems. A normative system should be effective in ensuring the satisfaction of a desirable system property, and minimal (i.e., not containing norms that unnecessarily over-constrain the behaviors of agents). Designing or even automatically synthesizing minimal effective normative systems is highly non-trivial. Previous attempts on synthesizing such systems through simulations often fail to generate normative systems which are both minimal and effective. In this work, we propose a framework that facilitates designing of minimal effective normative systems using lightweight formal methods. Given a minimal effective normative system which coordinates many agents must be minimal and effective for a small number of agents, we start with automatically synthesizing one such system with a few agents. We then increase the number of agents so as to check whether the same design remains minimal and effective. If it is, we manually establish an induction proof so as to lift the design to an arbitrary number of agents
Ten Lightweight SA&D Tools Based on Work System Theory and Its Extensions
This paper illustrates ten lightweight SA&D tools that could support initial deliberations about system requirements and subsequent sanity checking for high-level designs and for proposed functions and features. The tools are as relevant to agile development as to other approaches related to work systems in organizations. A brief introduction to the work system perspective leads to a section that illustrates ten SA&D tools based directly on work system theory or its extensions. The tools are applied to the same situation, a hiring system at a technical firm. These tools are not part of established SA&D pedagogy or practice. This paperâs ideas provide a context for comparing the focus of established SA&D methods and tools with a broader view of SA&D that engages managers and other business professionals more fully
Towards Experience Management for Very Small Entities
International audienceThe ISO/IEC 29110 standard: Lifecycle profiles for Very Small Entities, provides several Process Reference Models applicable to the vast majority of very small entities (defined by the ISO as "an entity (enterprise, organization, department or project) having up to 25 people") which do not develop critical software and share typical situational factors. An ISO/IEC 29110 pilot project has been established between the Software Engineering group at Brest University and a 14-employee company with the aim of establishing an engineering discipline for a new Web-based project. As the project proceeded, it became apparent that setting up the ISO/IEC 29110 standard has to be performed in two steps: 1) provide self-training materials to the VSE employees on this new standard; and 2) support good practices with a simple Experience Management system which is compatible with the ISO/IEC 29110 standard. This paper reports the lessons learned about training from the pilot project, and addresses the research issues associated with the Experience Management system
Video Game Development in a Rush: A Survey of the Global Game Jam Participants
Video game development is a complex endeavor, often involving complex
software, large organizations, and aggressive release deadlines. Several
studies have reported that periods of "crunch time" are prevalent in the video
game industry, but there are few studies on the effects of time pressure. We
conducted a survey with participants of the Global Game Jam (GGJ), a 48-hour
hackathon. Based on 198 responses, the results suggest that: (1) iterative
brainstorming is the most popular method for conceptualizing initial
requirements; (2) continuous integration, minimum viable product, scope
management, version control, and stand-up meetings are frequently applied
development practices; (3) regular communication, internal playtesting, and
dynamic and proactive planning are the most common quality assurance
activities; and (4) familiarity with agile development has a weak correlation
with perception of success in GGJ. We conclude that GGJ teams rely on ad hoc
approaches to development and face-to-face communication, and recommend some
complementary practices with limited overhead. Furthermore, as our findings are
similar to recommendations for software startups, we posit that game jams and
the startup scene share contextual similarities. Finally, we discuss the
drawbacks of systemic "crunch time" and argue that game jam organizers are in a
good position to problematize the phenomenon.Comment: Accepted for publication in IEEE Transactions on Game
Wikidatians are Born: Paths to Full Participation in a Collaborative Structured Knowledge Base
We investigated how participation evolves in Wikidata as its editors become established members of the community. Originally conceived to support Wikipedia, Wikidata is a collaborative structured knowledge base, created and maintained by a large number of volunteers, whose data can be freely reused in other contexts. Just like in any other online social environment, understanding its contributors\u27 pathways to full participation helps Wikidata improve user experience and retention. \ \ We analysed how participation changes in time under the frameworks of legitimate peripheral participation and activity theory. We found out that as they engage more with the project, ``Wikidatians\u27\u27 acquire a higher sense of responsibility for their work, interact more with the community, take on more advanced tasks, and use a wider range of tools. Previous activity in Wikipedia has varied effects. As Wikidata is a young community, future work should focus on volunteers with little or no experience in similar projects and specify means to improve critical aspects such as engagement and data quality
Regulated MAS: Social Perspective
This chapter addresses the problem of building normative multi-agent systems in terms of regulatory mechanisms. It describes a static conceptual model through which one can specify normative multi-agent systems along with a dynamic model to capture their operation and evolution. The chapter proposes a typology of applications and presents some open problems. In the last section, the authors express their individual views on these mattersMunindar Singhâs effort was partially supported by the U.S. Army Research Office under grant W911NF-08-1-0105. The content of this paper does not necessarily reflect the position or policy of the U.S. Government; no official endorsement should be inferred or implied. Nicoletta Fornaraâs effort is supported by the Hasler Foundation project nr. 11115-KG and
by the SER project nr. C08.0114 within the COST Action IC0801 Agreement Technologies. Henrique Lopes Cardosoâs effort is supported by Fundação para a CiĂȘncia e a Tecnologia (FCT), under project PTDC/EIA-EIA/104420/2008. Pablo Noriegaâs effort has been partially supported by the Spanish Ministry of Science and Technology through the Agreement Technologies CONSOLIDER project under contract CSD2007-0022, and the Generalitat of Catalunya grant 2009-SGR-1434.Peer Reviewe
Personalized Approaches to Supporting the Learning Needs of Lifelong Professional Learners
Advanced learning technology research has begun to take on a complex challenge: supporting
lifelong learning. Professional learning is an essential subset of lifelong learning that is more
tractable than the full lifelong learning challenge. Professionals do not always have access to
professional teachers to provide input to the problems they encounter, so they rely on their
peers in an online learning community (OLC) to help meet their learning needs. Supporting
professional learners within an OLC is a difficult problem as the learning needs of each
learner continuously evolve, often in different ways from other learners. Hence, there is a
need to provide personalized support to learners adapted to their individual learning needs.
This thesis explores personalized approaches for detecting the unperceived learning needs
and meeting the expressed learning needs of learners in an OLC. The experimental test bed
for this research is Stack Overflow (SO), an OLC used by software professionals. To date,
seven experiments have been carried out mining SO peer-peer interaction data. Knowing that
question-answerers play a huge role in meeting the learning needs of the question-askers, the
first experiment aimed to detect the learning needs of the answerers. Results from experiment
1 show that reputable answerers themselves demonstrate unperceived learning needs as
revealed by a decline in quality answers in SO. Of course, a decline in quality answers could
impact the help-seeking experience of question-askers; hence experiment 2 sought to
understand the effects of the help-seeking experience of question-askers on their enthusiasm
to continuously participate within the OLC. As expected, negative help-seeking experiences
of question-askers had a large impact on their propensity to seek further help within the OLC.
To improve the help-seeking experience of question-askers, it is important to proactively
detect the learning needs of the question-answerers before they provide poor quality answers.
Thus, in experiment 3 the goal was to predict whether a question-answerer would give a poor
answer to a question based on their past peer-peer interactions. Under various assumptions,
accuracies ranging from 84.57% to 94.54% were achieved. Next, experiment 4 attempted to
detect the unperceived learning needs of question-askers even before they are aware of such
needs. Using information about a learnerâs interactions over a 5-month period, a prediction
was made as to what they would be asking about during the next month, achieving recall and
precision values of 0.93 and 0.81. Knowing the learning needs of question-askers early
creates an opportunity to predict prospective answerers who could provide timely and quality
answers to their question. The goal of experiment 5 was thus to predict the actual answerers
for questions based only on information known at the time the question was asked. The
iv
success rate was at best 63.15%, which would only be marginally useful to inform a real-life
peer recommender system. Thus, experiment 6 explored new measures in predicting the
answerers, boosting the success rate to 89.64%. Of course, a peer recommender system
would be deemed to be especially useful if it can provide prompt interventions, especially to
get answers to questions that would otherwise not be answered quickly. To this end,
experiment 7 attempted to predict the question-askers whose questions would be answered
late or even remain unanswered, and a success rate of 68.4% was achieved.
Results from these experiments suggest that modelling the activities of learners in an OLC is
key in providing support to them to meet their learning needs. Perhaps, the most important
lesson learned in this research is that lightweight approaches can be developed to help meet
the evolving learning needs of professionals, even as knowledge changes within a profession.
Metrics based on the experiments above are exactly such lightweight methodologies and
could be the basis for useful tools to support professional learners
- âŠ