By Bernhard Bauer, Jörg P. Müller (auth.), Paolo Giorgini, Jörg P. Müller, James Odell (eds.)
The explosive progress of program components reminiscent of digital trade, ent- prise source making plans and cellular computing has profoundly and irreversibly replaced our perspectives on software program platforms. these days, software program is to be in line with open architectures that always swap and evolve to house new parts and meet new standards. software program should also function on di- hire structures, with no recompilation, and with minimum assumptions approximately its working setting and its clients. in addition, software program has to be strong and self sufficient, in a position to serving a naive person with no less than overhead and interference. Agent options carry nice promise for responding to the hot realities of so- ware platforms. They o?er higher-level abstractions and mechanisms that deal with matters comparable to wisdom illustration and reasoning, communique, coor- country, cooperation between heterogeneous and self sufficient events, belief, commitments, pursuits, ideals, and intentions, all of which want conceptual mo- ling. at the one hand, the concrete implementation of those options can result in complicated functionalities, e.g., in inference-based question answering, transaction keep an eye on, adaptive work?ows, brokering and integration of disparate details resources, and automatic communique strategies. nonetheless, their wealthy representational functions enable extra trustworthy and ?exible remedies of c- plex organizational techniques, resulting in extra e?ective requisites research and architectural/detailed design.
Read or Download Agent-Oriented Software Engineering IV: 4th InternationalWorkshop, AOSE 2003, Melbourne, Australia, July 15, 2003. Revised Papers PDF
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Extra info for Agent-Oriented Software Engineering IV: 4th InternationalWorkshop, AOSE 2003, Melbourne, Australia, July 15, 2003. Revised Papers
ManagePheromoneTrapPlant) have fulﬁllment constraints, but no creation constraints: we are not interested in modeling the reasons why an advisor wants to manage a pheromone trap plant. , AreaCollection, OrchardAnalysis) typically have constraints that relate their creation with the state of their parent intentional elements. For instance, Figure 2 shows that a creation condition for an instance of goal AreaCollection is that the parent goal ManagePheromoneTrapPlant is not yet fulﬁlled: if the advisor has already managed the pheromone trap plant there is no need to manage it again (unless a new management activity is started).
In plant disease control, it promotes the use of low-impact techniques and chemicals, and the exploitation of natural defense mechanisms. 40 A. Perini et al. Fig. 1. Goal diagram of the actor Advisor, showing an example of goal analysis. the help of agronomists of the local advisory service, represented by the actor Advisor. The example focuses on a particular technique for reducing the risk of infection of an apple pest which requires to install in the orchard a pheromones trapping system for preventing the pest population growth in the area.
The above question can generalized by the following: Focusing on a speciﬁc actor, what are the critical dependencies on external actors? That is, what actors can prevent the achievement of a main goal if they do not achieve delegated goals? This kind of questions can be seen as queries on the graph corresponding to a goal diagram. They are particularly useful when dealing with a complex model for which a direct inspection of the diagram is impractical. For these questions, the informal queries can be automatically translated into the relative FT speciﬁcation, so no additional eﬀort to the iAnalyst is required.