1,721,074 research outputs found
A methodology to take account of diversity in collective adaptive system
No full textCollective Adaptive Systems (CASs) are comprised of a heterogeneous set of components often developed in a distributed manner. Their users are diverse with respect to their profiles, preferences, interests and goals, and hence, have different requirements. We propose a typology for the diversity of these components, users, and their requirements. We then present a methodology which provides steps to integrate features that record diversity to support accountability. The foundation of accountability is provided by provenance data, and a CAS vocabulary, these knowledge representation languages provide the core vocabulary that can be exploited by agents and services
Prov-template evaluation dataset
No full textPROV-TEMPLATE is a declarative approach that allows designers and programmers to design and generate provenance compatible with the PROV standard. Designers specify the topology of the provenance to be generated by composing templates, which are provenance graphs containing variables, acting as placeholders for values. Programmers write programs that log values and package them up in sets of bindings, a datastructure associating variables and values. An expansion algorithm takes care of generating instantiated provenance from templates and sets of bindings in any of the serialisation format supported by PROV. Our quantitative evaluation shows that sets of bindings have a size typically 40% of expanded provenance and that the expansion algorithm is suitably tractable, operating in fractions of milliseconds for the type of templates surveyed in the paper. Furthermore, the approach shows four significant software engineering benefits in terms of distributed developement, provenance maintenance, potential runtime and static checks, and provenance consumption. The paper gathers quantitative data and qualitative benefits descriptions from four different applications making use of PROVTEMPLATE. The system is implemented and released in the open-source library ProvToolbox for provenance processing.
This data is related to the publication Moreau, Luc, Batlajery, Belfrit Victor, Huynh, Dong, Michaelides, Danius and Packer, Heather (2017) A Templating System to Generate Provenance. IEEE Transactions on Software Engineering</span
Evolving ontologies with online learning and forgetting algorithms
Full text linkAgents that require vocabularies to complete tasks can be limited by static vocabularies which cannot evolve to meet unforeseen domain tasks, or reflect its changing needs or environment. However, agents can benefit from using evolution algorithms to evolve their vocabularies, namely the ability to support new domain tasks. While an agent can capitalise on being able support more domain tasks, using existing techniques can hinder them because they do not consider the associated costs involved with evolving an agent's ontology. With this motivation, we explore the area of ontology evolution in agent systems, and focus on the reduction of the costs associated with an evolving ontology.In more detail, we consider how an agent can reduce the costs of evolving an ontology, these include costs associated with: the acquisition of new concepts; processing new concepts; the increased memory usage from storing new concepts; and the removal of unnecessary concepts. Previous work reported in the literature has largely failed to analyse these costs in the context of evolving an agent's ontology. Against this background, we investigate and develop algorithms to enable agents to evolve their ontologies.More specifically, we present three online evolution algorithms that enable agents to: i) augment domain related concepts, ii) use prediction to select concepts to learn, and iii) prune unnecessary concepts from their ontology, with the aim to reduce the costs associated with the acquisition, processing and storage of acquired concepts. In order to evaluate our evolution algorithms, we developed an agent framework which enables agents to use these algorithms and measure an agent's performance. Finally, our empirical evaluation shows that our algorithms are successful in reducing the costs associated with evolving an agent's ontology
Evolving Ontological Knowledge Bases through Agent Collaboration
No full textThis paper presents initial work that will enable an agent to augment its ontology to incorporate required knowledge from other agents, in order to let it answer domain related queries. Specifically, our agents are heterogeneous, whereby an agent has its own interest domain and represents this with an ontology that contains relevant conceptualisations. These agents have intersecting domain interests and their ontologies represent a set of overlapping concepts with alternative symbolic representations. In this setting, our proposed approach focuses on reducing the costs associated with acquiring knowledge through collaboration, and augmenting axioms into an agent’s ontology. In order to achieve this, we consider incorporating knowledge to reduce the number of messages required to answer repetitive domain related queries that require mediation, and select a shared set of axioms that represent conceptual knowledge. We present results from our approach and identify the number of messages and axioms required for a repeated transaction. These preliminary results show that augmenting an agent’s ontology can indeed reduce the number of messages and axioms required
Generating narratives from provenance relationship chains
No full textProvenance data is a rich data structured source that has a similar role to narratives, since they can both provide an account of connected events. Consuming prov data can be hard for both technical and non-technical users, because of its potential scale and the complexity of the relationships captured. Explicitly, it can be hard for users to follow and understand the chain of relationships connecting elements together. In this paper, we present an approach that generates narratives explaining chains of relationships and describe its nature with examples from a Ride Share applicatio
Ontology evolution through agent collaboration
No full textWe present a technique that enables a software agent to augment its ontology with domain related concepts by collaborating with other agents. The collaborating agents have their own individual ontologies, they can share concepts and relationships that relate to a requested specific concept (which is known as a fragment). Thus, specifically, our technique selects the fragments that will be shared. This approach enables agents to answer queries with more range and detail, and it also enables an agent to infer new exploitable knowledge. Without this capability, an agent may be limited by its domain model, and cannot reflect changes in the environment. Through empirical evaluation, we show that our technique reduces the cost of acquiring concepts that are regularly used (compared with learning nothing) and reduces the complexity of the agent's ontology by augmenting it with selected concepts and relationships which are related to its domain (compared with learning everything)
- …
