Artificial intelligence and software engineering: Status and future trends

The disciplines of Artificial Intelligence and Software Engineering have many commonalities. Both deal with modeling real world objects from the real world like business processes, expert knowledge, or process models. This article gives a short overview about these disciplines and describes some current research topics against the background of common points of contact

Bio-inspired Mechanisms for Artificial Self-organised Systems

Research on self-organization tries to describe and explain forms, complex patterns and behaviours that arise from a collection of entities without an external organizer. As researchers in artificial systems, our aim is not to mimic self-organizing phenomena arising in Nature, but to understand and to control underlying mechanisms allowing desired emergence of forms, complex patterns and behaviours. In this paper we analyze three forms of self-organization: stigmergy, reinforcement mechanisms and cooperation. For each forms of self-organisation, we present a case study to show how we transposed it to some artificial systems and then analyse the strengths and weaknesses of such an approach

Intelligent maintenance management in a reconfigurable manufacturing environment using multi-agent systems

Thesis (M. Tech.) -- Central University of Technology, Free State, 2010Traditional corrective maintenance is both costly and ineffective. In some situations it is more cost effective to replace a device than to maintain it; however it is far more likely that the cost of the device far outweighs the cost of performing routine maintenance. These device related costs coupled with the profit loss due to reduced production levels, makes this reactive maintenance approach unacceptably inefficient in many situations. Blind predictive maintenance without considering the actual physical state of the hardware is an improvement, but is still far from ideal. Simply maintaining devices on a schedule without taking into account the operational hours and workload can be a costly mistake. The inefficiencies associated with these approaches have contributed to the development of proactive maintenance strategies. These approaches take the device health state into account. For this reason, proactive maintenance strategies are inherently more efficient compared to the aforementioned traditional approaches. Predicting the health degradation of devices allows for easier anticipation of the required maintenance resources and costs. Maintenance can also be scheduled to accommodate production needs. This work represents the design and simulation of an intelligent maintenance management system that incorporates device health prognosis with maintenance schedule generation. The simulation scenario provided prognostic data to be used to schedule devices for maintenance. A production rule engine was provided with a feasible starting schedule. This schedule was then improved and the process was determined by adhering to a set of criteria. Benchmarks were conducted to show the benefit of optimising the starting schedule and the results were presented as proof. Improving on existing maintenance approaches will result in several benefits for an organisation. Eliminating the need to address unexpected failures or perform maintenance prematurely will ensure that the relevant resources are available when they are required. This will in turn reduce the expenditure related to wasted maintenance resources without compromising the health of devices or systems in the organisation

Design, Synthesis, and Evaluation of New Biodegradable Polymers and Nanoscale Assemblies for Drug Delivery

The objective of this research was to develop new polymeric nanomaterials for biomedical applications. It was envisioned that through careful design and synthesis, as well as the study of structure-property relationships, the development of materials with new properties and functions could be achieved. As a starting point, several poly(ester amide)s (PEAs) composed of α-amino acids, diols, and diacids, with varying chemical structures, molecular weights, and polydispersity indices were prepared and their thermal, rheological and mechanical properties were studied. The resulting data will aid in the design and selection of PEAs with optimal properties for targeted applications. Subsequently, a novel PEA-paclitaxel (PTX)-poly(ethylene oxide) conjugate was prepared and assembled into micelles to achieve controlled release of PTX via the hydrolysis of ester linkages. This system was compared with an analogous micellar system into which PTX was physically encapsulated and it was shown that the release of PTX from the covalent system was slower and more sustained. To provide an alternative release mechanism, a functionalized PEA with a photodegradable backbone covalently conjugated to both PTX and PEO was designed and prepared. Upon UV irradiation, micelles, formed from this graft copolymer through self-assembly, disintegrate. This feature accelerates the release of PTX compared with non-irradiated micelles, likely due to the increased exposure and hydrolysis of the ester linkages conjugating the drug to the support, upon micelle disruption. Finally, cross-linked polymer nanoparticles (nanogels) functionalized with Gd(III) chelates were designed, synthesized and characterized as enhanced contrast agents for magnetic resonance imaging (MRI). These nanogels exhibited a T1 relaxivity nearly 6-fold higher than the clinical contrast agent MagnevistÔ. This result is rationalized by the decrease in tumbling and rotational rates as a result of rigidity introduced by the cross-linking. A preliminary in vivo evaluation of this new agent was performed and the agent exhibited good contrast and enhanced circulation in the vasculature relative to MagnevistÔ

Design methodology for ontology-based multi-agent applications (MOMA)

Software agents and multi-agent systems (MAS) have grown into a very active area of research and commercial development activity. There are many current emerging real-world applications spanning multitude of diverse domains. In the context of agents, ontology has been widely recognised for their significant benefits to interoperability, reusability, and both development and operational aspects of agent systems and applications. Ontology-based multi-agent systems (OBMAS) exploit these advantages in providing intelligent and semantically aware applications. In addressing the lack of support for ontology in existing methodologies for multi-agent development, this thesis proposes a design methodology for the building of such intelligent multi-agent applications called MOMA. This alternative approach focuses on the development of ontology as the driving force of the development process. By allowing the domain and characteristics of utilisation and experimentation to be dictated through ontology, researchers and domain experts can specify the agent application without any knowledge of agent design and lower level programming. Through the use of a structured ontology model and the use of integrated tools, this approach contributes towards the building of semantically aware intelligent applications for use by researchers and domain experts. MOMA is evaluated through case studies in two different domains: financial services and e-Health

From Algorithms to (Sub-)Symbolic Inferences in Multi-Agent Systems

Extending metaphorically the Moisilean idea of “nuanced-reasoning logic” and adapting it to the e-world age of Information Technology (IT), the paper aims at showing that new logics, already useful in modern software engineering, become necessary mainly for Multi-Agent Systems (MAS), despite obvious adversities. The first sections are typical for a position paper, defending such logics from an anthropocentric perspective. Through this sieve, Section 4 outlines the features asked for by the paradigm of computing as intelligent interaction, based on “nuances of nuanced-reasoning”, that should be reflected by agent logics. To keep the approach credible, Section 5 illustrates how quantifiable synergy can be reached - even in advanced challenging domains, such as stigmergic coordination - by injecting symbolic reasoning in systems based on sub-symbolic “emergent synthesis”. Since for future work too the preferred logics are doxastic, the conclusions could be structured in line with the well-known agent architecture: Beliefs, Desires, Intentions