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This paper describes an algorithm for semantic decomposition. For that we surveys languages used to enrich contextual information with semantic descriptions. Such descriptions can be e.g. applied to enable reasoning when collecting vast amounts of information. In particular, we focus on the elements of the languages that make up their semantic. To do so, we compare the expressiveness of the well-known languages OWL, PDDL and MOF with a theory from linguistic called the Natural Semantic Metalanguage. We then analyze how the semantic of the language is build up and describe how semantic decomposition based on the semantic primes can be used for a so called mental lexicon. This mental lexicon can be used to reason upon semantic service description in the research domain of service match making.

Semantic information is considered as foundation upon which modern approaches attempt to tackle the challenges of dynamic environments - service orchestration and ontology matching are two examples for the use of such information. Yet, many developers avoid the additional effort of adding semantic information (e.g., through annotations) to their data sets - limiting the reusability and interoperability of their Apps, services, or data. This problem is called the "knowledge acquisition bottleneck", which can be addressed by providing suitable tool support. This survey analyses the state-of-the-art of such tools that support developers in the task of semantically enriching entities. Providing an overview of available tools from the early days until now, we particularly focus on the 'level of automation'. Concluding that automation is very limited in contemporary tools we propose a concept that mixes connectionist and symbolic representation of meaning to decrease the manual effort.

PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems.This volume presents the papers that have been accepted for the 2014 special sessions: Agents Behaviours and Artificial Markets (ABAM), Agents and Mobile Devices (AM), Bio-Inspired and Multi-Agents Systems: Applications to Languages (Bio MAS), Multi-Agent Systems and Ambient Intelligence (MASMAI), Self-Explaining Agents (SEA), Web Mining and Recommender systems (Web MiRes) and Intelligent Educational Systems (SSIES).

This work advocate self-explanation as one foundation of self-* properties. Arguing that for system component to become more self-explanatory the underlining foundation is an awareness of themselves and their environment. In the research area of adaptive software, self-* properties have shifted into focus caused by the tendency to push ever more design decisions to the applications runtime. Thus fostering new paradigms for system development like intelligent and learning agents. This work surveys the state-of-the-art methods of self-explanation in software systems and distills a definition of self-explanation. Additionally, we introduce a measure to compare explanations and propose an approach for the first steps towards extending descriptions to become more explanatory. The conclusion shows that explanation is a special kind of description. The kind of description that provides additional information about a subject of interest and is understandable for the audience of the explanation. Further the explanation is dependent on the context it is used in, which brings about that one explanation can transport different information in different contexts. The proposed measure reflects those requirements.

Currently, there are many models available which can be used to describe a driver's behaviour for a traffic simulation. Despite the number of available formalisms it is our opinion that existing approaches neglect the interplay between the simulation topology and strategic decisions of simulated drivers. Existing models either disregard strategy updates or focus on short-term strategies only. In this paper we tackle this problem and propose a model which incorporates (long-term) strategic reactions of simulated drivers to influences of the surrounding infrastructure. In doing so, we formalise a certain type of human behaviour which is commonly known as strategic-level compensation.