Community structure identification has been one of the most popular research areas in recent years due to its applicability to the wide scale of disciplines. To detect communities in varied topics, there have been many algorithms proposed so far. However, most of them still have some drawbacks to be addressed. In this paper, we present an agent-based based community detection algorithm. The algorithm that is a stochastic one makes use of agents by forcing them to perform biased moves in a smart way. Using the information collected by the traverses of these agents in the network, the network structure is revealed. Also, the network modularity is used for determining the number of communities. Our algorithm removes the need for prior knowledge about the network such as number of the communities or any threshold values. Furthermore, the definite community structure is provided as a result instead of giving some structures requiring further processes. Besides, the computational and time costs are optimized because of using thread like working agents. The algorithm is tested on three network data of different types and sizes named Zachary karate club, college football and political books. For all three networks, the real network structures are identified in almost every run.

We show how solution concepts in games such as Nash equilibrium, correlated equilibrium, rationalizability, and sequential equilibrium can be given a uniform definition in terms of \emph{knowledge-based programs}. Intuitively, all solution concepts are implementations of two knowledge-based programs, one appropriate for games represented in normal form, the other for games represented in extensive form. These knowledge-based programs can be viewed as embodying rationality. The representation works even if (a) information sets do not capture an agent's knowledge, (b) uncertainty is not represented by probability, or (c) the underlying game is not common knowledge.

We provide an overview of some recent progress on the complexity of election systems. The issues studied include the complexity of the winner, manipulation, bribery, and control problems.

In this paper, we study the construction and transformation of two-dimensional persistent graphs. Persistence is a generalization to directed graphs of the undirected notion of rigidity. In the context of moving autonomous agent formations, persistence characterizes the efficacy of a directed structure of unilateral distances constraints seeking to preserve a formation shape. Analogously to the powerful results about Henneberg sequences in minimal rigidity theory, we propose different types of directed graph operations allowing one to sequentially build any minimally persistent graph (i.e. persistent graph with a minimal number of edges for a given number of vertices), each intermediate graph being also minimally persistent. We also consider the more generic problem of obtaining one minimally persistent graph from another, which corresponds to the on-line reorganization of an autonomous agent formation. We prove that we can obtain any minimally persistent formation from any other one by a sequence of elementary local operations such that minimal persistence is preserved throughout the reorganization process.

In this paper we present a deeper analysis than has previously been carried out of a selective attention problem, and the evolution of continuous-time recurrent neural networks to solve it. We show that the task has a rich structure, and agents must solve a variety of subproblems to perform well. We consider the relationship between the complexity of an agent and the ease with which it can evolve behavior that generalizes well across subproblems, and demonstrate a shaping protocol that improves generalization.

Artificial Intelligence (AI) has recently become a real formal science: the new millennium brought the first mathematically sound, asymptotically optimal, universal problem solvers, providing a new, rigorous foundation for the previously largely heuristic field of General AI and embedded agents. At the same time there has been rapid progress in practical methods for learning true sequence-processing programs, as opposed to traditional methods limited to stationary pattern association. Here we will briefly review some of the new results, and speculate about future developments, pointing out that the time intervals between the most notable events in over 40,000 years or 2^9 lifetimes of human history have sped up exponentially, apparently converging to zero within the next few decades. Or is this impression just a by-product of the way humans allocate memory space to past events?

This paper discusses the systematic use of product feedback information to support life-cycle design approaches and provides guidelines for developing a design at both the product and the system levels. Design activities are surveyed in the light of the product life cycle, and the design information flow is interpreted from a semiotic perspective. The natural evolution of a design is considered, the notion of design expectations is introduced, and the importance of evaluation of these expectations in dynamic environments is argued. Possible strategies for reconciliation of the expectations and environmental factors are described. An Internet-enabled technology is proposed to monitor product functionality, usage, and operational environment and supply the designer with relevant information. A pilot study of assessing design expectations of a refrigerator is outlined, and conclusions are drawn.

E-learning is nowadays one of the most interesting of the "e- " domains available through the Internet. The main problem to create a Web-based, virtual environment is to model the traditional domain and to implement the model using the most suitable technologies. We analyzed the distance learning domain and investigated the possibility to implement some e-learning services using mobile agent technologies. This paper presents a model of the Student Assessment Service (SAS) and an agent-based framework developed to be used for implementing specific applications. A specific Student Assessment application that relies on the framework was developed.

The se s tructur es were embod ie d into a comprehensive agent be havi oral model shaped on t op of a unifying framework. By means of s uch a fr amework we managed to make the agent p la tform trans pare nt to the us er and, in the same time, deco uple the re us able patterns from the under lying mobile agent pl atfo rm. It thus beco mes cl ear that the model was s tructur ed to be highly indepe nd ent, encompas sing a handful of abst ract featur es that a llo w it to be eq ually expres sive re gardle s s of th e underlying agent suppor t . Enti ties common to eve ry agent p la tfor m (location, agent, mes s age, behavior, agent identifier along with ot her relevant ones) provi d e the cont ext within which we were able to d efine the reus ab le patterns . The s e patterns prod uc e an environment that ultimately sep arate s the behavi oral model from the a ctual s keleton upon which the pat ters are enacted (i.e. the J ADE agent plat fo rm) and, as s uch, once they are c re ated, rewriting them will not be necessary for every new p la tfor m. Simply put, one has onl y to write new ada p te rs if needed, or us e the avail able ones a long with the alread y exi s ting framewo rk items to integrate (coale sce) the compon ent sh e req ui res. Adapters were employed t o p rovi de the bridge between the framework and agent p la tfor ms .

The Unified Software Development Process (USDP) and UML have been now generally accepted as the standard methodology and modeling language for developing Object-Oriented Systems. Although Agent-based Systems introduces new issues, we consider that USDP and UML can be used in an extended manner for modeling Agent-based Systems. The paper presents a methodology for designing agent-based systems and the specific models expressed in an UML-based notation corresponding to each phase of the software development process. UML was extended using the provided mechanism: stereotypes. Therefore, this approach can be managed with any CASE tool supporting UML. A Case Study, the development of a specific agent-based Student Evaluation System (SAS), is presented.