Language evolution in large populations of autonomous agents:issues in scaling

In this paper we discuss issues relating to modelling language evolution in large populations of au-tonomous agents that are situated in a realistic environment where they have to evolve and learn means to survive for extended periods of time. As we intend to build such a model in relation to the recently started New Ties project, we identify three major problems that are expected for such a model. The paper proposes some solutions and discusses future directions.

Hybrid Genetic Relational Search for Inductive Learning

An important characteristic of all natural systems is the ability to acquire knowledge through experience and to adapt to new situations. Learning is the single unifying theme of all natural systems. One of the basic ways of gaining knowledge is through examples of some concepts.For instance, we may learn how to distinguish a dog from other creatures after that we have seen a number of creatures, and after that someone (a teacher, or supervisor) told us which creatures are dogs and which are not. This way of learning is called supervised learning. Inductive Concept Learning (ICL) constitutes a central topic in machine learning. The problem can be formulated in the following manner: given a description language used to express possible hypotheses, a background knowledge, a set of positive examples, and a set of negative examples, one has to find a hypothesis which covers all positive examples and none of the negative ones. This is a supervised way of learning, since a supervisor has already classified the examples of the concept into positive and negative examples. The so learned concept can be used to classify previously unseen examples. In general deriving general conclusions from specific observation is called induction. Thus in ICL, concepts are induced because obtained from the observation of a limited set of training examples. The process can be seen as a search process. Starting from an initial hypothesis, what is done is searching the space of the possible hypotheses for one that fits the given set of examples. A representation language has to be chosen in order to represent concepts, examples and the background knowledge. This is an important choice, because this may limit the kind of concept we can learn. With a representation language that has a low expressive power we may not be able to represent some problem domain, because too complex for the language adopted. On the other side, a too expressive language may give us the possibility to represent all problem domains. However this solution may also give us too much freedom, in the sense that we can build concepts in too many different ways, and this could lead to the impossibility of finding the right concept. We are interested in learning concepts expressed in a fragment of first--order logic (FOL). This subject is known as Inductive Logic Programming (ILP), where the knowledge to be learn is expressed by Horn clauses, which are used in programming languages based on logic programming like Prolog. Learning systems that use a representation based on first--order logic have been successfully applied to relevant real life problems, e.g., learning a specific property related to carcinogenicity. Learning first--order hypotheses is a hard task, due to the huge search space one has to deal with. The approach used by the majority of ILP systems tries to overcome this problem by using specific search strategies, like the top-down and the inverse resolution mechanism. However, the greedy selection strategies adopted for reducing the computational effort, render techniques based on this approach often incapable of escaping from local optima. An alternative approach is offered by genetic algorithms (GAs). GAs have proved to be successful in solving comparatively hard optimization problems, as well as problems like ICL. GAs represents a good approach when the problems to solve are characterized by a high number of variables, when there is interaction among variables, when there are mixed types of variables, e.g., numerical and nominal, and when the search space presents many local optima. Moreover it is easy to hybridize GAs with other techniques that are known to be good for solving some classes of problems. Another appealing feature of GAs is represented by their intrinsic parallelism, and their use of exploration operators, which give them the possibility of escaping from local optima. However this latter characteristic of GAs is also responsible for their rather poor performance on learning tasks which are easy to tackle by algorithms that use specific search strategies. These observations suggest that the two approaches above described, i.e., standard ILP strategies and GAs, are applicable to partly complementary classes of learning problems. More important, they indicate that a system incorporating features from both approaches could profit from the different benefits of the approaches. This motivates the aim of this thesis, which is to develop a system based on GAs for ILP that incorporates search strategies used in successful ILP systems. Our approach is inspired by memetic algorithms, a population based search method for combinatorial optimization problems. In evolutionary computation memetic algorithms are GAs in which individuals can be refined during their lifetime.Eiben, A.E. [Promotor]Marchiori, E. [Copromotor

Performance of geosynthetics as reinforcement in unpaved roads

O trabalho teve como objetivo avaliar o comportamento de estradas não pavimentadas construídas sobre solos moles reforçadas com diferentes geossintéticos. Foram selecionadas três estradas não pavimentadas que apresentassem um trecho com baixa capacidade de suporte de carga. A flecha foi medida pelo método fotográfico para cada número de passadas, nas condições com e sem reforço, e submetido à análise de variância, pelo teste de F, a 5% de probabilidade, e quando houve diferença significativa entre os tratamentos, suas médias foram comparadas pelo teste de Tukey. Posteriormente, realizou-se o cálculo da razão de benefício de tráfego fornecida pela inclusão do reforço. O geossintético não tecido, quando inserido no interior do solo, contribui de modo significativo para a redução das flechas, principalmente nos solos do trecho das estradas I e III. O geossintético tecido contribui para a redução das flechas, principalmente nos solos do trecho da estrada III. A geogrelha não contribui para a redução das flechas. A razão de benefício de tráfego para os reforços utilizados foram superiores a um, com valores médios de 1,28 para o trecho da estrada I, reforçado com geossintético não tecido, 1,54 para o trecho da estrada II, reforçado com geossintético tecido e de 2,7 para o trecho da estrada III, reforçado com geossintético tecido.The objective of this study was to evaluate the performance of unpaved roads built on soft soils reinforced with different geosynthetic materials. We selected three sections of unpaved roads with low load-bearing capacity. Vertical displacement (rut depth) was visually measured (photographs) for each number of passes on soils with and without reinforcement. The data underwent variance analysis by the F-test at 5% probability; when significant, means were compared by the Tukey’s test. Subsequently, we calculated traffic benefit ratio (TBR) or improvement factor due to the use of each reinforcement material. Non-woven geosynthetic materials reduced significantly the rut depths, mainly within the road sections I and III; by contrast, geosynthetic woven materials contributed in road section III. Moreover, geogrid use had no contribution to rut depth reductions. The TBR by using the reinforcements studied here showed values higher than one, with averages of 1.28 for road section I reinforced with geosynthetic nonwoven material, 1.54 for road section II reinforced with geosynthetic woven material, and 2.7 for road section III reinforced with geosynthetic woven

An Evolutionary Algorithm for Discovering Multi-Relational Association Rules in the Semantic Web

International audienceIn the Semantic Web context, OWL ontologies represent the conceptualization of domains of interest while the corresponding assertional knowledge is given by RDF data referring to them. Because of its open, distributed, and collaborative nature, such knowledge can be incomplete, noisy, and sometimes inconsistent. By exploiting the evidence coming from the assertional data, we aim at discovering hidden knowledge patterns in the form of multi-relational association rules while taking advantage of the intensional knowledge available in ontological knowledge bases. An evolutionary search method applied to populated ontological knowledge bases is proposed for finding rules with a high inductive power. The proposed method, EDMAR, uses problem-aware genetic operators, echoing the refinement operators of ILP, and takes the intensional knowledge into account, which allows it to restrict and guide the search. Discovered rules are coded in SWRL, and as such they can be straightforwardly integrated within the ontology, thus enriching its expressive power and augmenting the assertional knowledge that can be derived. Additionally , discovered rules may also suggest new axioms to be added to the ontology. We performed experiments on publicly available ontologies, validating the performances of our approach and comparing them with the main state-of-the-art systems

On Triangular Secure Domination Number

Let T_m=(V(T_m), E(T_m)) be a triangular grid graph of m ϵ N level. The order of graph T_m is called a triangular number. A subset T of V(T_m) is a dominating set of T_m  if for all u_V(T_m)\T, there exists vϵT such that uv ϵ E(T_m), that is, N[T]=V(T_m).  A dominating set T of V(T_m) is a secure dominating set of T_m if for each u ϵ V(T_m)\T, there exists v ϵ T such that uv ϵ E(T_m) and the set (T\{u})ꓴ{v} is a dominating set of T_m. The minimum cardinality of a secure dominating set of T_m, denoted by γ_s(T_m)  is called a secure domination number of graph T_m. A secure dominating number  γ_s(T_m) of graph T_m is a triangular secure domination number if γ_s(T_m) is a triangular number. In this paper, a combinatorial formula for triangular secure domination number of graph T_m was constructed. Furthermore, the said number was evaluated in relation to perfect numbers

Giardia duodenalis in Captive Tigers (Panthera tigris), Palawan Bearcats (Arctictis binturong whitei) and Asian Palm Civet (Paradoxurus hermaphroditus) at a Wildlife Facility in Manila, Philippines

Background: This study was conducted to determine the prevalence of Giardia duodenalis in captive animals in a wildlife facility. This is the first study conducted in these animals from the facility. Methods: Eight captive tigers (Panthera tigris), two Palawan bearcats (Arctictis binturong whitei) and one Asian Palm Civet (Paradoxurus hermaphroditus) currently housed at a wildlife facility in Manila, Philippines were considered in 2012. These animals were apparently healthy with no signs of disease during the study. Sample collection was done twice at two months interval where freshly voided fecal samples were grossly examined, characterized and preserved in Sodium Acetate Formalin (SAF). The samples were used to determine the presence of G. duodenalis using modified flotation-sedimentation and commercially available immuno-chromatographic assay test kit. Results: All fecal samples tested were negative for the presence of G. duodenalis trophozoites, and cysts using the former. Furthermore, none of the samples tested positive for and G. duodenalis antigen using immune-chromatographic assay. Conclusion: There is no existing infection of G. duodenalis among captive tigers, Palawan Bearcats and Asian palm civet housed at the wildlife facility

Emerging Artificial Societies Through Learning

The NewTies project is implementing a simulation in which societies of agents are expected to de-velop autonomously as a result of individual, population and social learning. These societies are expected to be able to solve environmental challenges by acting collectively. The challenges are in-tended to be analogous to those faced by early, simple, small-scale human societies. This report on work in progress outlines the major features of the system as it is currently conceived within the project, including the design of the agents, the environment, the mechanism for the evolution of language and the peer-to-peer infrastructure on which the simulation runs

Fast Evolutionary Adaptation for Monte Carlo Tree Search

This paper describes a new adaptive Monte Carlo Tree Search (MCTS) algorithm that uses evolution to rapidly optimise its performance. An evolutionary algorithm is used as a source of control parameters to modify the behaviour of each iteration (i.e. each simulation or roll-out) of the MCTS algorithm; in this paper we largely restrict this to modifying the behaviour of the random default policy, though it can also be applied to modify the tree policy

Evolutionary Algorithm Based on New Crossover for the Biclustering of Gene Expression Data

  Microarray represents a recent multidisciplinary technology. It measures the expression levels of several genes under different biological conditions, which allows to generate multiple data. These data can be analyzed through biclustering method to determinate groups of genes presenting a similar behavior under specific groups of conditions. This paper proposes a new evolutionary algorithm based on a new crossover method, dedicated to the biclustering of gene expression data. This proposed crossover method ensures the creation of new biclusters with better quality. To evaluate its performance, an experimental study was done on real microarray datasets. These experimentations show that our algorithm extracts high quality biclusters with highly correlated genes that are particularly involved in specific ontology structure