AntBeePath is a hybrid bio-inspired algorithm based on the behavior of ants and honeybees aimed at the resolution of the problem of finding the shortest paths for a given network topology. The algorithm, in brief, combines the pheromone release mechanism of existing Ant Colony Optimization (ACO) algorithms with a new bio-inspired mechanism based on the recruitment strategy of bees. Three versions of the algorithm were developed incrementally. Proof-of-concept results indicate that the AntBeePath Decay Hybrid Chain version is more efficient than the other developed versions and, beyond that, presented an improved performance in relation to an equivalent ACO algorithm. The results suggest that a hybrid algorithm, combining the ant’s pheromone release with the new bio-inspired mechanism of bee recruitment along with a stagnation control mechanism can result in a new bio-inspired algorithm for path determination with improved characteristics.

In this position paper, we synthesize "within the system" models of human influence over bio-inspired swarms, summarizing observations from previous experiments and identifying methods of influence that have not yet been explored. We describe (a) differences among agents that can be controlled by a human and those that can't, (b) agents that are aware of the type of other agents and those that aren't, and (c) the effects of attraction, repulsion, and orientation on human-guided swarm behavior. We also briefly discuss the interaction effort required to manage swarms.

Knowledge engineering from experimental design (KEfED) is a novel approach based on the dependency relationships that occur between the variables of a scientific study. We used this approach to curate the experimental designs of ten scientific papers from a well-established database of HIV vaccine trials in non-human primates. The KEfED models provide a characteristic, data-oriented signature for each measurement made in the study. We present preliminary analysis of these manually-curated, detailed representations using our own open-source curation tools and show the multi-variate statistical analyses on the resultant models of experimental design. The analyses produced a visualization of the similarities between studies and an account of the dependency relationships across studies. We describe our approach in the context of a knowledge engineering strategy based on creating large-scale domain-independent repositories of experimental observatio

In the context of science, abstract workflows can bridge the gap between scientists and technologists towards using computer systems to carry out scientific processes. Provenance traces provide evidence required to validate scientific products and support their use by others. With abstract workflows and provenance traces based on formal semantics, a knowledge-based framework that merges both technologies are devised, allowing scientists to formally document their processes of data collection and transformation and allowing others to use semantic-based technologies to discover and assess data, processes, and derived data products. This paper presents an approach for evaluating the level of scientist support in frameworks that integrate abstract workflows and provenance traces. In order to support discovery of scientific results, it is essential to provide tools for scientists to document the processes they use to obtain the results. The claim is that the complementary technologies of abstract workflows and provenance traces need to be flexible enough to support a scientist’s perspective and minimize imposition of technically-oriented abstractions that may be extraneous to them. The evaluation approach uses criteria that are derived from tasks performed by scientists using both technologies, i.e., process authoring, process analysis, process interoperability, provenance capturing, provenance analysis, and provenance interoperability.

We explore applicability of the situation calculus, the well-known logical framework developed in Artificial Intelligence for representation of dynamic systems, to the task of representing knowledge about processes, actions and events in the natural sciences. In this paper, we concentrate on a case study in the area of organic chemistry. More specifically, we adapt the situation calculus to the task of automating organic synthesis planning on a qualitative level, where the objective is to identify a chain of chemical reactions transforming the given initial molecules into the desired goal molecule. We present two approaches for reasoning about reactions in organic chemistry: a “micro” approach and a “macro” approach. The “micro” approach is a low level approach that explicitly represents the most elementary interactions between molecules during a single chemical reaction, namely the splitting and forming of bonds between atoms. In contrast, the“macro” approach is a higher level approach that treats each chemical reaction (a set of splits and formation of bonds) as an elementary action. Both approaches are implemented in PROLOG. Declarative heuristics are defined to reduce the search space and help the program to find the correct synthesis routes more quickly. We hope that the lessons learned from our successful case study can have discovery potential in other bio-medical sciences. We discuss briefly how the proposed approaches can contribute to solving other research problems and to communicating pathways.

In this paper, we describe an interactive environment for the representation, interpretation, and revision of explanatory biological models. We illustrate our approach on the systems biology of aging, a complex topic that involves many interacting components, and discuss our experiences using this environment to codify an informal model of aging. We close by discussing related efforts and directions for future research.

As biological data about genes and their interactions proliferates, scientists have the opportunity to identify sets of proteins whose interactions make them worthy of further investigation. This paper reports on a knowledge discovery technique to support that work. Foretell is an algorithm originally designed to support search for solutions to constraint satisfaction problems. Recent adaptations enable Foretell to detect sets of genes that interact heavily with one another. We provide empirical results, and describe ongoing work on biological meaning and knowledge infusion from the user.

Causality discovery has been one of the core tasks in scientific research since the beginning of human scientific history. In the age of data tsunami, the causality discovery task involves identification of causality among millions of variables which cannot be done manually by humans. However, the identification of causality relationships using artificial intelligence and statistical techniques in non-experimental settings faces several challenges. In this work, we address three of the challenges regarding Granger causality, one of the most popular causality inference techniques. First, we analyze the consistency of two most popular Granger causality techniques and show that the significance test is not consistent in high dimensions. Second, we review our nonparametric generalization of the Lasso-Granger technique called Generalized Lasso Granger (GLG) to uncover Granger causality relationships among irregularly sampled time series. Finally, we describe two techniques to uncover the casual dependence in non-linear datasets. Extensive experiments are provided to show the significant advantages of the proposed algorithms over their state-of-the-art counterparts.

Artificial Intelligence researchers have long sought to understand and replicate processes of scientific discovery. This article discusses Discovery Informatics as an emerging area of research that builds on that tradition and applies principles of intelligent computing and information systems to understand, automate, improve, and innovate processes of scientific discovery.

His informatics group built the reusable software platform for Stembook Despite the fact that we now have access to almost all peer reviewed (www.stembook.org), William Cohen exchanged and is orthogonal to any specific biomedical domain The growing size of the scientific literature has led to a number of ontology. We believe this approach will be extremely useful in attempts to automatically extract entities and relationships from drug discovery to break down information silos, increase information scientific papers, and then to populate databases with this extracted awareness and sharing, and integrate terminologies and information. In my group we have been exploring techniques data with documents and text, both public and private. We will for using this sort of extracted information for specific tasks, discuss applications we are currently developing in collaboration including "bootstrapping" to improve the coverage of an extraction with a major pharma.