Knowledge acquisition is the essential process of extracting and encoding knowledge, both domainspecific and commonsense, to be used in intelligent systems. While many large knowledge bases have been constructed, none is close to complete. This paper presents an approach to improving a knowledge base efficiently under resource constraints. Using a guiding knowledge base, questions are generated from a weak form of similarity-based inference given the glossary mapping between two knowledge bases. The candidate questions are prioritized in terms of the concept coverage of the target knowledge. Experiments were conducted to find questions to grow the Chinese ConceptNet using the English ConceptNet as a guide. The results were evaluated by online users to verify that 94.17% of the questions and 85.77% of the answersare good. In addition, the answers collected in a six-week period showed consistent improvement to a 36.33% increase in concept coverage of the Chinese commonsense knowledge base against the English ConceptNet.

We consider a generalization of instance retrieval over knowledge bases that provides users with assertions in which descriptions of qualifying objects are given in addition to their identifiers. Notably, this involves a transfer of basic database paradigms involving caching and query rewriting in the context of an assertion retrieval algebra. We present an optimization framework for this algebra, with a focus on finding plans that avoid any need for general knowledge base reasoning at query execution time when sufficient cached results of earlier requests exist.

The Semantic Web aims to offer an interoperable environment that will allow users to safely delegate complex actions to intelligent agents. Much work has been done for agents' interoperability; especially in the areas of ontology-based metadata and rule-based reasoning. Nevertheless, the SW proof layer has been neglected so far, although it is vital for agents and humans to understand how a result came about, in order to increase the trust in the interchanged information. This paper focuses on the implementation of third party SW reasoning and proofing services wrapped as agents in a multi-agent framework. This way, agents can exchange and justify their arguments without the need to conform to a common rule paradigm. Via external reasoning and proofing services, the receiving agent can grasp the semantics of the received rule set and check the validity of the inferred results.

The Partner Units Problem is a specific type of configuration problem with important applications in the area of surveillance and security. In this work we show that a special case of the problem, that is of great interest to our partners in industry, can directly be tackled via a structural problem decompostion method. Combining these theoretical insights with general purpose AI techniques such as constraint satisfaction and SAT solving proves to be particularly effective in practice.

Willow is a free-text Adaptive Computer Assisted Assessment system, which supports natural language processing and user modeling. In this paper we discuss the benefits coming from extending Willow with recommendations. The approach combines human computer interaction methods to elicit the recommendations with data mining techniques to adjust their definition. Following a scenario-based approach, 12 recommendations were designed and delivered in a large scale evaluation with 377 learners. A statistically significant positive impact was found on indicators dealing with the engagement in the course, the learning effectiveness and efficiency, as well as the knowledge acquisition. We present the overall system functionality, the interaction among the different subsystems involved and some evaluation findings.

Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria.

We present a description of the PhD thesis which aims to propose a rule-based query answering method for relational data. In this approach we use an additional knowledge which is represented as a set of rules and describes the source data at concept (ontological) level. Queries are posed in the terms of abstract level. We present two methods. The first one uses hybrid reasoning and the second one exploits only forward chaining. These two methods are demonstrated by the prototypical implementation of the system coupled with the Jess engine. Tests are performed on the knowledge base of the selected economic crimes: fraudulent disbursement and money laundering.

Informledge System (ILS) is a knowledge network with autonomous nodes and intelligent links that integrate and structure the pieces of knowledge. In this paper, we put forward the strategies for knowledge embedding and retrieval in an ILS. ILS is a powerful knowledge network system dealing with logical storage and connectivity of information units to form knowledge using autonomous nodes and multi-lateral links. In ILS, the autonomous nodes known as Knowledge Network Nodes (KNN)s play vital roles which are not only used in storage, parsing and in forming the multi-lateral linkages between knowledge points but also in helping the realization of intelligent retrieval of linked information units in the form of knowledge. Knowledge built in to the ILS forms the shape of sphere. The intelligence incorporated into the links of a KNN helps in retrieving various knowledge threads from a specific set of KNNs. A developed entity of information realized through KNN forms in to the shape of a knowledge cone

Cancer kills millions of people each year. From an AI perspective, finding effective treatments for cancer is a high-dimensional search problem characterized by many molecularly distinct cancer subtypes, many potential targets and drug combinations, and a dearth of high quality data to connect molecular subtypes and treatments to responses. The broadening availability of molecular diagnostics and electronic medical records, presents both opportunities and challenges to apply AI techniques to personalize and improve cancer treatment. We discuss these in the context of Cancer Commons, a “rapid learning” community where patients, physicians, and researchers collect and analyze the molecular and clinical data from every cancer patient, and use these results to individualize therapies. Research opportunities include: adaptively-planning and executing individual treatment experiments across the whole patient population, inferring the causal mechanisms of tumors, predicting drug response in individuals, and generalizing these findings to new cases. The goal is to treat each patient in accord with the best available knowledge, and to continually update that knowledge to benefit subsequent patients. Achieving this goal is a worthy grand challenge for AI.

A geoscientist would be faced with the situation shown on the right of the figure; his task is to deduce the situation shown at the left, along with the processes that were at work and the timeline involved. To accomplish this, a geoscientist first dates a set of rock samples from the present surface, then reasons backward to deduce what process affected the original landform. This is a difficult deduction: geological processes take place over an extremely long period of time, and evidence remaining today is scarce and noisy. Finally, experts in geological dating, like experts in any field, are only human, and can be biased in favor of one theory over another. In the face of these problems, experts form an exhaustive list of possible hypotheses and consider the evidence for and against each one--much like the AI concept of argumentation. Our system to automate this reasoning, Calvin, uses the same argumentation process as experts, comparing the strength of the evidence for and against a set of hypotheses before coming to a conclusion. We collected knowledge about how isotope dating experts reason through interviews with several dozen geoscientists.