A tailored model of a system is the prerequisite for various analysis tasks, such as anomaly detection, fault identification, or quality assurance. This paper deals with the algorithmic learning of a system’s behavior model given a sample of observations. In particular, we consider real-world production plants where the learned model must capture timing behavior, dependencies between system variables, as well as mode switches—in short: hybrid system’s characteristics. Usually, such model formation tasks are solved by human engineers, entailing the well-known bunch of problems including knowledge acquisition, development cost, or lack of experience. Our contributions to the outlined field are as follows. (1) We present a taxonomy of learning problems related to model formation tasks. As a result, an important open learning problem for the domain of production system is identified: The learning of hybrid timed automata. (2) For this class of models, the learning algorithm HyBUTLA is presented. This algorithm is the first of its kind to solve the underlying model formation problem at scalable precision. (3) We present two case studies that illustrate the usability of this approach in realistic settings. (4) We give a proof for the learning and runtime properties of HyBUTLA.

Current Coreference Resolution systems utilize a broad range of general knowledge features to make resolutions in a general setting. These approaches ignore coreference knowledge found in domain specific collections and how coreferent entities interact in different domains. This research addresses these issues by developing knowledge bases of coreference characteristics drawn from annotated and unannotated domain texts and utilizing lexical and discourse information to improve resolution.

Humans currently use arguments for explaining choices which are already made, or for evaluating potential choices. Each potential choice has usually pros and cons of various strengths. In spite of the usefulness of arguments in a decision making process, there have been few formal proposals handling this idea if we except works by Fox and Parsons and by Bonet and Geffner. In this paper we propose a possibilistic logic framework where arguments are built from an uncertain knowledge base and a set of prioritized goals. The proposed approach can compute two kinds of decisions by distinguishing between pessimistic and optimistic attitudes. When the available, maybe uncertain, knowledge is consistent, as well as the set of prioritized goals (which have to be fulfilled as far as possible), the method for evaluating decisions on the basis of arguments agrees with the possibility theory-based approach to decision-making under uncertainty. Taking advantage of its relation with formal approaches to defeasible argumentation, the proposed framework can be generalized in case of partially inconsistent knowledge, or goal bases.

In this paper, we discuss the implementation of a rule based expert system for diagnosing neuromuscular diseases. The proposed system is implemented as a rule based expert system in JESS for the diagnosis of Cerebral Palsy, Multiple Sclerosis, Muscular Dystrophy and Parkinson's disease. In the system, the user is presented with a list of questionnaires about the symptoms of the patients based on which the disease of the patient is diagnosed and possible treatment is suggested. The system can aid and support the patients suffering from neuromuscular diseases to get an idea of their disease and possible treatment for the disease.

The ways in which an agent's actions affect the world can often be modeled compactly using a set of relational probabilistic planning rules. This paper addresses the problem of learning such rule sets for multiple related tasks. We take a hierarchical Bayesian approach, in which the system learns a prior distribution over rule sets. We present a class of prior distributions parameterized by a rule set prototype that is stochastically modified to produce a task-specific rule set. We also describe a coordinate ascent algorithm that iteratively optimizes the task-specific rule sets and the prior distribution. Experiments using this algorithm show that transferring information from related tasks significantly reduces the amount of training data required to predict action effects in blocks-world domains.

Machine reading aims at extracting formal knowledge representations from text to enable programs to execute some performance task, for example, diagnosis or answering complex queries stated in a formal representation language. Information extraction techniques are a natural starting point for machine reading, however, since they focus on explicit surface features at the phrase and sentence level, they generally miss information only stated implicitly. Moreover, the combination of multiple extraction results leads to error compounding which dramatically affects extraction quality for composite structures. To address these shortcomings, we present a new approach which aggregates locally extracted information into a larger story context and uses abductive constraint reasoning to generate the best story-level interpretation. We demonstrate that this approach significantly improves formal question answering performance on complex questions.

The second was the propensity of the computing industry toward more lucrative assignments in the service sector. Both these factors are changing, not least because leading international software companies have set up research and development centers in the country. Computer science education established itself in India in the early 1980s when the Indian Institutes of Technology (IITs) set up computer science departments and started offering undergraduate programs in the discipline. Research in artificial intelligence took off soon afterward when the government of India launched the Knowledge Based Computing Systems (KBCS) program in conjunction with the United Nations Development Program (Saint-Dizier 1991). A number of nodal centers were set up to focus on different areas of research including expert systems (IIT Madras), speech processing (Tata Institue of Fundamental Research), parallel processing (Indian Institute for Science), image processing (Indian Statistical Institute), and natural language processing (Center for Development of Advanced Computing).

ICINCO 2012 will be held July 28-31, 2012 in Rome, Italy This page includes forthcoming AAAI sponsored conferences, conferences presented Sixth International RuleML Symposium by AAAI Affiliates, and conferences held in cooperation with AAAI. RuleML-2012 will be Magazine also maintains a calendar listing that includes nonaffiliated conferences held August 27-31, 2012 in Montpellier, at www.aaai.org/Magazine/calendar.php. Knowledge Engineering and Knowledge ICWSM-12 will be held June 4-7 at Flairs-2012 will be held May 23-25, Management. AAAI-12 will be Representation and Reasoning. Twenty-Fourth Innovative Applications Twenty-Second International Conference of Artificial Intelligence Conference. on Automated Planning and IAAI-12 will be held July Scheduling.

This paper develops an inconsistency measure on conditional probabilistic knowledge bases. The measure is based on fundamental principles for inconsistency measures and thus provides a solid theoretical framework for the treatment of inconsistencies in probabilistic expert systems. We illustrate its usefulness and immediate application on several examples and present some formal results. Building on this measure we use the Shapley value-a well-known solution for coalition games-to define a sophisticated indicator that is not only able to measure inconsistencies but to reveal the causes of inconsistencies in the knowledge base. Altogether these tools guide the knowledge engineer in his aim to restore consistency and therefore enable him to build a consistent and usable knowledge base that can be employed in probabilistic expert systems.

Based on World Health Organization (WHO) fact sheet in the 2011, outbreaks of poultry diseases especially Avian Influenza in poultry may raise global public health concerns due to their effect on poultry populations, their potential to cause serious disease in people, and their pandemic potential. In this research, we built a Poultry Diseases Expert System using Dempster-Shafer Theory. In this Poultry Diseases Expert System We describe five symptoms which include depression, combs, wattle, bluish face region, swollen face region, narrowness of eyes, and balance disorders. The result of the research is that Poultry Diseases Expert System has been successfully identifying poultry diseases.