The increase in the number of complex temporal datasets collected today has prompted the development of methods that extend classical machine learning and data mining methods to time-series data.  This work focuses on methods for multivariate time-series classification. Time series classification is a challenging problem mostly because the number of temporal features that describe the data and are potentially useful for classification is enormous. We study and develop a temporal abstraction framework for generating multivariate time series features suitable for classification tasks. We propose the STF-Mine algorithm that automatically mines discriminative temporal abstraction patterns from the time series data and uses them to learn a classification model. Our experimental evaluations, carried out on both synthetic and real world medical data, demonstrate the benefit of our approach in learning accurate classifiers for time-series datasets.

This paper presents an approach to creating flexible general-logic representations from language for use in high-level reasoning tasks in cognitive modeling.  These representations are grounded in a large-scale ontology and emphasize the need for semantic breadth at the cost of syntactic breadth.  The task-independent interpretation process allows task-specific pragmatics to guide the interpretation process. In the context of a particular cognitive model, we discuss our use of limited abduction for interpretation and show results of its performance.

Collaboration between research scientists, particularly those with diverse backgrounds, is a driver of scientific innovation. However, finding the right collaborator is often an unscientific process that is subject to chance. This paper explores recommending collaborators based on repeating patterns of previous successful collaboration experiences, what we term prototypical collaborations. We investigate a method for discovering such prototypes to use them as a basis to guide the recommendation of new collaborations. To this end, we also examine two methods for matching collaboration seekers to these prototypical collaborations. Our initial studies reveal that though promising, improving collaborations through recommendation is a complex goal.

Data mining is a field of research dedicated to the process of extracting underlying patterns in data collections. The FLAIRS special track on data mining has the goal of presenting new and important contributions to this field. Areas of interest include, but are not limited to, applications such as intelligence analysis, medical and health applications, text, video, and multimedia mining, e-commerce and web data, financial data analysis, intrusion detection, remote sensing, earth sciences, and astronomy; modeling algorithms such as hidden Markov, decision trees, neural networks, statistical methods, or probabilistic methods; case studies in areas of application, or over different algorithms and approaches; feature extraction and selection; post-processing techniques such as visualization, summarization, or trending; preprocessing and data reduction; data engineering or warehousing; or other data mining research that is related to artificial intelligence.

Recently, the development of distributed knowledge systems has become more attractive due to the existence of new social semantic applications such as semantic knowledge wikis. User-friendly tools like wikis allow for a simple acquisition of formal knowledge, but also pose new challenges in knowledge engineering. In this paper, we reconsider classic criteria for verification in the light of a distributed knowledge base and we discuss novel anomalies that possibly occur during the collaborative development of a distributed knowledge base.

This paper introduces a qualitative ranking function that uses signed integers to describe the surprise associated with the occurrence of events. The measure introduced, kappa++, is based on the kappa calculus but differs from it in that its semantics enable an explicit representation of complements. As a result, the kappa++ is more capable of enforcing probability theory-like constraints to carry on reasoning.

This volume contains the papers presented at the 22nd International FLAIRS Conference (FLAIRS-22) held 19-21 May 2009 on Sanibel Island, Florida, USA. The call for papers attracted 158 paper submissions, 40 to the general conference and 118 to the 10 special tracks. Over 80 percent of the papers were reviewed by at least four reviewers, and all papers by at least three. Reviewing was coordinated by the program committees of the general conference and the special tracks. The program committees finally accepted the 85 papers that appear in these proceedings, all as presented papers (21 from the general conference and 64 from the special tracks) and 29 as poster papers (6 from the general conference and 23 from the special tracks).

In our experiments with four well-known systems for solving partially observable planning problems  (Contingent-FF, MBP, PKS, and POND), we were greatly surprised to find that they could only solve problems with a small number of contingencies. Apparently they were repeatedly trying to solve many combinations of contingencies at once, thus unnecessarily using up huge amounts of time and space. This difficulty can be alleviated if the planner can maintain focus on the contingency that it is currently trying to solve. We provide a way to accomplish this by incorporating focusing information directly into the planning domain's operators, without any need to modify the planning algorithm itself. This enables the above planners to solve larger problems and to solve them much more quickly. We also provide a new planner, FOCUS, in which focusing information can be provided as a separate input. This provides even better performance by allowing the planner to utilize more extensive focusing information.

Evaluating changes in documentation of technical products is a key issue in knowledge management. A product may be declined in different versions and one way to evaluate changes is to compare the sets of documents which describe each version. The aim of this paper is to propose a framework for exhibiting changes between sets of documents. This framework is based on the representation of the sets of documents in terms of association rules and on the definition of first order predicates for reasoning with these association rules. The aim of the reasoning stage is to exhibit the differences between the sets of documents. These predicates show what rules are specific to a corpus or how differs the usage of concepts appearing in the associations rules. The framework is  experimented with the comparison of two corpuses of documents which describe documentation about two different versions of a spatial component.

One of the most salient subfields of AI is computational linguistics, which includes its applied branch - natural language processing (NLP). Computational linguistics is a subfield of AI, developing methods and algorithms for all the aspects of language analysis and their computer implementation. We can see language analysis split into two parts: the theoretic analysis and the applicative one. The theoretic aspect includes standard levels considered in linguistics: semantics, syntax, and morphology. Semantic theories have to be a guide of syntactical theories and morphological developments.