This paper presents a plan recognition algorithm for inferring student behavior using virtual science laboratories. The algorithm extends existing plan recognition technology and was integrated with an existing educational application for chemistry. Automatic recognition of students’ activities in virtual laboratories can provide important information to teachers as well as serve as the basis for intelligent tutoring. Student use of virtual laboratories presents several challenges: Students may repeat activities indefinitely, interleave between activities, and engage in exploratory behavior using trial-anderror. The plan recognition algorithm uses a recursive grammar that heuristically generates plans on the fly, taking into account chemical reactions and effects to determine students’ intended high-level actions. The algorithm was evaluated empirically on data obtained from college students using virtual laboratory software for teaching chemistry. Results show that the algorithm was able to (1) infer the plans used by students to construct their models; (2) recognize such key processes as titration and dilution when they occurred in students’ work; (3) identify partial solutions; (4) isolate sequences of actions that were part of a single error.

Deliberation itself can be a computationally costly process and requires This paper shows how belief revision techniques an appropriate intention reconsideration policy which can be used in Defeasible Logic to change rulebased helps the agent to deliberate only when necessary. In this picture, theories characterizing the deliberation process it is still overlooked the problem of changing intentions of cognitive agents. We discuss intention reconsideration not because of the change of beliefs, but because the normative as a strategy to make agents compliant constraints require to do so.

Extracting the relations that exist between two entities is an important step in numerousWeb-related tasks such as information extraction.A supervised relation extraction system that is trained to extract a particular relation type might not accurately extract a new type of a relation for which it has not been trained.However, it is costly to create training data manually for every new relation type that one might want to extract.We propose a method to adapt an existing relation extraction system to extractnew relation types with minimum supervision. Our proposed method comprises two stages: learning a lower-dimensional projection between different relations, and learning a relational classifier for the target relation type with instance sampling. We evaluate the proposed method using a dataset that contains 2000 instances for 20 different relation types. Our experimental results show that the proposed method achieves a statistically significant macro-average F-score of 62.77. Moreover, the proposed method outperforms numerous baselines and a previously proposed weakly-supervised relation extraction method.

The paper describes a method of relation extraction, which is based on parsing the input text using a combination of a generic HPSG-based grammar and a highly focused domain- and relation-specific lexicon. We also show a method of unsupervised acquisition of such a lexicon from a large unlabeled corpus. Together, the methods introduce a novel approach to the “Open IE” task, which is superior in accuracy and in quality of relation identification to the existing approaches.

We review recent results on inferencing for SROEL(×), a description logic that subsumes the main features of the W3C recommendation OWL EL. Rule-based deduction systems are developed for various reasoning tasks and logical sublanguages. Certain feature combinations lead to increased space upper bounds for materialisation, suggesting that efficient implementations are easier to obtain for suitable fragments of OWL EL.

The paper describes a method of relation extraction, which is based on parsing the input text using a combination of a generic HPSG-based grammar and a highly focused domain- and relation-specific lexicon. We also show a method of unsupervised acquisition of such a lexicon from a large unlabeled corpus. Together, the methods introduce a novel approach to the “Open IE” task, which is superior in accuracy and in quality of relation identification to the existing approaches.

Information retrieval may suggest a document, and information extraction may tell us what it says, but which information sources do we trust and which assertions do we believe when different authors make conflicting claims? Trust algorithms known as fact-finders attempt to answer these questions, but consider only which source makes which claim, ignoring a wealth of background knowledge and contextual detail such as the uncertainty in the information extraction of claims from documents, attributes of the sources, the degree of similarity among claims, and the degree of certainty expressed by the sources. We introduce a new, generalized fact-finding framework able to incorporate this additional information into the fact-finding process. Experiments using several state-of-the-art fact-finding algorithms demonstrate that generalized fact-finders achieve significantly better performance than their original variants on both semi-synthetic and real-world problems.

In weighted voting games, each agent has a weight, and a coalition of players is deemed to be winning if its weight meets or exceeds the given quota. An agent's power in such games is usually measured by her Shapley value, which depends both on the agent's weight and the quota. [Zuckerman et. al., 2008] show that one can alter a player's power significantly by modifying the quota, and investigate some of the related algorithmic issues. In this paper, we answer a number of questions that were left open by [Zuckerman et. al., 2008]: we show that, even though deciding whether a quota maximizes or minimizes an agent's Shapley value is coNP-hard, finding a Shapley value-maximizing quota is easy. Minimizing a player's power appears to be more difficult. However, we propose and evaluate a heuristic for this problem, which takes into account the voter's rank and the overall weight distribution. We also explore a number of other algorithmic issues related to quota manipulation.

We propose a nonmonotonic extension of low complexity Description Logics EL⊥ and DL-Litecore for reasoning about typicality and defeasible properties. The resulting logics are called EL⊥ T min and DL-Litec T min . Concerning DL-Litec T min , we prove that entailment is in \Pi^p_2. With regard to EL⊥ T min , we first show that entailment remains EXPTIME-hard. Next we consider the known fragment of Left Local EL⊥ T min and we prove that the complexity of entailment drops to \Pi^p_2.

Most of the text mining tasks, such as clustering, is dominated by statistical approaches that treat text as a bag of words. Semantics in the text is largely ignored in the mining process, and the mining results are often not easily interpretable. One particular challenge faced by such approaches is short text understanding, as short text lacks enough content from which a statistical conclusion can be drawn. For example, traditional topic analysis methods consider topic segments with tens of hundreds of words. Latent topic modeling, such as latent Dirichlet allocation, also requires sufficient words to infer document topic distribution. We enhance machine learning algorithms by first giving the machine a probabilistic knowledgebase that contains as big, rich, and consistent concepts (of worldly facts) as those in our mental world. Then a Bayesian inference mechanism is developed to conceptualize words and short text. We conducted comprehensive tests of our method on conceptualizing set of text terms, as well as clustering Twitter messages (tweets), which are typically approximately ten words long. Compared to latent semantic topic modeling and other four kinds of methods that using WordNet, Freebase and Wikipedia (category links and explicit semantic analysis), we show significant improvements in terms of tweets clustering accuracy.