WordNet is an online lexical resource which expresses unique concepts in a language. English WordNet is the first WordNet which was developed at Princeton University. Over a period of time, many language WordNets were developed by various organizations all over the world. It has always been a challenge to store the WordNet data. Some WordNets are stored using file system and some WordNets are stored using different database models. In this paper, we present the World WordNet Database Structure which can be used to efficiently store the WordNet information of all languages of the World. This design can be adapted by most language WordNets to store information such as synset data, semantic and lexical relations, ontology details, language specific features, linguistic information, etc. An attempt is made to develop Application Programming Interfaces to manipulate the data from these databases. This database structure can help in various Natural Language Processing applications like Multilingual Information Retrieval, Word Sense Disambiguation, Machine Translation, etc.

Narrative generation represents an application domain for AI planning where plan quality is related to properties such as shape of plan trajectory. In our work we have developed a plan-based approach to narrative generation that uses character relationships as a key determinant in controlling plan shape (relationships are key in genres such as serial dramas and soaps). Our approach is implemented in a demonstration Interactive Narrative, called NetworkING, set in the medical drama genre. The system features a user-friendly mechanism for specifying relationships between virtual characters, via a social network and real-time visualisation of generated narratives on a 3D stage.

Two other algorithms require the knowledge Markov Decision Processes (MDPs) offer a general framework of the optimal policy and its expected reward. We show to describe probabilistic planning problems of varying that the expected reward of the optimal policy is a lower complexity. The development of algorithms that act successfully bound for the expected performance of both strategies. in MDPs is important to many AI applications. Our final algorithm switches between the application of Since it is often impossible or intractable to evaluate MDP the optimal policy and the policy with the highest possible algorithms based on a theoretical analysis alone, the International outcome, which can be computed without notable overhead Probabilistic Planning Competition (IPPC) was introduced in the Trial-based Heuristic Tree Search (THTS) framework to allow a comparison based on experimental evaluation. (Keller and Helmert 2013). We show theoretically and empirically The idea is to approximate the quality of an MDP that all algorithms outperform the naïve base approach solver by performing a sequence of runs on a problem instance, that ignores the potential of optimizing evaluation and by using the average of the obtained results as runs in hindsight, and that it pays off to take suboptimal base an approximation of the expected reward.

We examine the inferential complexity of Bayesian networks specified through logical constructs. We first consider simple propositional languages, and then move to relational languages. We examine both the combined complexity of inference (as network size and evidence size are not bounded) and the data complexity of inference (where network size is bounded); we also examine the connection to liftability through domain complexity. Combined and data complexity of several inference problems are presented, ranging from polynomial to exponential classes.

The paper proposes a condition for preserving the KD45 property of a Kripke model when a sequence of update models is applied to it. The paper defines the notions of a primitive update model and a semi-reflexive KD45 (or sr-KD45) Kripke model. It proves that updating a sr-KD45 Kripke model using a primitive update model results in a sr-KD45 Kripke model, i.e., a primitive update model preserves the properties of a sr-KD45 Kripke model. It shows that several update models for modeling well-known actions found in the literature are primitive. This result provides guarantees that can be useful in presence of multiple applications of actions in multi-agent system (e.g., multi-agent planning).

Pruning techniques have recently been shown to speed up search algorithms by reducing the branching factor of large search spaces. One such technique is sleep sets, which were originally introduced as a pruning technique for model checking, and which have recently been investigated on a theoretical level for planning. In this paper, we propose a generalization of sleep sets and prove its correctness. While the original sleep sets were based on the commutativity of operators, generalized sleep sets are based on a more general notion of operator sequence redundancy. As a result, our approach dominates the original sleep sets variant in terms of pruning power. On a practical level, our experimental evaluation shows the potential of sleep sets and their generalizations on a large and common set of planning benchmarks.

Datalog-based systems often materialise all consequences of a datalog program and the data, allowing users' queries to be evaluated directly in the materialisation. This process, however, can be computationally intensive, so most systems update the materialisation incrementally when input data changes. We argue that existing solutions, such as the well-known Delete/Rederive (DRed) algorithm, can be inefficient in cases when facts have many alternate derivations. As a possible remedy, we propose a novel Backward/Forward (B/F) algorithm that tries to reduce the amount of work by a combination of backward and forward chaining. In our evaluation, the B/F algorithm was several orders of magnitude more efficient than the DRed algorithm on some inputs, and it was never significantly less efficient.

Peer-to-peer lending is a new highly liquid market for debt, which is rapidly growing in popularity. Here we consider modelling market rates, developing a non-linear Gaussian Process regression method which incorporates both structured data and unstructured text from the loan application. We show that the peer-to-peer market is predictable, and identify a small set of key factors with high predictive power. Our approach outperforms baseline methods for predicting market rates, and generates substantial profit in a trading simulation.

Management of Parkinson's Disease (PD) could be improved significantly if reliable, objective information about fluctuations in disease severity can be obtained in ecologically valid surroundings such as the private home. Although automatic assessment in PD has been studied extensively, so far no approach has been devised that is useful for clinical practice. Analysis approaches common for the field lack the capability of exploiting data from realistic environments, which represents a major barrier towards practical assessment systems. The very unreliable and infrequent labelling of ambiguous, low resolution movement data collected in such environments represents a very challenging analysis setting, where advances would have significant societal impact in our ageing population. In this work we propose an assessment system that abides practical usability constraints and applies deep learning to differentiate disease state in data collected in naturalistic settings. Based on a large data-set collected from 34 people with PD we illustrate that deep learning outperforms other approaches in generalisation performance, despite the unreliable labelling characteristic for this problem setting, and how such systems could improve current clinical practice.

Timed Failure Propagation Graphs (TFPGs) are a formalism used in industry to describe failure propagation in a dynamic partially observable system. TFPGs are commonly used to perform model-based diagnosis. As in any model-based diagnosis approach, however, the quality of the diagnosis strongly depends on the quality of the model. Approaches to certify the quality of the TFPG are limited and mainly rely on testing. In this work we address this problem by leveraging efficient Satisfiability Modulo Theories (SMT) engines to perform exhaustive reasoning on TFPGs. We apply model-checking techniques to certify that a given TFPG satisfies (or not) a property of interest. Moreover, we discuss the problem of refinement and diagnosability testing and empirically show that our technique can be used to efficiently solve them.