Commonsense reasoning, in particular qualitative spatial and temporal reasoning (QSTR), provides flexible and intuitive methods for reasoning about vague and uncertain information including spatial orientation, topology and proximity.  Despite a number of theoretical advances in QSTR, there are relatively few applications that employ these methods.  The central problem is a significant lack of application level standards and validation methods for supporting developers in adapting and integrating QSTR with their domain specific qualitative spatial and temporal models.  To address this we present a significantly novel methodology for QSTR application validation, inspired by research in software engineering.  In this paper we focus on unit testing, and adapt the software engineering strategy of defining boundary cases.  We present two critical boundary concepts, a methodology for isolating the units under testing from other parts of the model, and methods to assist the designer in integrating our critical boundary unit testing approach with a broader validation plan.

Ontology mapping plays an important role in interoperability over ontologies. Many researchers have proposed algorithms and tools for (semi-)automatically mapping one concept to another concept. Among them, WordNet is widely used as the domain knowledge support in the mapping process. To our knowledge, however, most of them only use synonym, hypernym and hyponym relations in WordNet and the actual meanings provided in natural English(as gloss) are often ignored. In this paper, we treat the concepts(c) as English words (w) and propose an ontology mapping technique where we use the meanings of the words as given in Wordnet (in English) for semantic mapping by constructing their parse trees first and simplifying them for computing similarity measures. Our experimental results show that our method performs better in Recall and F1-Measure than many techniques reported in the literature.

This paper presents a tree-to-tree transduction method for sentence compression. Our model is based on synchronous tree substitution grammar, a formalism that allows local distortion of the tree topology and can thus naturally capture structural mismatches. We describe an algorithm for decoding in this framework and show how the model can be trained discriminatively within a large margin framework. Experimental results on sentence compression bring significant improvements over a state-of-the-art model.

Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.

SPARK'08 was the first edition of a workshop series designed to provide a stable, longterm forum where researchers could discuss Workshop (SPARK) was established to help address this issue. Building on precursory events, SPARK'08 was the first workshop designed Scheduling (ICAPS-08) held in Sydney, Australia, in September 2008. Like its immediate predecessor (the ICAPS'07 Workshop on Moving Planning and Scheduling Systems), the 2008 SPARK workshop was collocated with the International Conference on Automated Planning and Scheduling (ICAPS), a premier forum for research in AI planning and scheduling, and the International Conference on Principles and Practice of Constraint Programming (CP). A handful of outstanding application-oriented papers are presented each year at the ICAPS conference. Time and again, in invited talks and in open microphone discussion sessions such as ICAPS's Festivus (where conference participants air their grievances in an open and entertaining way), researchers have lamented the small number of applications papers accepted at conferences such as ICAPS, CP, and the AAAI Conference on Artificial Intelligence.

In this work, we first show that feature selection methods other than boosting can also be used for training an efficient object detector. In particular, we introduce Greedy Sparse Linear Discriminant Analysis (GSLDA) \cite{Moghaddam2007Fast} for its conceptual simplicity and computational efficiency; and slightly better detection performance is achieved compared with \cite{Viola2004Robust}. Moreover, we propose a new technique, termed Boosted Greedy Sparse Linear Discriminant Analysis (BGSLDA), to efficiently train a detection cascade. BGSLDA exploits the sample re-weighting property of boosting and the class-separability criterion of GSLDA.

One common type of symmetry is when values are symmetric. For example, if we are assigning colours (values) to nodes (variables) in a graph colouring problem then we can uniformly interchange the colours throughout a colouring. For a problem with value symmetries, all symmetric solutions can be eliminated in polynomial time. However, as we show here, both static and dynamic methods to deal with symmetry have computational limitations. With static methods, pruning all symmetric values is NP-hard in general. With dynamic methods, we can take exponential time on problems which static methods solve without search.

To model combinatorial decision problems involving uncertainty and probability, we introduce scenario based stochastic constraint programming. Stochastic constraint programs contain both decision variables, which we can set, and stochastic variables, which follow a discrete probability distribution. We provide a semantics for stochastic constraint programs based on scenario trees. Using this semantics, we can compile stochastic constraint programs down into conventional (non-stochastic) constraint programs. This allows us to exploit the full power of existing constraint solvers. We have implemented this framework for decision making under uncertainty in stochastic OPL, a language which is based on the OPL constraint modelling language [Hentenryck et al., 1999]. To illustrate the potential of this framework, we model a wide range of problems in areas as diverse as portfolio diversification, agricultural planning and production/inventory management.

Tetravex is a widely played one person computer game in which you are given $n^2$ unit tiles, each edge of which is labelled with a number. The objective is to place each tile within a $n$ by $n$ square such that all neighbouring edges are labelled with an identical number. Unfortunately, playing Tetravex is computationally hard. More precisely, we prove that deciding if there is a tiling of the Tetravex board is NP-complete. Deciding where to place the tiles is therefore NP-hard. This may help to explain why Tetravex is a good puzzle. This result compliments a number of similar results for one person games involving tiling. For example, NP-completeness results have been shown for: the offline version of Tetris, KPlumber (which involves rotating tiles containing drawings of pipes to make a connected network), and shortest sliding puzzle problems. It raises a number of open questions. For example, is the infinite version Turing-complete? How do we generate Tetravex problems which are truly puzzling as random NP-complete problems are often surprising easy to solve? Can we observe phase transition behaviour? What about the complexity of the problem when it is guaranteed to have an unique solution? How do we generate puzzles with unique solutions?

A wide range of constraints can be compactly specified using automata or formal languages. In a sequence of recent papers, we have shown that an effective means to reason with such specifications is to decompose them into primitive constraints. We can then, for instance, use state of the art SAT solvers and profit from their advanced features like fast unit propagation, clause learning, and conflict-based search heuristics. This approach holds promise for solving combinatorial problems in scheduling, rostering, and configuration, as well as problems in more diverse areas like bioinformatics, software testing and natural language processing. In addition, decomposition may be an effective method to propagate other global constraints.