But each They are linked by an arrow which is labeled by discourse method for representing a context is quite different. Our relations R. We represent SDRS in the form of boxes like study is based on two representational methods of temporal DRS. To induce a temporal and hierarchical structure, relations: the Segmented Discourse Representation Theory SDRT distinguish discourse relations'coordinating' from (SDRT) and the model of Cognitive and Applicative'subordinating', therefore coordination and subordination Grammar (CAG). This paper presents a comparison of affect the temporal order of text: the former indicate a continuation these two approaches about aspect and tense by an analysis of some discourses pattern, like relations of'Narration' of relations between events. We are not going to show all or'Result' in discourse segmentation, and the later steps of SDRT's representations, but we take a simple discourse indicate with types of information like relations of'Elaboration' (Asher and Lascarides 2003) and we analyze the or'Explanation'. These relations are appeared same discourse with the framework of the CAG.

We present an efficient random decision tree algorithm for case-based reasoning systems. We combine this algorithm with a simple similarity measure based on domain knowledge to create a stronger hybrid algorithm. This combination is based on our general approach for combining lazy and eager learning methods. We evaluate the resulting algorithms on a case base of patient records in a palliative care domain. Our hybrid algorithm consistently produces a lower average error than the base algorithms.

This volume contains the papers accepted for presentation at ICAPS 2011, the Twenty-First International Conferenceon Automated Planning and Scheduling, held in Freiburg, Germany, on June 11–16, 2011. The annual ICAPS conference series was established in 2003 through the merger of two pre-existing biennial conferences, the International Conference on Artificial Intelligence Planning and Scheduling (AIPS) and the European Conference on Planning (ECP). ICAPS continues the traditional high standards of AIPS and ECP as an archival forum for new research in the rapidly developing field of automated planning andscheduling. This volume contains the papers accepted at the conference.

We discuss and evaluate metrics for difficulty rating of Sudoku puzzles. The correlation coefficient with human performance for our best metric is 0.95. The data on human performance were obtained from three web portals and they comprise thousands of hours of human solving over 2000 problems. We provide a simple computational model of human solving activity and evaluate it over collected data. Using the model we show that there are two sources of problem difficulty: complexity of individual steps (logic operations) and structure of dependency among steps. Beside providing a very good Sudoku-tuned metric, we also discuss a metric with few Sudoku-specific details, which still provides good results (correlation coefficient is 0.88). Hence we believe that the approach should be applicable to difficulty rating of other constraint satisfaction problems.

Planning programs are loose, high-level, declarative representations of the behavior of agents acting in a domain and following a path of goals to achieve. Such programs are specified through transition systems that can include cycles and decisions to make at certain points. We investigate a new effective approach for solving the problem of realizing a planning program, i.e., informally, for finding and combining a collection of plans that guarantee the planning program executability. We focus on deterministic domains and propose a general algorithm that solves the problem exploiting a planning technique handling goal constraints and preferences. A preliminary experimental analysis indicates that our approach dramatically outperforms the existing method based on formal verification and synthesis techniques.

Program committee for the Twenty-First International Conference on Automated Planning and Scheduling, held in Freiburg, Germany, on June 11–16, 2011.

Most planning formalisms allow instances with shortest plans of exponential length. While such instances are problematic, they are usually unavoidable and can occur in practice. There are several known cases of restricted planning problems where plans can be exponential but always have a compact (ie. polynomial) representation, often using recursive macros. Such compact representations are important since exponential plans are difficult both to use and to understand. We show that these results do not extend to the general case, by proving a number of bounds for compact representations of plans under various criteria, like efficient sequential or random access of actions. Further, we show that it is unlikely to get around this by reformulating planning into some other problem. The results are discussed in the context of abstraction, macros and plan explanation.

This paper presents our explorations in using graph centrality measures to solve the synonym expansion problem. In particular, we use the concept of directional similarity to derive directed graphs on which we apply centrality algorithms to identify the most likely synonyms for a target word in a given context. We show that our method can lead to performance comparable to the state-of-the-art.

The paper deals with the problem of an analysis of complex sentences in Czech on the basis of manually annotated data. The availability of a specialized corpus explicitly describing mutual relationships between segments and clauses in Czech complex sentences, together with the availability of a thoroughly syntactically annotated corpus, the Prague Dependency Treebank, provide a solid background for linguistic investigation. The paper presents quantitative, linguistic and structural observations which provide a number of clues for building an algorithm for analyzing a structure of complex sentences in the future.

The aim of this paper is threefold: (1) it explores methods for the detection of affective states in text, (2) it presents the usage of such affective cues in a conversational system and (3) it evaluates its effectiveness in a virtual reality setting. Valence and arousal values, used for generating facial expressions of users' avatars, are also incorporated into the dialog, helping to bridge the gap between textual and visual modalities. The system is evaluated in terms of its ability to: (i) generate a realistic dialog, (ii) create an enjoyable chatting experience, and (iii) establish an emotional connection with participants. Results show that user ratings for the conversational agent match those obtained in a Wizard of Oz setting.