What determines difficulty of solving a problem? Although this question has been studied before, we found examples which show large differences in problem difficulty which are not explained by concepts identified in previous research. This differences are caused mainly by the structure of a problems' state spaces and cannot be easily captured by static metrics like size of the state space or the length of a solution. To address these unexplained differences, we propose a computational model of human problem solving behaviour. We provide evaluation of the model over large scale dataset (hundreds of hours of problem solving, more than 100 problem instances) for three transport puzzles (Sokoban, Rush hour, and Replacement puzzle).

We investigate two publicly available web knowledge bases, Wikipedia and Yago, in an attempt to leverage semantic information and increase the performance level of a state-of-the-art coreference resolution (CR) engine. We extract semantic compatibility and aliasing information from Wikipedia and Yago, and incorporate it into a CR system. We show that using such knowledge with no disambiguation and filtering does not bring any improvement over the baseline, mirroring the previous findings. We propose, therefore, a number of solutions to reduce the amount of noise coming from web resources: using disambiguation tools for Wikipedia, pruning Yago to eliminate the most generic categories and imposing additional constraints on affected mentions. Our evaluation experiments on the ACE-02 corpus show that the knowledge, extracted from Wikipedia and Yago, improves our system's performance by 2-3 percentage points.

Considering directed graphs on n vertices and m edges with real (possibly negative) weights, we present two new, efficient algorithms for computing all-pairs shortest paths (APSP). These algorithms make use of directed path consistency (DPC) along a vertex ordering d. The algorithms run in O(n 2 w d ) time, where w d is the graph width induced by this vertex ordering. For graphs of constant treewidth, this yields O(n 2 ) time, which is optimal. On chordal graphs, the algorithms run in O(nm) time. We show empirically that also in many general cases, both constructed and from realistic benchmarks, the algorithms often outperform Johnson's algorithm, which represents the current state of the art with a run time of O(nm + n 2 log n). These algorithms can be used for temporal and spatial reasoning, e.g. for the Simple Temporal Problem (STP), which underlines its relevance to the planning and scheduling community.

Ontologies have been criticized because they are not sufficiently flexible, and thus cannot capture the dynamism and complexity of reality. However, they have increasingly come into focus because of the need for knowledge management in both general and specialized knowledge domains. EcoLexicon is a frame-based visual thesaurus on the environment that is gradually evolving towards the status of a formal ontology. For this purpose, the information in its relational database is in the process of being linked to the ontological system of FunGramKB, a multipurpose knowledge base that has been specifically designed for natural language understanding with modules for lexical, grammatical, and conceptual knowledge. This enables the explicitation of specialized knowledge as an extension of general knowledge through its representation in the domain-specific satellite ontology of a main general ontology.

Most predominant approaches in probabilistic planning utilize techniques from the more thoroughly investigated field of classical planning by determinizing the problem at hand. In this paper, we present a method to map probabilistic operators to an equivalent set of probabilistic operators in a novel normal form, requiring polynomial time and space. From this, we directly derive a determinization which can be used for, e.g., replanning strategies incorporating a classical planning system. Unlike previously described all outcome determinizations, the number of deterministic operators is not exponentially but polynomially bounded in the number of parallel probabilistic effects, enabling the use of more sophisticated determinization-based techniques in the future.

In this paper optimal state space planning is parallelized by exploiting the processing power of a graphics card. The two exploration steps, namely selecting the actions to be applied and generating the successors, are performed on a graphics processing unit. Duplicate detection, however, is delayed to be executed on the central processing unit. Multiple cores are employed to bypass main memory latency. To increase processing speed for exact duplicate detection, the hash tables are lock-free. Moreover, a bucket-based representation enhances the concurrent distribution of frontier states. The planner supports cost-first exploration and is able to deal with a considerable fraction of current PDDL, including numerical state variables, complex objective functions, and goal preferences. It can maximize the net-benefit. Experimental findings show visible performance gains especially for larger benchmark problems.

Causality plays an important role in our comprehension of the world. It amounts to determine what truly causes what and what it matters. Interventions allow the identification of elements in a sequence of events that are related in a causal way. In this paper, we introduce belief causation and we proposea method for handling interventions in graphical model under an uncertain environment where the uncertainty is represented by belief masses, so-called belief causal networks. More specifically, we propose a generalization of the “DO” operator and explain the needed changes on the structure of the graph to model a belief causal network on which interventions are proceeded.

This paper presents a generalization of the coupled-task scheduling problem introduced by Shapiro, where considered tasks are subject to incompatibility constraint depicted by an undirected graph. The motivation of this problem comes from data acquisition and processing in a mono-processor torpedo used for underwater exploration. As we add the compatibility graph, we focus on complexity of the problem, and more precisely on the border between P and NP-completeness when some other input parameters are restricted (e.g. the ratio between the durations of the two sub-tasks composing a task): we adapt the global visualization of the complexity of scheduling problems with coupled-task given by Orman and Potts to our problem, determine new complexity results, and thus propose a new visualization including incompatibility constraint. In the end, we give a new polynomial-time approximation algorithm result which completes previous works.

List of the organizers and conference committees for FLAIRS-24, including special tracks.

Analyzing chat traffic has important applications for both the military and the civilian world. This poster will report on an effort to automatically separate chat messages into topic threads.