Uncertainty is unavoidable when modeling most application domains. In medicine, for example, symptoms (such as pain, dizziness, or nausea) are always subjective, and hence imprecise and incomparable. Additionally, concepts and their relationships may be inexpressible in a crisp, clear-cut manner. We extend the description logic ALC with multi-valued semantics based on lattices that can handle uncertainty on concepts as well as on the axioms of the ontology. We introduce reasoning methods for this logic w.r.t. general concept inclusions and show that the complexity of reasoning is not increased by this new semantics.

In Model-Based Diagnosis, a diagnostic algorithm is typically used to compute diagnoses using a model of a real-world system and some observations. Contrary to classical hypothesis, in real-world applications it is sometimes the case that either the model, the observations or the diagnostic algorithm are abnormal with respect to some required properties; with possibly huge economical consequences. Determining which abnormalities exist constitutes a meta-diagnostic problem. We contribute, first, with a general theory of meta-diagnosis with clear semantics to handle this problem. Second, we propose a series of typically required properties and relate them between themselves. Finally, using our meta-diagnostic framework and the studied properties and relations, we model and solve some common meta-diagnostic problems.

We provide reformulations and generalizations of both the semantics of logic programs by Faber, Leone and Pfeifer and its extension to arbitrary propositional formulas by Truszczynski. Unlike the previous definitions, our generalizations refer neither to grounding nor to fixpoints, and apply to first-order formulas containing aggregate expressions. In the same spirit as the first-order stable model semantics proposed by Ferraris, Lee and Lifschitz, the semantics proposed here are based on syntactic transformations that are similar to circumscription. The reformulations provide useful insights into the FLP semantics and its relationship to circumscription and the first-order stable model semantics.

Problems that require multiple agents to follow non-interfering paths from their current states to their respective goal states are called cooperative pathfinding problems. We present the first {complete algorithm for finding these paths that is sufficiently fast for real-time applications. Furthermore, our algorithm offers a trade-off between running time and solution quality. We then refine our algorithm into an anytime algorithm that first quickly finds a solution, and then uses any remaining time to incrementally improve that solution until it is optimal or the algorithm is terminated. We compare our algorithms to those in the literature and show that in addition to completeness, our algorithms offer improved solution quality as well as competitive running time.

We develop a real-time algorithm based on a Monte-Carlo game tree search for solving a quantified constraint satisfaction problem (QCSP), which is a CSP where some variables are universally quantified. A universally quantified variable represents a choice of nature or an adversary. The goal of a QCSP is to make a robust plan against an adversary. However, obtaining a complete plan off-line is intractable when the size of the problem becomes large. Thus, we need to develop a real-time algorithm that sequentially selects a promising value at each deadline. Such a problem has been considered in the field of game tree search. In a standard game tree search algorithm, developing a good static evaluation function is crucial. However, developing a good static evaluation function for a QCSP is very difficult since it must estimate the possibility that a partially assigned QCSP is solvable. Thus, we apply a Monte-Carlo game tree search technique called UCT. However, the simple application of the UCT algorithm does not work since the player and the adversary are asymmetric, i.e., finding a game sequence where the player wins is very rare. We overcome this difficulty by introducing constraint propagation techniques. We experimentally compare the winning probability of our UCT-based algorithm and the state-of-the-art alpha-beta search algorithm. Our results show that our algorithm outperforms the state-of-the-art algorithm in large-scale problems.

Monte Carlo tree search (MCTS) methods have had recent success in games, planning, and optimization. MCTS uses results from rollouts to guide search; a rollout is a path that descends the tree with a randomized decision at each ply until reaching a leaf. MCTS results can be strongly influenced by the choice of appropriate policy to bias the rollouts. Most previous work on MCTS uses static uniform random or domain-specific policies. We describe a new MCTS method that dynamically adapts the rollout policy during search, in deterministic optimization problems. Our starting point is Cazenave's original Nested Monte Carlo Search (NMCS), but rather than navigating the tree directly we instead use gradient ascent on the rollout policy at each level of the nested search. We benchmark this new Nested Rollout Policy Adaptation (NRPA) algorithm and examine its behavior. Our test problems are instances of Crossword Puzzle Construction and Morpion Solitaire. Over moderate time scales NRPA can substantially improve search efficiency compared to NMCS, and over longer time scales NRPA improves upon all previous published solutions for the test problems. Results include a new Morpion Solitaire solution that improves upon the previous human-generated record that had stood for over 30 years.

Many systems use Markov models to generate finite-length sequences that imitate a given style. These systems often need to enforce specific control constraints on the sequences to generate. Unfortunately, control constraints are not compatible with Markov models, as they induce long-range dependencies that violate the Markov hypothesis of limited memory. Attempts to solve this issue using heuristic search do not give any guarantee on the nature and probability of the sequences generated. We propose a novel and efficient approach to controlled Markov generation for a specific class of control constraints that 1) guarantees that generated sequences satisfy control constraints and 2) follow the statistical distribution of the initial Markov model. Revisiting Markov generation in the framework of constraint satisfaction, we show how constraints can be compiled into a non-homogeneous Markov model, using arc-consistency techniques and renormalization. We illustrate the approach on a melody generation problem and sketch some realtime applications in which control constraints are given by gesture controllers.

As adversarial environments become more complex, it is increasingly crucial for agents to exploit the mistakes of weaker opponents, particularly in the context of winning tournaments and competitions.In this work, we present a simple post processing technique, which wecall Perfect Information Post-Mortem Analysis (PIPMA), that can quickly assess the playing strength of an opponent in certain classes of game environments. We apply this technique to skat, a popular German card game, and show that we can achieve substantial performance gains against not only players weaker than our program, but against stronger players as well. Most importantly, PIPMA can model the opponent after only a handful of games. To our knowledge, this makes our work the first successful example of an opponent modelling technique that can adapt its play to a particular opponent in real time in a complex game setting.

Hash Distributed A* (HDA*) is a parallel A* algorithm that is proven to be effective in optimal sequential planning with unit edge costs. HDA* leverages the Zobrist function to almost uniformly distribute and schedule work among processors. This paper evaluates the performance of HDA* in optimal sequence alignment. We observe that with a large number of CPU cores HDA* suffers from an increase of search overhead caused by reexpansions of states in the closed list due to nonuniform edge costs in this domain. We therefore present a new work distribution strategy limiting processors to distribute work, thus increasing the possibility of detecting such duplicate search effort. We evaluate the performance of this approach on a cluster of multi-core machines and show that the approach scales well up to 384 CPU cores.

Transposition tables are a powerful tool in search domains for avoiding duplicate effort and for guiding node expansions. Traditionally, however, they have only been applicable when the current state is exactly the same as a previously explored state. We consider a generalized transposition table, whereby a similarity metric that exploits local structure is used to compare the current state with a neighbourhood of previously seen states. We illustrate this concept and forward pruning based on function approximation in the domain of Skat, and show that we can achieve speedups of 16+ over standard methods.