This work focuses on developing domain-independent heuristics for probabilistic planning problems characterized by full observability and non-deterministic effects of actions that are expressed by probability distributions. The approach is to first search for a high probability deterministic plan using a classical planner. A novel probabilistic plan graph heuristic is used to guide the search towards high probability plans. The resulting plans can be used in a system that handles unexpected outcomes by runtime replanning. The plans can also be incrementally augmented with contingency branches for the most critical action outcomes. This abstract will describe the steps that we have taken in completing the above work and the obtained results.

The behavior and dynamics of complex systems are in focus of many research fields. The complexity of such systems comes not only from the number of their elements, but also from the unavoidable emergence of new properties of the system, which are not just a simple summation of the properties of its elements. The behavior of complex systems can be fitted with a number of well developed models, which, however, do not incorporate the modularity and the evolution of a system simultaneously. In this work, we propose a generalized model that addresses this issue. Our model is developed within the Random Set Theory’s framework and allows for reconstructing the stochastic evolution diagrams of complex systems.

Dots-And-Boxes is a well-known and widely-played combinatorial game. While the rules of play are very simple, the state space for even small games is extremely large, and finding the outcome under optimal play is correspondingly hard. In this paper we introduce a Dots-And-Boxes solver which is significantly faster than the current state-of-the-art: over an order-of-magnitude faster on several large problems. We describe our approach, which uses Alpha-Beta search and applies a number of techniques—both problem-specific and general—to reduce the number of duplicate states explored and reduce the search space to a manageable size. Using these techniques, we have determined for the first time that Dots- And-Boxes on a board of 4x5 boxes is a tie given optimal play. This is the largest game solved to date.

Selection bias, caused by preferential exclusion of units (or samples) from the data, is a major obstacle to valid causal inferences, for it cannot be removed or even detected by randomized experiments. This paper highlights several graphical and algebraic methods capable of mitigating and sometimes eliminating this bias.

We also require a generic model of provenance The Web has evolved from a collection of hyperlinked documents in order to support the diverse ecosystem of sensor to a complex ecosystem of interconnected documents, platforms and data. We have investigated a number of existing services and devices. Due to the inherent open nature of the models for representing provenance information but Web, data can be published by anyone or any'thing'. As a found many of these to be tailored to specific domains result of this, there is enormous variation in the quality of (e.g.

Opponent modeling is a critical mechanism in repeated games. It allows a player to adapt its strategy in order to better respond to the presumed preferences of his opponents. We introduce a new modeling technique that adaptively balances exploitability and risk reduction. An opponent’s strategy is modeled with a set of possible strategies that contain the actual strategy with a high probability. The algorithm is safe as the expected payoff is above the minimax payoff with a high probability, and can exploit the opponents’ preferences when sufficient observations have been obtained. We apply them to normal-form games and stochastic games with a finite number of stages. The performance of the proposed approach is first demonstrated on repeated rock-paper-scissors games. Subsequently, the approach is evaluated in a human-robot table-tennis setting where the robot player learns to prepare to return a served ball. By modeling the human players, the robot chooses a forehand, backhand or middle preparation pose before they serve. The learned strategies can exploit the opponent’s preferences, leading to a higher rate of successful returns.

We address the problem of building a continuous occupancy representation of the environment with ranging sensors. Observations from such sensors provide two types of information: a line segment or a beam indicating no returns along them (free-space); a point or return at the end of the segment representing an occupied surface. To model these two types of observations in a principled statistical manner, we propose a novel methodology based on integral kernels. We show that integral kernels can be directly incorporated into a Gaussian process classification (GPC) framework to provide a continuous non-parametric Bayesian estimation of occupancy. Directly handling line segment and point observations avoids the need to discretise segments into points, reducing the computational cost of GPC inference and learning. We present experiments on 2D and 3D datasets demonstrating the benefits of the approach.

In this paper, we propose a bottom-up approach to generating short descriptive sentences from images, to enhance scene understanding. We demonstrate automatic methods for mapping the visual content in an image to natural spoken or written language. We also introduce a human-in-the-loop evaluation strategy that quantitatively captures the meaningfulness of the generated sentences. We recorded a correctness rate of 60.34% when human users were asked to judge the meaningfulness of the sentences generated from relatively challenging images. Also, our automatic methods compared well with the state-of-the-art techniques for the related computer vision tasks.

Logic, and declarative representation of knowledge in general, have long been a preferred framework for problem solving in AI. However, specific subareas of AI have been eager to abandon general-purpose knowledge representation in favor of methods that seem to address their computational core problems better. In planning, for example, state-space search has in the last several years been preferred to logic-based methods such as SAT. In our recent work, we have demonstrated that the observed performance differences between SAT and specialized state-space search methods largely go back to the difference between a blind (or at least planning-agnostic) and a planning-specific search method. If SAT search methods are given even simple heuristics which make the search goal-directed, the efficiency differences disappear.

The automated processing of multiple seismic signals to detect and localize seismic events is a central tool in both geophysics and nuclear treaty verification. This paper reports on a project, begun in 2009, to reformulate this problem in a Bayesian framework. A Bayesian seismic monitoring system, NET-VISA, has been built comprising a spatial event prior and generative models of event transmission and detection, as well as an inference algorithm. Applied in the context of the International Monitoring System (IMS), a global sensor network developed for the Comprehensive Nuclear-Test-Ban Treaty (CTBT), NET-VISA achieves a reduction of around 50% in the number of missed events compared to the currently deployed system. It also finds events that are missed even by the human analysts who post-process the IMS output.