The overall goal of our research is the automatic recognition of the intended message of a grouped bar chart. This paper presents our preliminary work on a system that utilizes the communicative signals in a grouped bar chart as evidence in a Bayesian network that hypothesizes the primary message conveyed by the graphic. The paper discusses the kinds of communicative signals present in grouped bar charts and an ACT-R model for computationalizing one important communicative signal, the relative effort involved in performing the perceptual tasks necessary for the recognition. It also describes our Bayesian network and its implementation on a subset of the kinds of messages that can be conveyed by grouped bar charts.

The paper argues that (1) stochastic planning should be used as a core problem domain for relational probabilistic models providing problems of interest that are challenging for current approaches and significant scope for extending their capabilities, (2) that symbolic dynamic programming solving such problems can be seen as a prime example of lifted inference in relational probabilistic problems, (3) that first order decision diagrams provide a useful tool to drive such lifted computations, and (4) that the resulting lifted inference is qualitatively different from what other approaches are providing. As a result, this relationship can be studied to the benefit of developing foundations for relational probabilistic models and to the benefit of stochastic planning.

Much has been achieved in the field of AI, yet much remains Gibbs sampling code in C/C . Chechetka et al. investigate relational learning for collective classification of entities to be done if we are to reach the goals we all imagine. in images. Choi et al. present a lifted inference One of the key challenges with moving ahead is closing approach for relational continuous models. Logical AI has Gogate and Domingos shows how to exploit logical structure mainly focused on complex representations, and statistical in lifted probabilistic inference. Hadiji et al. discuss AI on uncertainty.

The projects that we have developed for UC Berkeley’s introductory artificial intelligence (AI) course teach foundational concepts using the classic video game Pac-Man. There are four project topics: state-space search, multi-agent search, probabilistic inference, and reinforcement learning. Each project requires students to implement general-purpose AI algorithms and then to inject domain knowledge about the Pac- Man environment using search heuristics, evaluation functions, and feature functions. We have found that the Pac-Man theme adds consistency to the course, as well as tapping in to students’ excitement about video games.

Providing cognitive assistance in smart homes is a field of research that receives a lot of attention lately. In order to give adequate assistance at the opportune moment, we need to recognize the observed behavior when the patient carries out some activities in a smart home. To address this challenging issue, we present a formal activity recognition framework based on possibility theory. We present initial results from an implementation of this possibilistic recognition approach in a smart home laboratory.

The starting point is an ongoing effort to structure underlying intelligent behavior, whether intended reconstruct cognitive architectures from the ground up via as models of human intelligence and/or implementations of graphical models (Koller and Friedman 2009), with the artificial intelligence (Langley, Laird and Rogers 2009). A aim of understanding existing architectures better, basic cognitive architecture may comprise memories, exploring the overall space of architectures, and decision algorithms, learning mechanisms, and some developing new and improved architectures (Rosenbloom means of interacting with external environments.

Lifting can greatly reduce the cost of inference on first-order probabilistic models, but constructing the lifted network can itself be quite costly. In addition, the minimal lifted network is often very close in size to the fully propositionalized model; lifted inference yields little or no speedup in these situations. In this paper, we address both these problems. We propose a compact hypercube-based representation for the lifted network, which can greatly reduce the cost of lifted network construction. We also present two methods for approximate lifted network construction, which groups together similar but distinguishable objects and treats them as if they were identical. This can greatly reduce the size of the lifted network as well as the time required for lifted network construction, but potentially at some cost to accuracy. The coarseness of the approximation can be adjusted depending on the accuracy required, and we can bound the resulting error. Experiments on six domains show great efficiency gains with only minor loss in accuracy.

In this paper, we introduce Bayesian Abductive Logic Programs (BALPs), a new formalism that integrates Bayesian Logic Programs (BLPs) and Abductive Logic Programming (ALP) for abductive reasoning. Like BLPs, BALPs also combine first-order logic and Bayesian networks. However, unlike BLPs that use logical deduction to construct Bayes nets, BALPs employ logical abduction. As a result, BALPs are more suited for solving problems like plan/activity recognition and diagnosis that require abductive reasoning. First, we present the necessary enhancements to BLPs in order to support logical abduction. Next, we apply BALPs to the task of plan recognition and demonstrate its efficacy on two data sets. We also compare the performance of BALPs with several existing approaches for abduction.

Machine reading aims to automatically extract knowledge from text. It is a long-standing goal of AI and holds the promise of revolutionizing Web search and other fields. In this paper, we analyze the core challenges of machine reading and show that statistical relational AI is particularly well suited to address these challenges. We then propose a unifying approach to machine reading in which statistical relational AI plays a central role. Finally, we demonstrate the promise of this approach by presenting OntoUSP, an end-to-end machine reading system that builds on recent advances in statistical relational AI and greatly outperforms state-of-the-art systems in a task of extracting knowledge from biomedical abstracts and answering questions.

Exploiting ontologies for efficient inference is one of the most widely studied topics in knowledge representation and reasoning. The use of ontologies for probabilistic inference, however, is much less developed. A number of algorithms for lifted inference in first-order probabilistic languages have been proposed, but their scalability is limited by the combinatorial explosion in the sets of objects that need to be considered. We propose a coarse-to-fine inference approach that leverages a class hierarchy to combat this problem. Starting at the highest level, our approach performs inference at successively finer grains, pruning low-probability atoms before refining. We provide bounds on the error incurred by this approach relative to full ground inference as a function of the pruning threshold. We also show how to learn parameters in a coarse-to-fine manner to maximize the opportunities for pruning during inference. Experiments on link prediction and biomolecular event prediction tasks show our method can greatly improve the scalability of lifted probabilistic inference.