Ignorable Information in Multi-Agent Scenarios

In some multi-agent scenarios, identifying observations that an agent can safely ignore reduces exponentially the size of the agent's strategy space and hence the time required to find a Nash equilibrium. We consider games represented using the multi-agent influence diagram (MAID) framework of Koller and Milch [2001], and analyze the extent to which information edges can be eliminated. We define a notion of a safe edge removal transformation, where all equilibria in the reduced model are also equilibria in the original model. We show that existing edge removal algorithms for influence diagrams are safe, but limited, in that they do not detect certain cases where edges can be removed safely. We describe an algorithm that produces the "minimal" safe reduction, which removes as many edges as possible while still preserving safety. Finally, we note that both the existing edge removal algorithms and our new one can eliminate equilibria where agents coordinate their actions by conditioning on irrelevant information. Surprisingly, in some games these "lost" equilibria can be preferred by all agents in the game

Random-World Semantics and Syntactic Independence for Expressive Languages

We consider three desiderata for a language combining logic and probability: logical expressivity, random-world semantics, and the existence of a useful syntactic condition for probabilistic independence. Achieving these three desiderata simultaneously is nontrivial. Expressivity can be achieved by using a formalism similar to a programming language, but standard approaches to combining programming languages with probabilities sacrifice random-world semantics. Naive approaches to restoring random-world semantics undermine syntactic independence criteria. Our main result is a syntactic independence criterion that holds for a broad class of highly expressive logics under random-world semantics. We explore various examples including Bayesian networks, probabilistic context-free grammars, and an example from Mendelian genetics. Our independence criterion supports a case-factor inference technique that reproduces both variable elimination for BNs and the inside algorithm for PCFGs

Threshold-free Selection of Taxonomic Multilabels

In online search or content selection systems, significant computational resources are expended to classify or categorize electronic documents into topics, concepts, or entities. A classifier can process, parse or otherwise analyze the document to assign one or more labels to the document based on the taxonomy. The classifier can generate a score for each of the labels, and provide the labels and the scores to other components or modules for further downstream processing. To keep downstream processes efficient without causing excessive processing of labels, the classifier may filter out the labels to return a subset of labels based on comparing a label’s score with a threshold. However, using a threshold-based technique to filter out labels may not account for the tree structure of the taxonomy, and it may also fail to take into account the likelihood dependencies between all parent nodes and child nodes. The proposed technique solves this by (1) selecting a set of labels returned by the classifier that optimizes certain metrics, such as precision and recall metrics; and (2) using a greedy multi-label selection algorithm that optimizes the precision/recall in step (1). Using these techniques, the system can select a subset of labels to return or provide for further processing

Query-free news search

Many daily activities present information in the form of a stream of text, and often people can benefit from additional information on the topic discussed. TV broadcast news can be treated as one such stream of text; in this paper we discuss finding news articles on the web that are relevant to news currently being broadcast.We evaluated a variety of algorithms for this problem, looking at the impact of inverse document frequency, stemming, compounds, history, and query length on the relevance and coverage of news articles returned in real time during a broadcast. We also evaluated several postprocessing techniques for improving the precision, including reranking using additional terms, reranking by document similarity, and filtering on document similarity. For the best algorithm, 84%-91% of the articles found were relevant, with at least 64% of the articles being on the exact topic of the broadcast. In addition, a relevant article was found for at least 70% of the topics

Scenic: A Language for Scenario Specification and Scene Generation

We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning. Specifically, we consider the problems of training a perception system to handle rare events, testing its performance under different conditions, and debugging failures. We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs and sampling these to generate specialized training and test sets. More generally, such languages can be used for cyber-physical systems and robotics to write environment models, an essential prerequisite to any formal analysis. In this paper, we focus on systems like autonomous cars and robots, whose environment is a "scene", a configuration of physical objects and agents. We design a domain-specific language, Scenic, for describing "scenarios" that are distributions over scenes. As a probabilistic programming language, Scenic allows assigning distributions to features of the scene, as well as declaratively imposing hard and soft constraints over the scene. We develop specialized techniques for sampling from the resulting distribution, taking advantage of the structure provided by Scenic's domain-specific syntax. Finally, we apply Scenic in a case study on a convolutional neural network designed to detect cars in road images, improving its performance beyond that achieved by state-of-the-art synthetic data generation methods.Comment: 41 pages, 36 figures. Full version of a PLDI 2019 paper (extending UC Berkeley EECS Department Tech Report No. UCB/EECS-2018-8

Elevated amygdala responses to emotional faces in youths with chronic irritability or bipolar disorder

AbstractA major controversy in child psychiatry is whether bipolar disorder (BD) presents in children as severe, non-episodic irritability (operationalized here as severe mood dysregulation, SMD), rather than with manic episodes as in adults. Both classic, episodic BD and SMD are severe mood disorders characterized by deficits in processing emotional stimuli. Neuroimaging techniques can be used to test whether the pathophysiology mediating these deficits are similar across the two phenotypes. Amygdala dysfunction during face emotion processing is well-documented in BD, but little is known about amygdala dysfunction in chronically irritable youth. We compared neural activation in SMD (n=19), BD (n=19), and healthy volunteer (HV; n=15) youths during an implicit face-emotion processing task with angry, fearful and neutral expressions. In the right amygdala, both SMD and BD exhibited greater activity across all expressions than HV. However, SMD and BD differed from each other and HV in posterior cingulate cortex, posterior insula, and inferior parietal lobe. In these regions, only SMD showed deactivation in response to fearful expressions, whereas only BD showed deactivation in response to angry expressions. Thus, during implicit face emotion processing, youth with BD and those with SMD exhibit similar amygdala dysfunction but different abnormalities in regions involved in information monitoring and integration