Does Skepticism Presuppose Explanationism?

A common response to radical skeptical challenges to our knowledge of the external world has been that there are explanatory reasons (e.g., simplicity, coherence, explanatory power, conservatism) for favoring commonsense explanations of our sensory experiences over skeptical explanations. Despite the degree of visibility this class of response has enjoyed, it has often been viewed with skepticism [sic] by the epistemological community because of concerns about the epistemic merits of explanatory reasoning. I argue that skeptical challenges that employ skeptical hypotheses presuppose central explanationist tenets and that this fact should raise one’s estimation of the strength of explanationist responses to skepticism

Hot Hands, Streaks and Coin-flips: Numerical Nonsense in the New York Times

The existence of "Hot Hands" and "Streaks" in sports and gambling is hotly debated, but there is no uncertainty about the recent batting-average of the New York Times: it is now two-for-two in mangling and misunderstanding elementary concepts in probability and statistics; and mixing up the key points in a recent paper that re-examines earlier work on the statistics of streaks. In so doing, it's high-visibility articles have added to the general-public's confusion about probability, making it seem mysterious and paradoxical when it needn't be. However, those articles make excellent case studies on how to get it wrong, and for discussions in high-school and college classes focusing on quantitative reasoning, data analysis, probability and statistics. What I have written here is intended for that audience

Logic Programming for Finding Models in the Logics of Knowledge and its Applications: A Case Study

The logics of knowledge are modal logics that have been shown to be effective in representing and reasoning about knowledge in multi-agent domains. Relatively few computational frameworks for dealing with computation of models and useful transformations in logics of knowledge (e.g., to support multi-agent planning with knowledge actions and degrees of visibility) have been proposed. This paper explores the use of logic programming (LP) to encode interesting forms of logics of knowledge and compute Kripke models. The LP modeling is expanded with useful operators on Kripke structures, to support multi-agent planning in the presence of both world-altering and knowledge actions. This results in the first ever implementation of a planner for this type of complex multi-agent domains.Comment: 16 pages, 1 figure, International Conference on Logic Programming 201

Reasoning about visibility in mirrors: A comparison between a human observer and a camera

Human observers make errors when predicting what is visible in a mirror. This is true for perception with real mirrors as well as for reasoning about mirrors shown in diagrams. We created an illustration of a room, a top-down map, with a mirror on a wall, and objects (nails) on the opposite wall. The task was to select which nails were visible in the mirror from a given position (viewpoint). To study the importance of the social nature of the viewpoint we divided the sample (N=108) in two groups. One group (N=54) were tested with a scene in which there was the image of a person. The other group (N=54) were tested with the same scene but with a camera replacing the person. Participants were instructed to think about what would be captured by a camera on a tripod. This manipulation tests the effect of social perspective taking in reasoning about mirrors. As predicted, performance on the task shows an overestimation of what can be seen in a mirror, and a bias to underestimate the role of the different viewpoints, i.e. a tendency to treat the mirror as if it captures information independently of viewpoint. In terms of the comparison between person and camera there were more errors for the camera, suggesting an advantage for evaluating a human viewpoint as opposed to an artificial viewpoint. We suggest that social mechanisms may be involved in perspective taking in reasoning rather than in automatic attention allocation

Space Transportation System Meteorological Expert

Computers are being used today to build the expert systems of tomorrow. Expert systems are computer programs that are smart about a domain in the way that people are smart. Expert systems technology is being applied to weather forecasting to support Shuttle operations for launch and for ground processing at Kennedy Space Center (KSC), Florida. The Space Transportation System Meterological ExperT (STSMET) is a long term project, now-in its third year, to capture general Shuttle operational weather forecasting expertise specific to our locale, to apply it to Shuttle operational weather forecasting tasks at the Cape Canaveral Forecast Facility (CCFF) at the Cape Canaveral Air Force Station (CCAFS), and to ultimately provide an on-line, real-time operational aid to the duty forecasters in performing their tasks. The first domain addressed by the project has been summer thunderstorms. The effort to represent this knowledge and a control structure to reason about it has resulted in an approach that we call scenario-based reasoning. Other meteorological domains on our agenda are frontal weather phenomena, visibility including fog, and wind shear. We believe that scenario-based reasoning is also applicable to these other meteorological domains. The specific operational tasks to which to apply the general knowledge about summer thunderstorms are being identified during this phase of the contract. The project is being developed using state-of-the-art hardware and software: a Symbolics Lisp Machine, Zetalisp and Automated Reasoning Tool (ART), an expert system shell. Scenario-based reasoning appears to have applications outside of weather forecasting. The abilities of a scenario-based system to reason qualitatively, to reason over time, and to reason across scale are all applicable to planning in autonomous systems. With further research, we expect to add analogical reasoning to the abilities of scenario-based reasoning

A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects

Tracking humans that are interacting with the other subjects or environment remains unsolved in visual tracking, because the visibility of the human of interests in videos is unknown and might vary over time. In particular, it is still difficult for state-of-the-art human trackers to recover complete human trajectories in crowded scenes with frequent human interactions. In this work, we consider the visibility status of a subject as a fluent variable, whose change is mostly attributed to the subject's interaction with the surrounding, e.g., crossing behind another object, entering a building, or getting into a vehicle, etc. We introduce a Causal And-Or Graph (C-AOG) to represent the causal-effect relations between an object's visibility fluent and its activities, and develop a probabilistic graph model to jointly reason the visibility fluent change (e.g., from visible to invisible) and track humans in videos. We formulate this joint task as an iterative search of a feasible causal graph structure that enables fast search algorithm, e.g., dynamic programming method. We apply the proposed method on challenging video sequences to evaluate its capabilities of estimating visibility fluent changes of subjects and tracking subjects of interests over time. Results with comparisons demonstrate that our method outperforms the alternative trackers and can recover complete trajectories of humans in complicated scenarios with frequent human interactions.Comment: accepted by CVPR 201