Readings

“Scheduling Conservation Designs for Maximum Flexibility via Network Cascade Optimization” by Shan Xue, Alan Fern and Daniel Sheldon

“One approach to conserving endangered species is to purchase and protect a set of land parcels in a way that maximizes the expected future population spread. Unfortunately, an ideal set of parcels may have a cost that is beyond the immediate budget constraints and must thus be purchased incrementally. This raises the challenge of deciding how to schedule the parcel purchases in a way that maximizes the flexibility of budget usage while keeping population spread loss in control. In this paper, we introduce a formulation of this scheduling problem that does not rely on knowing the future budgets of an organization. In particular, we consider scheduling purchases in a way that achieves a population spread no less than desired but delays purchases as long as possible…”

“Lazy Model Expansion: Interleaving Grounding with Search” by Broes De Cat, Marc Denecker, Maurice Bruynooghe and Peter Stuckey

Finding satisfying assignments for the variables involved in a set of constraints can be cast as a (bounded) model generation problem: search for (bounded) models of a theory in some logic. The state-of-the-art approach for bounded model generation for rich knowledge representation languages is ground-and-solve: reduce the theory to a ground or propositional one and apply a search algorithm to the resulting theory.
An important bottleneck is the blow-up of the size of the theory caused by the grounding phase. Lazily grounding the theory during search is a way to overcome this bottleneck. We present a theoretical framework and an implementation in the context of the FO(.) knowledge representation language. Instead of grounding all parts of a theory, justifications are derived for some parts of it…

“Scaling up Heuristic Planning with Relational Decision Trees” by T. De la Rosa, S. Jimenez, R. Fuentetaja and D. Borrajo

“Current evaluation functions for heuristic planning are expensive to compute. In numerous planning problems these functions provide good guidance to the solution, so they are worth the expense. However, when evaluation functions are misguiding or when planning problems are large enough, lots of node evaluations must be computed, which severely limits the scalability of heuristic planners. In this paper, we present a novel solution for reducing node evaluations in heuristic planning based on machine learning…”

“Identifying Aspects for Web-Search Queries” by F. Wu, J. Madhavan and A. Halevy

“Many web-search queries serve as the beginning of an exploration of an unknown space of information, rather than looking for a specific web page. To answer such queries effec- tively, the search engine should attempt to organize the space of relevant information in a way that facilitates exploration.

We describe the Aspector system that computes aspects for a given query. Each aspect is a set of search queries that together represent a distinct information need relevant to the original search query. To serve as an effective means to explore the space, Aspector computes aspects that are orthogonal to each other and to have high combined coverage…

Writers’s reading lists: Carl Sagan’s 1954 reading list

From Library if Congress if the U.S. Carl’s Sagan’s papers.

  1. The Immoralist, by André Gide 
  2. The Republic, by Plato 
  3. What Is Mathematics?: An Elementary Approach to Ideas and Methods, by Richar Courant 
  4. Death Be Not Proud, by John Gunther 
  5. An Outline of Abnormal Psychology, by Murphy Gardner 
  6. Who Speaks for Man by, Norman Cousins 
  7. Astronomy: An Introduction, by Robert Horace Baker 
  8. The Observational Approach to Cosmology, by Edwin Powell Hubble 
  9. Quantitative aspects of the carcinogenic radiations, by Harold Thayer Davis 
  10. Star Short Novels, by Frederik Pohl (Editor) 
  11. Young Archimedes and Other Stories, by Aldous Huxley 
  12. Julius Caesar, by William Shakespeare 
  13. The Symposium, by Plato 
  14. The Autobiography of an Uneducated Man, by Robert Maynard Hutchins 
  15. Timaeus, by Plato 
  16. Extraordinary Popular Delusions and the Madness of Crowds, by Charles Mackay 
  17. But We Were Born Free, by Elmer Holmes Davis 
  18. A History of Western Philosophy, Volume 1: The Classical Mind, by W.T. Jones 
  19. The Portable Greek Reader, by W.H. Auden (Editor) 
  20. In the Matter of J. Robert Oppenheimer, by J. Robert Oppenheimer 
  21. The Berlitz Self-Teacher: German, by Berlitz Editors 
  22. The Uses Of The Past: Profiles Of Former Societies, by Herbert Joseph Muller
  23. Holy Bible
  24. Heat and Thermodynamics: An Intermediate Textbook, by Mark W. Zemansky Physics, by Hugh D. Young 
  25. Kinetic theory of gases: with an introduction to statistical mechanics, by Earle Hesse Kennard 
  26. Thermodynamics: An Advanced Treatment For Chemists And Physicists, by E.A. Guggenheim 
  27. Principles of electricity and electromagnetism, by Gaylord Probasco Harnwell 
  28. Theory of Functions, Parts I and II by Konrad Knopp 
  29. Advanced Calculus, by Wilfred Kaplan 
  30. Complex Analysis: An Introduction to The Theory of Analytic Functions of One Complex Variable (International Series in Pure & Applied Mathematics), by Lars Ahlfors 
  31. Introduction to electric fields: a vector analysis approach, by Walter Edwin Rogers 
  32. Electromagnetics with Applications, by John Daniel Kraus 
  33. Communication circuit fundamentals for radio and communication engineers, by Carl Edwin Smith 

“Efficient Planning under Uncertainty with Macro-actions” by R. He, E. Brunskill and N. Roy

“Deciding how to act in partially observable environments remains an active area of research. Identifying good sequences of decisions is particularly challenging when good control performance requires planning multiple steps into the future in domains with many states. Towards addressing this challenge, we present an online, forward-search algorithm called the Posterior Belief Distribution (PBD). PBD leverages a novel method for calculating the posterior distribution over beliefs that result after a sequence of actions is taken, given the set of observation sequences that could be received during this process. This method allows us to efficiently evaluate the expected reward of a sequence of primitive actions, which we refer to as macro-actions…”

“Multiagent Learning in Large Anonymous Games” by I. A. Kash, E. J. Friedman and J. Y. Halpern (2011)

In large systems, it is important for agents to learn to act effectively, but sophisticated multi-agent learning algorithms generally do not scale. An alternative approach is to find restricted classes of games where simple, efficient algorithms converge. It is shown that stage learning efficiently converges to Nash equilibria in large anonymous games if best-reply dynamics converge. Two features are identified that improve convergence. First, rather than making learning more difficult, more agents are actually beneficial in many settings. Second, providing agents with statistical information about the behavior of others can significantly reduce the number of observations needed.