1211760
Heuristic Search Value Iteration for POMDPs
We present a novel POMDP planning algorithm called heuristic search value iteration (HSVI). HSVI is an anytime algorithm that returns a policy and a provable bound on its regret with respect to the optimal policy. HSVI gets its power by combining two well-known techniques: attention-focusing search heuristics and piecewise linear convex representations of the value function. HSVI's soundness and convergence have been proven. On some bench-mark problems from the literature, HSVI displays speedups of greater than 100 with respect to other state-of-the-art POMDP value iteration algorithms. We also apply HSVI to a new rover exploration problem 10 times larger than most POMDP problems in the literature.
3284353
Point-Based POMDP Algorithms: Improved Analysis and Implementation
Existing complexity bounds for point-based POMDP value iteration algorithms focus either on the curse of dimensionality or the curse of history. We derive a new bound that relies on both and uses the concept of discounted reachability; our conclusions may help guide future algorithm design. We also discuss recent improvements to our (point-based) heuristic search value iteration algorithm. Our new implementation calculates tighter initial bounds, avoids solving linear programs, and makes more effective use of sparsity.
7247766
Semantic and Generative Models for Lossy Text Compression
The complementary paradigms of text compression and image compression suggest that there may be potential for applying methods developed for one domain to the other. In image coding, lossy techniques yield compression factors that are vastly superior to those of the best lossless schemes, and we show that this is also the case for text. This paper investigates the resulting tradeoff between subjective quality of the transmission and its compression factor. Two different methods are described, which can be combined into an extremely effective technique that provides far better compression than the present state of the art and yet preserves a reasonable degree of perceived match between the original and received text. The major challenge for lossy text compression is the quantitative evaluation of the quality of this match.
11264916
Learning language using genetic algorithms
Strict pattern-based methods of grammar induction are often frustrated by the apparently inexhaustible variety of novel word combinations in large corpora. Statistical methods offer a possible solution by allowing frequent well-formed expressions to overwhelm the infrequent ungrammatical ones. They also have the desirable property of being able to construct robust grammars from positive instances alone. Unfortunately, the “zero-frequency” problem entails assigning a small probability to all possible word patterns, thus ungrammatical n-grams become as probable as unseen grammatical ones. Further, such grammars are unable to take advantage of inherent lexical properties that should allow infrequent words to inherit the syntactic properties of the class to which they belong.
16631800
Fast convergence with a greedy tag-phrase dictionary
Lexical categories have been shown to assist in giving good compression results when incorporated into context models. This paper describes a greedy dictionary-based model that maintains a dictionary of tag-phrases, along with separate lexicons for each unique tag. The text is tagged with part-of-speech (POS) labels and then given to the encoder, which uses the tags to construct the phrase dictionary in a manner similar to LZ78. The output is a sequence of arithmetically encoded phrase number coupled with the information needed to match the correct word with each tag in the phrase. Each unique word (defined as each novel word/tag pair) is transmitted once when it is first encountered, then retained in the appropriate dictionary and thereafter arithmetically encoded according to the empirical distribution for that dictionary whenever the word is encountered. We present results from some empirical tests showing that this "tag-phrase dictionary" technique achieves nearly identical compression as that obtainable using PPM, an explicit-context model. This goes against the widely held view that greedy dictionary schemes require much larger samples of text before they can compete with statistical context methods. Some interesting theoretical issues pertaining to text compression in general are implied, and these are also discussed.
10411630
The ADEPT Concept-Based Digital Learning Environment
We describe the design and application of a Digital Learning Environment (DLE) that is integrated with the collections and services of the Alexandria Digital Library (ADL). This DLE is in operational use in undergraduate teaching environments. Its design and development incorporate the assumption that deep understanding of both scientific phenomena and scientific methods is facilitated when learning materials are explicitly organized, accessed and presented at the level of granularity of appropriate sets of scientific concepts and their interrelationships. The DLE supports services for the creating, searching, and displaying: (1) knowledge bases (KBs) of strongly structured models of scientific concepts; (2) DL collections of information objects organized and accessible by the concepts of the KBs; and (3) collections of presentation materials, such as lectures and laboratory materials, that are organized as trajectories through the KB of concepts.
1624330
Recipes for digital living [ubiquitous computing in consumer electronics]
As networked devices become increasingly ubiquitous, the potential benefits to users increase exponentially. However, this potential can't be fully realized if it is entirely up to developers and manufacturers to expose the combined functionality buried in the expanding network. Recipes for digital living - created and modified by users themselves - offer an easy-to-understand, logically consistent way to capture and share the various ensembles possible in the expanding world of networked components.