Experiments in reactive constraint logic programming1This paper is the complete version of a previous paper published in [14].1

AbstractIn this paper we study a reactive extension of constraint logic programming (CLP). Our primary concerns are search problems in a dynamic environment, where interactions with the user (e.g. in interactive multi-criteria optimization problems) or interactions with the physical world (e.g. in time evolving problems) can be modeled and solved efficiently. Our approach is based on a complete set of query manipulation commands for both the addition and the deletion of constraints and atoms in the query. We define a fully incremental model of execution which, contrary to other proposals, retains as much information as possible from the last derivation preceding a query manipulation command. The completeness of the execution model is proved in a simple framework of transformations for CSLD derivations, and of constraint propagation seen as chaotic iteration of closure operators. A prototype implementation of this execution model is described and evaluated on two applications

A Functional Taxonomy of Music Generation Systems

Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys and facilitates the identification of grand challenges to set the stage for new breakthroughs.Comment: survey, music generation, taxonomy, functional survey, survey, automatic composition, algorithmic compositio

Fexprs as the basis of Lisp function application; or, $vau: the ultimate abstraction

Abstraction creates custom programming languages that facilitate programming for specific problem domains. It is traditionally partitioned according to a two-phase model of program evaluation, into syntactic abstraction enacted at translation time, and semantic abstraction enacted at run time. Abstractions pigeon-holed into one phase cannot interact freely with those in the other, since they are required to occur at logically distinct times. Fexprs are a Lisp device that subsumes the capabilities of syntactic abstraction, but is enacted at run-time, thus eliminating the phase barrier between abstractions. Lisps of recent decades have avoided fexprs because of semantic ill-behavedness that accompanied fexprs in the dynamically scoped Lisps of the 1960s and 70s. This dissertation contends that the severe difficulties attendant on fexprs in the past are not essential, and can be overcome by judicious coordination with other elements of language design. In particular, fexprs can form the basis for a simple, well-behaved Scheme-like language, subsuming traditional abstractions without a multi-phase model of evaluation. The thesis is supported by a new Scheme-like language called Kernel, created for this work, in which each Scheme-style procedure consists of a wrapper that induces evaluation of operands, around a fexpr that acts on the resulting arguments. This arrangement enables Kernel to use a simple direct style of selectively evaluating subexpressions, in place of most Lisps\u27 indirect quasiquotation style of selectively suppressing subexpression evaluation. The semantics of Kernel are treated through a new family of formal calculi, introduced here, called vau calculi. Vau calculi use direct subexpression-evaluation style to extend lambda calculus, eliminating a long-standing incompatibility between lambda calculus and fexprs that would otherwise trivialize their equational theories. The impure vau calculi introduce non-functional binding constructs and unconventional forms of substitution. This strategy avoids a difficulty of Felleisen\u27s lambda-v-CS calculus, which modeled impure control and state using a partially non-compatible reduction relation, and therefore only approximated the Church-Rosser and Plotkin\u27s Correspondence Theorems. The strategy here is supported by an abstract class of Regular Substitutive Reduction Systems, generalizing Klop\u27s Regular Combinatory Reduction Systems

DNA sequence similarity recognition by hybridization to short oligomers

Methods are disclosed for the comparison of nucleic acid sequences. Data is generated by hybridizing sets of oligomers with target nucleic acids. The data thus generated is manipulated simultaneously with respect to both (i) matching between oligomers and (ii) matching between oligomers and putative reference sequences available in databases. Using data compression methods to manipulate this mutual information, sequences for the target can be constructed

The Morphosyntactic Parser: Developing and testing a sentence processor that uses underspecified morphosyntactic features

This dissertation presents a fundamentally new approach to describe not only the architecture of the language system but also the processes behind its capability to predict, analyze and integrate linguistic input into its representation in a parsimonious way. By the example of morphosyntax, underspecified case, the use of decomposed, binary case, number and gender features to account for syncretism, will offer insights into both: Carrying over this idea to language processing raises the question whether the language system—limited in its storage capacity—makes use of similar means of representational parsimony during the processing of linguistic input. This thesis will propose a processing system that is tightly related to the aforementioned architectural assumptions of morphosyntactically underspecified lexical entries as a parsimonious way of representation. In that sense, prediction is viewed as the language system’s drive to avoid feature deviance from one incrementally available linguistic element to another subsequentially incoming one. In this way, the parser’s goal is to maintain minimal feature deviance or at best feature identity to keep processing load as low as possible. This approach allows for position-dependent hypothesis with regard to the expected processing load. To test the processor’s claims, the electrophysiological data of a series of event-related brain potential (ERP) experiments will be presented. The results suggest that with the input’s increased feature deviance the amplitude of an ERP component sensitive for prediction error increases. In comparison to that, elements that rather maintain feature identity and that do not lack or introduce additional features to the analysis do not increase processing difficulty. These results indicate that the language processing system uses the available features of morphosyntactically underspecified mental entries to build up larger constituents. The experiments showed, that this buildup process is determined by the language system’s drive to avoid feature deviance

Natively probabilistic computation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2009.Includes bibliographical references (leaves 129-135).I introduce a new set of natively probabilistic computing abstractions, including probabilistic generalizations of Boolean circuits, backtracking search and pure Lisp. I show how these tools let one compactly specify probabilistic generative models, generalize and parallelize widely used sampling algorithms like rejection sampling and Markov chain Monte Carlo, and solve difficult Bayesian inference problems. I first introduce Church, a probabilistic programming language for describing probabilistic generative processes that induce distributions, which generalizes Lisp, a language for describing deterministic procedures that induce functions. I highlight the ways randomness meshes with the reflectiveness of Lisp to support the representation of structured, uncertain knowledge, including nonparametric Bayesian models from the current literature, programs for decision making under uncertainty, and programs that learn very simple programs from data. I then introduce systematic stochastic search, a recursive algorithm for exact and approximate sampling that generalizes a popular form of backtracking search to the broader setting of stochastic simulation and recovers widely used particle filters as a special case. I use it to solve probabilistic reasoning problems from statistical physics, causal reasoning and stereo vision. Finally, I introduce stochastic digital circuits that model the probability algebra just as traditional Boolean circuits model the Boolean algebra.(cont.) I show how these circuits can be used to build massively parallel, fault-tolerant machines for sampling and allow one to efficiently run Markov chain Monte Carlo methods on models with hundreds of thousands of variables in real time. I emphasize the ways in which these ideas fit together into a coherent software and hardware stack for natively probabilistic computing, organized around distributions and samplers rather than deterministic functions. I argue that by building uncertainty and randomness into the foundations of our programming languages and computing machines, we may arrive at ones that are more powerful, flexible and efficient than deterministic designs, and are in better alignment with the needs of computational science, statistics and artificial intelligence.by Vikash Kumar Mansinghka.Ph.D

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Context-aware Trace Contracts

The behavior of concurrent, asynchronous procedures depends in general on the call context, because of the global protocol that governs scheduling. This context cannot be specified with the state-based Hoare-style contracts common in deductive verification. Recent work generalized state-based to trace contracts, which permit to specify the internal behavior of a procedure, such as calls or state changes, but not its call context. In this article we propose a program logic of context-aware trace contracts for specifying global behavior of asynchronous programs. We also provide a sound proof system that addresses two challenges: To observe the program state not merely at the end points of a procedure, we introduce the novel concept of an observation quantifier. And to combat combinatorial explosion of possible call sequences of procedures, we transfer Liskov's principle of behavioral subtyping to the analysis of asynchronous procedures

Narrative Information Extraction with Non-Linear Natural Language Processing Pipelines

Computational narrative focuses on methods to algorithmically analyze, model, and generate narratives. Most current work in story generation, drama management or even literature analysis relies on manually authoring domain knowledge in some specific formal representation language, which is expensive to generate. In this dissertation we explore how to automatically extract narrative information from unannotated natural language text, how to evaluate the extraction process, how to improve the extraction process, and how to use the extracted information in story generation applications. As our application domain, we use Vladimir Propp's narrative theory and the corresponding Russian and Slavic folktales as our corpus. Our hypothesis is that incorporating narrative-level domain knowledge (i.e., Proppian theory) to core natural language processing (NLP) and information extraction can improve the performance of tasks (such as coreference resolution), and the extracted narrative information. We devised a non-linear information extraction pipeline framework which we implemented in Voz, our narrative information extraction system. Finally, we studied how to map the output of Voz to an intermediate computational narrative model and use it as input for an existing story generation system, thus further connecting existing work in NLP and computational narrative. As far as we know, it is the first end-to-end computational narrative system that can automatically process a corpus of unannotated natural language stories, extract explicit domain knowledge from them, and use it to generate new stories. Our user study results show that specific error introduced during the information extraction process can be mitigated downstream and have virtually no effect on the perceived quality of the generated stories compared to generating stories using handcrafted domain knowledge.Ph.D., Computer Science -- Drexel University, 201