A lazy non-deterministic functional language.

This paper addresses the starting point of the refinement process: the naive program. Although a naive program provides a useful starting point for refinement and is complete with respect to all the possible outcomes it usually exhibits exponential computational complexity which prohibits using the initial naive system as a prototype

A lazy non-deterministic functional language.

This paper addresses the starting point of the refinement process: the naive program. Although a naive program provides a useful starting point for refinement and is complete with respect to all the possible outcomes it usually exhibits exponential computational complexity which prohibits using the initial naive system as a prototype

Finding structure in language

Since the Chomskian revolution, it has become apparent that natural language is richly structured, being naturally represented hierarchically, and requiring complex context sensitive rules to define regularities over these representations. It is widely assumed that the richness of the posited structure has strong nativist implications for mechanisms which might learn natural language, since it seemed unlikely that such structures could be derived directly from the observation of linguistic data (Chomsky 1965).This thesis investigates the hypothesis that simple statistics of a large, noisy, unlabelled corpus of natural language can be exploited to discover some of the structure which exists in natural language automatically. The strategy is to initially assume no knowledge of the structures present in natural language, save that they might be found by analysing statistical regularities which pertain between a word and the words which typically surround it in the corpus.To achieve this, various statistical methods are applied to define similarity between statistical distributions, and to infer a structure for a domain given knowledge of the similarities which pertain within it. Using these tools, it is shown that it is possible to form a hierarchical classification of many domains, including words in natural language. When this is done, it is shown that all the major syntactic categories can be obtained, and the classification is both relatively complete, and very much in accord with a standard linguistic conception of how words are classified in natural language.Once this has been done, the categorisation derived is used as the basis of a similar classification of short sequences of words. If these are analysed in a similar way, then several syntactic categories can be derived. These include simple noun phrases, various tensed forms of verbs, and simple prepositional phrases. Once this has been done, the same technique can be applied one level higher, and at this level simple sentences and verb phrases, as well as more complicated noun phrases and prepositional phrases, are shown to be derivable

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

The affordance-based concept

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (p. 89-95).Natural language use relies on situational context. The meaning of words and utterances depend on the physical environment and the goals and plans of communication partners. These facts should be central to theories of language and automatic language understanding systems. Instead, they are often ignored, leading to partial theories and systems that cannot fully interpret linguistic meaning. I introduce a new computational theory of conceptual structure that has as its core claim that concepts are neither internal nor external to the language user, but instead span the objective-subjective boundary. This theory proposes interaction and prediction as a central theme, rather than solely emphasizing deducing, sensing or acting. To capture the possible interactions between subject and object, the theory relies on the notion of perceived affordances: structured units of interaction that can be used for prediction at certain levels of abstraction. By using perceived affordances as a basis for language understanding, the theory accounts for many aspects of the situated nature of human language use. It provides a unified solution to a number of other demands on a theory of language understanding including conceptual combination, prototypicality effects, and the generative nature of lexical items.(cont.) To support the theory, I describe an implementation that relies on probabilistic hierarchical plan recognition to predict possible interactions. The elements of a recognized plan provide an instance of perceived affordances which are used by a linguistic parser to ground the meaning of words and grammatical constituents. Evaluations performed in a multiuser role playing game environment show that this implementation captures the meaning of free-form spontaneous directive speech acts that cannot be understood without taking into account the intentional and physical situation of speaker and listener.by Peter John Gorniak.Ph.D

Local Halting Criteria for Stochastic Diffusion Search Using Nature-inspired Quorum Sensing

Stochastic Diffusion Search (SDS) is a Swarm Intelligence algorithm in which a population of homogeneous agents locate a globally optimal solution in a search space through repeated iteration of partial evaluation and communication of hypotheses. In this work a variant of SDS, Quorum Sensing SDS (QSSDS), is developed in which agents employ only local knowledge to determine whether the swarm has successfully converged on a solution of sufficient quality, and should therefore halt. It is demonstrated that this criterion performs at least as well as SDS in locating the optimal solution in the search space, and that the parameters of Quorum Sensing SDS may be tuned to optimise behaviour towards a fast decision or a high quality solution. Additionally it is shown that Quorum Sensing SDS can be used as a model of distributed decision making and hence makes testable predictions about the capacities and abilities of swarms in nature

The Road to General Intelligence

Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation tasks.The recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory. • Details the pragmatic requirements for real-world General Intelligence. • Describes how machine learning fails to meet these requirements. • Provides a philosophical basis for the proposed approach. • Provides mathematical detail for a reference architecture. • Describes a research program intended to address issues of concern in contemporary AI. The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and mathematics.The target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts This is an open access book