A cognitive architecture for learning in reactive environments

Previous research in machine learning has viewed the process of empirical discovery as search through a space of 'theoretical' terms. In this paper, we propose a problem space for empirical discovery, specifying six complementary operators for defining new terms that ease the statement of empirical laws. The six types of terms include: numeric attributes (such as PV/T); intrinsic properties (such as mass); composite objects (such as pairs of colliding balls); classes of objects (such as acids and alkalis); composite relations (such as chemical reactions); and classes of relations (such as combustion/oxidation). We review existing machine discovery systems in light of this framework, examining which parts of the problem space were, covered by these systems. Finally, we outline an integrated discovery system (IDS) we are constructing that includes all six of the operators and which should be able to discover a broad range of empirical laws

A framework for empirical discovery

Previous research in machine learning has viewed the process of empirical discovery as search through a space of 'theoretical' terms. In this paper, we propose a problem space for empirical discovery, specifying six complementary operators for defining new terms that ease the statement of empirical laws. The six types of terms include: numeric attributes (such as PV/T); intrinsic properties (such as mass); composite objects (such as pairs of colliding balls); classes of objects (such as acids and alkalis); composite relations (such as chemical reactions); and classes of relations (such as combustion/oxidation). We review existing machine discovery systems in light of this framework, examining which parts of the problem space were, covered by these systems. Finally, we outline an integrated discovery system (IDS) we are constructing that includes all six of the operators and which should be able to discover a broad range of empirical laws

An Integrated Semantic Web Service Discovery and Composition Framework

In this paper we present a theoretical analysis of graph-based service composition in terms of its dependency with service discovery. Driven by this analysis we define a composition framework by means of integration with fine-grained I/O service discovery that enables the generation of a graph-based composition which contains the set of services that are semantically relevant for an input-output request. The proposed framework also includes an optimal composition search algorithm to extract the best composition from the graph minimising the length and the number of services, and different graph optimisations to improve the scalability of the system. A practical implementation used for the empirical analysis is also provided. This analysis proves the scalability and flexibility of our proposal and provides insights on how integrated composition systems can be designed in order to achieve good performance in real scenarios for the Web.Comment: Accepted to appear in IEEE Transactions on Services Computing 201

Take another little piece of my heart: a note on bridging cognition and emotions

Science urges philosophy to be more empirical and philosophy urges science to be more reflective. This markedly occurred along the “discovery of the artificial” (CORDESCHI 2002): in the early days of Cybernetics and Artificial Intelligence (AI) researchers aimed at making machines more cognizant while setting up a framework to better understand human intelligence. By and large, those genuine goals still hold today, whereas AI has become more concerned with specific aspects of intelligence, such as (machine) learning, reasoning, vision, and action. As a matter of fact, the field suffers from a chasm between two formerly integrated aspects. One is the engineering endeavour involving the development of tools, e.g., autonomous systems for driving cars as well as software for semantic information retrieval. The other is the philosophical debate that tries to answer questions concerning the nature of intelligence. Bridging these two levels can indeed be crucial in developing a deeper understanding of minds. An opportunity might be offered by the cogent theme of emotions. Traditionally, computer science, psychological and philosophical research have been compelled to investigate mental processes that do not involve mood, emotions and feelings, in spite of Simon’s early caveat (SIMON 1967) that a general theory of cognition must incorporate the influences of emotion. Given recent neurobiological findings and technological advances, the time is ripe to seriously weigh this promising, albeit controversial, opportunity

A comparative survey of integrated learning systems

This paper presents the duction framework for unifying the three basic forms of inference - deduction, abduction, and induction - by specifying the possible relationships and influences among them in the context of integrated learning. Special assumptive forms of inference are defined that extend the use of these inference methods, and the properties of these forms are explored. A comparison to a related inference-based learning frame work is made. Finally several existing integrated learning programs are examined in the perspective of the duction framework

A conceptual architecture for semantic web services development and deployment

Several extensions of the Web Services Framework (WSF) have been proposed. The combination with Semantic Web technologies introduces a notion of semantics, which can enhance scalability through automation. Service composition to processes is an equally important issue. Ontology technology – the core of the Semantic Web – can be the central building block of an extension endeavour. We present a conceptual architecture for ontology-based Web service development and deployment. The development of service-based software systems within the WSF is gaining increasing importance. We show how ontologies can integrate models, languages, infrastructure, and activities within this architecture to support reuse and composition of semantic Web services

Correlation, price discovery and co-movement of ABS and equity

Asset-backed securitization (ABS) has become a viable and increasingly attractive risk management and refinancing method either as a standalone form of structured finance or as securitized debt in Collateralized Debt Obligations (CDO). However, the absence of industry standardization has prevented rising investment demand from translating into market liquidity comparable to traditional fixed income instruments, in all but a few selected market segments. Particularly low financial transparency and complex security designs inhibits profound analysis of secondary market pricing and how it relates to established forms of external finance. This paper represents the first attempt to measure the intertemporal, bivariate causal relationship between matched price series of equity and ABS issued by the same entity. In a two-dimensional linear system of simultaneous equations we investigate the short-term dynamics and long-term consistency of daily secondary market data from the U.K. Sterling ABS/MBS market and exchange traded shares between 1998 and 2004 with and without the presence of cointegration. Our causality framework delivers compelling empirical support for a strong co-movement between matched price series of ABS-equity pairs, where ABS markets seem to contribute more to price discovery over the long run. Controlling for cointegration, risk-free interest and average market risk of corporate debt hardly alters our results. However, once we qualify the magnitude and direction of price discovery on various security characteristics, such as the ABS asset class, we find that ABS-equity pairs with large-scale CMBS/RMBS and credit card/student loan ABS reveal stronger lead-lag relationships and joint price dynamics than whole business ABS. JEL Classifications: G10, G12, G2

Firm development as an integrated process: with evidence from the General Motors-Fisher Body case

This paper argues that an adequate approach to the firm should be able to accommodate the complexities of actual firm development. The latter is conceptualized in terms of three general stages: prime movers or drivers of change, change processes, and change attractors. Furthermore, any "real-world" firm is both a technical and an institutional unit. To emphasize the importance of "real firm" analysis, the discussion presented here revolves around an understanding of the much considered case of General Motors and Fisher Body integration has developed over time. Generalization from this case suggests that an integrated view of the firm is necessary that combines the three stages and the two bases (technical and institutional). Six general perspectives on the firm are identified as having technical or institutional bases that are relevant in each of the three stages. This integrated approach to the firm is explored in terms of the general topic of firm development. It is concluded that, without an integrated approach to firm development, a potentially biased or incomplete analysis can result