Greedy search is commonly used in an attempt to generate solutions quickly at the expense of completeness and optimality. In this work, we consider learning sets of weighted action-selection rules for guiding greedy search with application to automated planning. We make two primary contributions over prior work on learning for greedy search. First, we introduce weighted sets of action-selection rules as a new form of control knowledge for greedy search. Prior work has shown the utility of action-selection rules for greedy search, but has treated the rules as hard constraints, resulting in brittleness. Our weighted rule sets allow multiple rules to vote, helping to improve robustness to noisy rules. Second, we give a new iterative learning algorithm for learning weighted rule sets based on RankBoost, an efficient boosting algorithm for ranking. Each iteration considers the actual performance of the current rule set and directs learning based on the observed search errors. This is in contrast to most prior approaches, which learn control knowledge independently of the search process. Our empirical results have shown significant promise for this approach in a number of domains.

This paper presents a variation of Apriori algorithm that includes the role of domain expert to guide and speed up the overall knowledge discovery task. Usually, the user is interested in finding relationships between certain attributes instead of the whole dataset. Moreover, he can help the mining algorithm to select the target database which in turn takes less time to find the desired association rules. Variants of the standard Apriori and Interactive Apriori algorithms have been run on artificial datasets. The results show that incorporating user's preference in selection of target attribute helps to search the association rules efficiently both in terms of space and time.

A Digital Product Memory provides a digital diary of the complete product life cycle that is embedded in the product itself using smart wireless sensor technology. The data is hereby gathered by recording relevant ambient parameters in digital form. In this paper, we present the architecture and cost-efficient implementation of an autonomous digital product memory that generates and interprets its diary using rule-based reasoning methods. As we assume an open, heterogeneous sensor infrastructure, we rely on standard syntax and semantics provided by the Web Ontology Language OWL. The digital product memory collects and provides data using the OWL fragment OWL2 RL which can be processed with standard rule engines. As rule engine we use CLIPS on embedded hardware and exemplify the application of the digital product memory e.g. for predictive maintenance.

Recently, new approaches to adaptive control have sought to reformulate the problem as a minimization of a relative entropy criterion to obtain tractable solutions. In particular, it has been shown that minimizing the expected deviation from the causal input-output dependencies of the true plant leads to a new promising stochastic control rule called the Bayesian control rule. This work proves the convergence of the Bayesian control rule under two sufficient assumptions: boundedness, which is an ergodicity condition; and consistency, which is an instantiation of the sure-thing principle.

Preface This is a companion paper to Conscious Intelligent Systems Part 1 by the same author (1), which discusses a possible evolutionary path for consciousness and intelligence from simple systems to human level consciousness and intelligence. Man has long been held to be a thinking animal, his thought processes have been held to be the reason for his superiority over the animals. The grand aim of AI has always been to make an entity that can think. Turing took up this very question in his paper (2) on whether machines can think. On the more prosaic roads that real AI has been forced to follow, such grand questions have almost died down. Another major trigger for the demise has been Searle's Chinese Room (3) parody . With this rather cunning device, Searle set the cat among the pigeons and has helped induce self-doubt in the best of AI theorists. One of the major triggers towards Searle's views was language, whether syntax suffices for semantics and therefore understanding. From our evolutionary learning system perspective, which we discuss in Part I of this discussion, we see that all these processes are tied together, the processes of consciousness, intelligence, mind, thought, and language. In a bid to show the interconnectedness of these factors, we take up the question of understanding and its communication. Similar to our treatment of the subject of consciousness based intelligent systems in Part 1, here we treat understanding from first principles. Understanding In the real world when we use the term understanding, it has two main attributes; one is the capacity to infer, the other is the capacity to recognize or discern. In computing and AI contexts the word understanding is arguably tilted more in favor of inference than perception or cognition, in normal life and in the natural kingdom the reverse is true. This is primarily because AI's aims and present status look elemental when compared to the entities of the natural world. The other reason is that AI entities find it easier to infer than cognize, which is in itself a reflection of their design sources and its aims. For the purposes of this discussion the term understanding implies the natural version, a mix of cognition and inference. If we start from first principles, it is clear that for a rule to emerge out of a set of raw data, an inferential process has to run on it. This process could be a formal inferential process or a process that is driven by the needs of economy or efficiency. Rules need not always rise out of intentional activity, for instance the interaction of water flowing from an open tap into a pot already full of water can create a set of rules that disallow further water entry, limit mixing and regulate overflow, many natural rules rise from interactions like these.

Outsourcing of complex IT infrastructure to IT service providers has increased substantially during the past years. IT service providers must be able to fulfil their service-quality commitments based upon predefined Service Level Agreements (SLAs) with the service customer. They need to manage, execute and maintain thousands of SLAs for different customers and different types of services, which needs new levels of flexibility and automation not available with the current technology. The complexity of contractual logic in SLAs requires new forms of knowledge representation to automatically draw inferences and execute contractual agreements. A logic-based approach provides several advantages including automated rule chaining allowing for compact knowledge representation as well as flexibility to adapt to rapidly changing business requirements. We suggest adequate logical formalisms for representation and enforcement of SLA rules and describe a proof-of-concept implementation. The article describes selected formalisms of the ContractLog KR and their adequacy for automated SLA management and presents results of experiments to demonstrate flexibility and scalability of the approach.

A fuzzy logic based classification engine has been developed for classifying mass spectra obtained with an imaging internal source Fourier transform mass spectrometer (I^2LD-FTMS). Traditionally, an operator uses the relative abundance of ions with specific mass-to-charge (m/z) ratios to categorize spectra. An operator does this by comparing the spectrum of m/z versus abundance of an unknown sample against a library of spectra from known samples. Automated positioning and acquisition allow I^2LD-FTMS to acquire data from very large grids, this would require classification of up to 3600 spectrum per hour to keep pace with the acquisition. The tedious job of classifying numerous spectra generated in an I^2LD-FTMS imaging application can be replaced by a fuzzy rule base if the cues an operator uses can be encapsulated. We present the translation of linguistic rules to a fuzzy classifier for mineral phases in basalt. This paper also describes a method for gathering statistics on ions, which are not currently used in the rule base, but which may be candidates for making the rule base more accurate and complete or to form new rule bases based on data obtained from known samples. A spatial method for classifying spectra with low membership values, based on neighboring sample classifications, is also presented.

An important problem to be addr essed within Event-Driven Architecture (EDA) is how to correctly and efficiently capture and process the event/action-based logic. This paper endeavors to bridge the gap between the Knowledge Representation (KR) approaches based on durable events/actions and such formalisms as event calculus, on one hand, and event-condition-action (ECA) reaction rules extending the approach of active databases that view events as instantaneous occurrences and/or sequences of events, on the other. We propose formalism based on reaction rules (ECA rules) and a novel interval-based event logic and present concrete RuleML-based syntax, semantics and implementation. We further evaluate this approach theoretically, experimentally and on an example derived from common industry use cases and illustrate its benefits.

Event-driven reactive functionalities are an urgent need in nowadays distributed service-oriented applications and (Semantic) Web-based environments. An important problem to be addressed is how to correctly and efficiently capture and process the event-based behavioral, reactive logic represented as ECA rules in combination with other conditional decision logic which is represented as derivation rules. In this paper we elaborate on a homogeneous integration approach which combines derivation rules, reaction rules (ECA rules) and other rule types such as integrity constraint into the general framework of logic programming. The developed ECA-LP language provides expressive features such as ID-based updates with support for external and self-updates of the intensional and extensional knowledge, transactions including integrity testing and an event algebra to define and process complex events and actions based on a novel interval-based Event Calculus variant.

Doctoral Symposium of MATES'06 Abstract: Automated management and monitoring of service contracts like Service Level Agreements (SLAs) or higher-level policies is vital for efficient and reliable distributed se rvice-oriented architectures (SOA) with high quality of service (QoS) levels. IT service provider need to manage, exec ute and maintain thousands of SLAs for different customers and different types of services, which needs new levels of flexibility and automation not available with the current technology. I propose a novel rule-based knowledge representation (KR) for SLA rules and a respective rule-based service level management (RBSLM) framework. My rule-based approach based on logic programming pr ovides several advantages including automated rule chaining allowing for compact knowledge representation and high levels of automation as well as flexibility to adapt to rapidly changing business requirements. Therewith, I address an urgent need service-oriented businesses do have nowadays which is to dynamically change their business and contractual logic in order to adapt to rapidly changing business environments and to overcome the restricting nature of slow change cycles.