There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

An Introduction to MultiAgent Systems.

The Semantic Web should enable greater access not only to content but also to services on the Web. Users and software agents should be able to discover, invoke, compose, and monitor Web resources offering particular services and having particular properties. As part of the DARPA Agent Markup Language program, we have begun to develop an ontology of services, called DAML-S, that will make these functionalities possible. In this paper we describe the overall structure of the ontology, the service profile for advertising services, and the process model for the detailed description of the operation of services. We also compare DAML-S with several industry efforts to define standards for characterizing services on the Web.

/pdf/daml-s-semantic-markup-for-web-services-2bz2kpfwet.pdf

DAML-S: semantic markup for web services

We present an approach to hybrid semantic Web service matching that complements logic based reasoning with approximate matching based on syntactic IR based similarity computations. The hybrid matchmaker, called OWLS-MX, applies this approach to services and requests specified in OWL-S. Experimental results of measuring performance and scalability of different variants of OWLS-MX show that under certain constraints logic based only approaches to OWL-S service I/O matching can be significantly outperformed by hybrid ones.

/pdf/automated-semantic-web-service-discovery-with-owls-mx-3i8wfj09ur.pdf

Automated semantic web service discovery with OWLS-MX

Service matchmaking among heterogeneous software agents in the Internet is usually done dynamically and must be efficient. There is an obvious trade-off between the quality and efficiency of matchmaking on the Internet. We define a language called Larks for agent advertisements and requests, and present a flexible and efficient matchmaking process that uses Larks. The Larks matchmaking process performs both syntactic and semantic matching, and in addition allows the specification of concepts (local ontologies) via ITL, a concept language. The matching process uses five different filters: context matching, profile comparison, similarity matching, signature matching and constraint matching. Different degrees of partial matching can result from utilizing different combinations of these filters. We briefly report on our implementation of Larks and the matchmaking process in Java. Fielded applications of matchmaking using Larks in several application domains for systems of information agents are ongoing efforts.

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Larks: Dynamic Matchmaking Among Heterogeneous Software Agents in Cyberspace

The amount of services and deployed software agents in the most famous offspring of the Internet, the World Wide Web, is exponentially increasing. In addition, the Internet is an open environment, where information sources, communication links and agents themselves may appear and disappear unpredictably. Thus, an effective, automated search and selection of relevant services or agents is essential for human users and agents as well. We distinguish three general agent categories in the Cyberspace, service providers, service requester, and middle agents. Service providers provide some type of service, such as finding information, or performing some particular domain specific problem solving. Requester agents need provider agents to perform some service for them. Agents that help locate others are called middle agents[2]. Matchmaking is the process of finding an appropriate provider for a requester through a middle agent, and has the following general form: (1) Provider agents advertise their capabilities to middle agents, (2) middle agents store these advertisements, (3) a requester asks some middle agent whether it knows of providers with desired capabilities, and (4) the middle agent matches the request against the stored advertisements and returns the result, a subset of the stored advertisements. While this process at first glance seems very simple, it is complicated by the fact that not only local information sources but even providers and requesters in the Cyberspace are usually heterogeneous and incapable of understanding each other. This gives rise to the need for a common language for describing the capabilities and requests of software agents in a conve-

/pdf/dynamic-service-matchmaking-among-agents-in-open-information-1aj8sr8xux.pdf

Dynamic service matchmaking among agents in open information environments

We present an OWL-S service composition planner, called OWLS-Xplan, that allows for fast and flexible composition of OWL-S services in the semantic Web. OWLS-Xplan converts OWL-S 1.1 services to equivalent problem and domain descriptions thatare specified inthe planning domain description language PDDL 2.1, and invokes an efficient AI planner Xplan to generate a service composition plan sequence that satisfies a given goal. Xplan extends an action based FastForward-planner with a HTN planning and re-planning component.

/pdf/semantic-web-service-composition-planning-with-owls-xplan-28pvl3rp9k.pdf

Semantic Web Service Composition Planning with OWLS-Xplan ∗

https://www.dfki.de/~klusch/i2s/owlsmx-08-final.pdf

Matthias Klusch

Papers

Automated semantic web service discovery with OWLS-MX

Larks: Dynamic Matchmaking Among Heterogeneous Software Agents in Cyberspace

Dynamic service matchmaking among agents in open information environments

Semantic Web Service Composition Planning with OWLS-Xplan ∗

OWLS-MX: A hybrid Semantic Web service matchmaker for OWL-S services