SHOP's preconditions can include logical inferences, SHOP's expressive power can be used to create Here, we summarize the SHOP algorithm's primary SHOP algorithm is shown in figure 1. S is a state, T is a list of tasks, and D is the knowledge base (methods, operators, and Horn-clause axioms). As long as the procedure for inferring m's preconditions from S is a sound and complete inference procedure (such as Horn-clause theorem proving), the For example, the Horn clauses can include calls to attached procedures for numeric computations (for example, "distance(UofMD,BWI) 50" in the previous example), or (in some of the implementations) any other procedure calls defined by the user. In our experiments (Nau et al. 1999), SHOP generated SHOP's higher level of expressivity made PLAN and SHOP was not too different. We intend to make more optimizations in the near future. HICAP is shown in figure 4. HICAP (Aha and Breslow 1997).

Since 1994, GE Plastics has employed a case-based reasoning (CBR) tool that determines color formulas that match requested colors. This tool, called FormTool, has saved GE millions of dollars in productivity and material (that is, colorant) costs. The technology developed in FormTool has been used to create an online color-selection tool for our customers called ColorXpress Select. A customer innovation center has been developed around the FormTool software. In offices and factories, in hospitals, homes, and schools, on the road and in outer space, products made with GE materials make life simpler, safer, and more comfortable for people every day.

The Sixth International Conference on Case-Based Reasoning (ICCBR-05) took place from 23 August through 26 August 2005 at the downtown campus of De-Paul University, in the heart of Chicago's downtown Loop. The conference program included Industry Day, four workshops, and two days of technical paper presentations divided into poster sessions and a single plenary track. This report describes the conference in detail. Days 3 and 4 comprised presentations and posters on theoretical and applied CBR research, as well as invited talks from two distinguished scholars: Derek Bridge, the University College Cork, and Craig Knoblock, the University of Southern California. ICCBR-05 received 74 paper submissions from 19 countries around the world.

In this document we report on the MDAI 2006 conference that was held in Tarragona (Catalonia, Spain) in April 2006. MDAI 2006, the third in this series of conferences, was held in Tarragona (Catalonia, Spain) from April 3-5. The conference consisted of four plenary talks and about 40 presentations. Regular papers were devoted to the different aspects related to decision: theory, tools, and applications. The papers on tools described methods for model construction (selection of the operators and of their parameters using, for example, analytic hierarchy process [AHP] techniques) as well as measures and indices for evaluating operators (such as orness).

TEXTAL is a computer program that automaticallyinterprets electron density maps to determine the atomic structures of proteins through X-ray crystallography. Electron density maps are traditionally interpreted by visually fitting atoms into density patterns. This manual process can be time-consuming and error prone, even for expert crystallographers. To automate the process, TEXTAL employs a variety of AI and pattern-recognition techniques that emulate the decision-making processes of domain experts. In this article, we discuss the various ways AI technology is used in TEXTAL, including neural networks, case-based reasoning, nearest neighbor learning and linear discriminant analysis.

This article reports on the activities, papers, speakers, and workshops of the Seventh International Conference on Case-Based Reasoning, held 13-16 August in Belfast, Northern Ireland. CCBR 2007, the Seventh International Conference on Case-Based Reasoning, was held in the joyful city of Belfast, Northern Ireland, UK. Our host in Belfast was David Patterson from the University of Ulster. The 2007 program attempted to open the field's frontiers by inviting speakers from neighboring areas and insiders who could expand the vision of the attending case-based reasoning (CBR) researchers and practitioners. An introspective talk, given by David W. Aha (Naval Research Lab, USA) kicked off the event, making attendees question how case-based reasoning is perceived by the outside world and the balance between theoretical foundations and applied research. His talk, "Addressing Perceptions of Case-Based Reasoning," set the tone for discussions throughout the conference.

The newly emerging field of machine ethics (Anderson and Anderson 2006) is concerned with adding an ethical dimension to machines. Unlike computer ethics--which has traditionally focused on ethical issues surrounding humans' use of machines--machine ethics is concerned with ensuring that the behavior of machines toward human users, and perhaps other machines as well, is ethically acceptable. In this article we discuss the importance of machine ethics, the need for machines that represent ethical principles explicitly, and the challenges facing those working on machine ethics. We also give an example of current research in the field that shows that it is possible, at least in a limited domain, for a machine to abstract an ethical principle from examples of correct ethical judgments and use that principle to guide its own behavior. We need to make a distinction between what James Moor has called an "implicit ethical agent" and an "explicit ethical agent" (Moor 2006).

International Florida Artificial Intelligence Research Society Conference (FLAIRS-23) was held May 19-21, 2010, at the Shores Resort and Spa in Daytona Beach Shores, Florida, USA. The conference featured an exciting lineup of invited speakers, a general conference track on artificial intelligence research, and numerous special tracks. The conference chair was David Wilson from the University of North Carolina at Charlotte. The program cochairs were R. Charles Murray from Carnegie Learning and Hans W. Guesgen from Massey University in New Zealand. The special tracks coordinator was Philip McCarthy from the University of Memphis.

Its goal is to capture, in a general form, the internal structure of the objects, relations, strategies, and processes used to solve tasks drawn from a source domain, and exploit that knowledge to improve performance in a target domain. A Note from the AI Magazine Editor in Chief: Part Two of the Structured Knowledge Transfer special issue will be published in the summer 2011 issue (volume 32 number 2) of AI Magazine. Articles in this issue will include: "Knowledge Transfer between Automated Planners," by Susana Fernández, Ricardo Aler, and Daniel Borrajo "Transfer Learning by Reusing Structured Knowledge," by Qiang Yang, Vincent W. Zheng, Bin Li, and Hankz Hankui Zhuo "An Application of Transfer to American Football: From Observation of Raw Video to Control in a Simulated Environment," by David J. Stracuzzi, Alan Fern, Kamal Ali, Robin Hess, Jervis Pinto, Nan Li, Tolga Könik, and Dan Shapiro "Toward a Computational Model of Transfer," by Daniel Oblinger While the field of psychology has studied transfer learning in people for many years, AI has only recently taken up the challenge. The topic received initial attention with work on inductive transfer in the 1990s, while the number of workshops and conferences has noticeably increased in the last five years. This special issue represents the state of the art in the subarea of transfer learning that focuses on the acquisition and reuse of structured knowledge.

In contrast to an explicit definition of each individual item, configurable products such as computers, financial service portfolios, and cars are repre sented in the form of a configuration knowledge base that de - scribes the properties of allowed instances. Although the knowledge representation used is different compared to nonconfi gurable products, the decision support requirements remain the same: users have to be supported in finding a solution that fits their wishes and needs. In this article we show how recommendation technologies can be applied for supporting the configuration of products. In addition to existing approaches we discuss relevant issues for future research. Similar to knowledge-based recommendation (Burke 2000) configuration is a process where users specify (and often adapt) their requirements and the configuration system provides feedback. Requirements specifications range from feature value definitions to textual queries specified on an informal level. Feedback is provided, for example, in terms of further questions that need to be answered, solutions (configurations), explanations of solutions, and proposals for relaxations of the user requirements in situations where no solution can be found. A major difference between configuration systems and recommender systems in general is the way in which product knowledge is represented. Configuration systems are operating on a configuration knowledge base (Stumptner 1997), which describes the properties of all allowed instances. In contrast to configuration systems, recommender systems are operating on the basis of an assortment of explicitly defined solution alternatives. The reason for using a configuration knowledge base is the large number of solution alternatives (possible configurations), which make an explicit representation infeasible. Although the used knowledge representations are different, the decision support goal is quite the same for both types of systems: users have to be proactively supported in finding a solution that fits their wishes and needs. Configuration systems often achieve this goal only partially since the amount and complexity of options presented by the configurator outstrip the capability of a user to identify an appropriate solution (configuration). Users are unable to find the features they would like to specify, they are unsure about their preferences regarding complex technical product properties, and they do not know how best to adapt their requirements in the case of inconsistencies (if no solution can be identified).