Memory-Based Learning


The 1996 Simon Newcomb Award

AI Magazine

His proofs are ingenious, cleverly argued, quite convincing to many of his contemporaries, and utterly wrong. The Simon Newcomb Award is given annually for the silliest published argument attacking AI. Our subject may be unique in the virulence and frequency with which it is attacked, both in the popular media and among the cultured intelligentsia. Recent articles have argued that the very idea of AI reflects a cancer in the heart of our culture and have proven (yet again) that it is impossible. While many of these attacks are cited widely, most of them are ridiculous to anyone with an appropriate technical education.


1058

AI Magazine

Case-based reasoning (CBR) is becoming a viable real-world technology. First, it fragments each CBR system across many chapters, making it difficult to get the big picture of how the system works and obscuring the interrelatedness of the system's parts. In addition, having each chapter draw its examples from multiple systems adds a certain context-switching overhead: Each time a system is introduced (or reintroduced), the book must set the context anew, and the reader must recall the details of the system. A second drawback to the unified framework is that although it has fairly broad coverage, it is still biased toward those systems that fit it best. As a result, important work sometimes gets only a cursory mention in the book.


Letters

AI Magazine

However, I believe that the distinction of "neats" and "scruffies" raised at Cog Sci in '81 didn't define scruffies as people who built expert systems [they didn't really exist as a "real" part of MAD. Instead, I believe AI These are the researchers who read Hawkings and say "gee, if his model of the lo-23 second big bang is right, then the distribution of intergalactic gases should be relatively even. Let's go see if that's true. However, to run our experiments we'll need a more sensitive space-based sensing device, so let's work with the engineers to design one." I think one could make the case (although not from the data collected in Cohen's survey) that the two methodologies are not informed and influenced by each other to the extent they should or could be.


Last-Minute Travel Application

AI Magazine

It is impossible for a travel agent to keep track of all the offered tour packages. Traditional database-driven applications, as used by most of the tour operators, are not sufficient enough to implement a sales process with consultation on the World Wide Web. The last-minute travel application presented here uses case-based reasoning to bridge this gap and simulate the sales assistance of a human travel agent. A case retrieval net, as an internal data structure, proved to be efficient in handling the large amount of data. A usual tour package contains the flight to the destination and back, transfers from the airport to the hotel and back, board, and lodging.


The 1998 Simon Newcomb Award

AI Magazine

His proofs are ingenious, cleverly argued, quite convincing to many of his contemporaries, and utterly wrong. The Simon Newcomb Award is given annually for the silliest published argument attacking AI. Our subject may be unique in the virulence and frequency with which it is attacked, both in the popular media and among the cultured intelligentsia. Recent articles have argued that the very idea of AI reflects a cancer in the heart of our culture and have proven (yet again) that it is impossible. While many of these attacks are cited widely, most of them are ridiculous to anyone with an appropriate technical education.


The Third International Conference on Case-Based Reasoning (ICCBR'99)

AI Magazine

Case-based reasoning (CBR) is a problem-solving paradigm that uses exemplars or previous solutions to solve new problems (Aamodt and Plaza 1994; Kolodner 1993). First, CBR can reduce search. Solution reuse is compatible with a wide range of problem-solving methods, so a CBR component can be used in many types of problem-solving system. When similar problems recur, CBR can significantly improve performance. This performance improvement can be particularly significant for problems with large search spaces, such as planning and design (Bergmann and Wilke 1996; Branting and Aha 1995; Veloso 1994; Koton 1988).


The SHOP

AI Magazine

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).


1767

AI Magazine

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.


The Invited Speakers

AI Magazine

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.


Special Issue on Structured Knowledge Transfer

AI Magazine

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.