Knowledge-intensive CBR assumes that cases are enriched with general domain knowledge. In CREEK, there is a very strong coupling between cases and general domain knowledge, in that cases are embedded within a general domain model. This increases the knowledge-intensiveness of the cases themselves. A knowledge-intensive CBR method calls for powerful knowledge acquisition and modeling techniques,...

the present study intended to look into and compare the possible effects of competitive team-based learning (ctbl) with learning together (lt) or cooperative group-based learning (cgbl) – the most popular method of cooperative learning (cl) -- on oral performance of iranian efl intermediate students. after administering the oral interview, this researcher selected a group of 40 almost homogeneo...

imagine you have traveled to an unfamiliar city. before you start your daily tour around the city, you need to know a good route. in network theory (nt), this is the traveling salesman problem (tsp). a dynamic programming algorithm is often used for solving this problem. however, when the road network of the city is very complicated and dense, which is usually the case, it will take too long fo...

Multiagent Inductive Learning is the problem that groups of agents face when they want to perform inductive learning, but the data of interest is distributed among them. This paper focuses on concept learning, and presents A-MAIL, a framework for multiagent induction integrating ideas from inductive learning, case-based reasoning and argumentation. Argumentation is used as a communication frame...

Video games are an interesting field of study for many artificial intelligence researchers, since many different AI methods can be studied and tested with them, and later those investigations can be applied to many other situations. In this paper we use case based reasoning and reinforcement learning principles to train bots to play the Ms. PacMan vs. Ghosts game. In particular, we use the well...

The aim of this work is to combine three successful AI techniques –Reinforcement Learning (RL), Heuristics Search and Case Based Reasoning (CBR)– creating a new algorithm that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms. This approach, called Case Based Heuristically Accelerated Reinforcement Learning (CB-HARL), builds upon an emerging tech...

54 AI MAGAZINE Observations of human reasoning motivate AI research on transfer learning (TL) and case-based reasoning (CBR). Our ability to transfer knowledge and expertise from understood domains to novel ones has been thoroughly documented in psychology and education (for example, Thorndike and Woodworth 1901; Perkins and Salomon 1994; Bransford, Brown, and Cocking 2000), among other discipl...

Case-based reasoning (CBR) systems have tight connections with machine learning and knowledge discovery and often incorporate diverse knowledge discovery functionalities and algorithms. This article presents themes identified in work presented at recent workshops on synergies between CBR and knowledge discovery. Among the main themes appear Big Data, with cases involving signals, images, texts,...

In case-based reasoning systems, several learning techniques may apply to a given situation. In a failuredriven learning environment, the problems of strategy selection are to choose the best set of learning algorithms or strategies that recover from a processing failure and to use the strategies to modify the system’s background knowledge so that the failure will not repeat in similar future s...