There is a growing consensus among computer science faculty that it is quite difficult to teach the introductory course on Artificial Intelligence well [4, 6]. In part this is because AI lacks a unified methodology, overlaps with many other disciplines, and involves a wide range of skills from very applied to quite formal. In the funded project described here we have addressed these problems by " Offering a unifying theme that draws together the disparate topics of AI; " Focusing the course syllabus on the role AI plays in the core computer science curriculum; and " Motivating the students to learn by using concrete, hands-on laboratory exercises. Our approach is to conceive of topics in AI as robotics tasks. In the laboratory, students build their own robots and program them to accomplish the tasks. By constructing a physical entity in conjunction with the code to control it, students have a unique opportunity to directly tackle many central issues of computer science including the interaction between hardware and software, space complexity in terms of the memory limitations of the robot's controller, and time complexity in terms of the speed of the robot's action decisions. More importantly, the robot theme provides a strong incentive towards learning because students want to see their inventions succeed. This robot-centered approach is an extension of the agent-centered approach adopted by Russell and Norvig in their recent text book [11]. Taking the agent perspective, the problem of AI is seen as describing and building agents that receive perceptions as input and then output appropriate actions based on them. As a result the study of AI centers around how best to implement this mapping from perceptions to actions. The robot perspective takes this approach one step further; rather than studying software agents in a simulated environment, we embed physical agents in the real world. This adds a dimension of complexity as well as excitement to the AI course. The complexity has to do with additional demands of learning robot building techniques but can be overcome by the introduction of kits that are easy to assemble. Additionally, they are lightweight, inexpensive to maintain, programmable through the standard interfaces provided on most computers, and yet, offer sufficient extensibility to create and experiment with a wide range of agent behaviors. At the same time, using robots also leads the students to an important conclusion about scalability: the real world is very different from a simulated world, which has been a long standing criticism of many well-known AI techniques. We proposed a plan to develop identical robot building laboratories at both Bryn Mawr and Swarthmore Colleges that would allow us to integrate the construction of robots into our introductory AI courses. Furthermore, we hoped that these laboratories would encourage our undergraduate students to pursue honors theses and research projects dealing with the building of physical agents.

We have developed a CS1 curriculum that uses a robotics context to teach introductory programming [1]. Core to our approach is that each student has their own personal robot. Our robot and software have been specifically developed to support the needs of a CS1 curriculum. We frame traditional problems (robot control) in terms that are personal, relevant, and fun. Initial trial classes have shown that our approach is successful and adaptable.

Background: Curcumin and quercetin are the two important flavonoids that are being used as food ingredients across the globe. They also possess many pharmacological activities such as anti-inflammatory, anticancer and antidiabetic activity. The combination of both these phytoconstituents is available in the market. However, there is lack of any suitable analytical method reported for their simultaneous estimation in food as well as pharmaceutical products. Hence, there is a need to develop a simultaneous method for them. Objectives: The study aims to develop and validate a simple high-performance liquid chromatography (HPLC) method for the simultaneous determination of curcumin and quercetin. Method: Gradient elution was carried out in ACN and GAA (2% v/v) for 15 min wherein the ratio of ACN and GAA was varied from 40-60 v/v ACN: 60-40 GAA v/v from 0-5 min followed by 60-80 v/v ACN: 40-20 v/v GAA between 7-10 min followed by 80 % ACN: 20 % GAA between 10-15 min. The flow rate was kept 1 mL/min and detection wavelength at 395 nm. The method has been validated according to International Conference of Harmonization (ICH) Q2 (R1) guidelines with respect to specificity, system suitability, accuracy, precision, and robustness. The developed method was further used for the determination of curcumin and quercetin in different herbal extracts, marketed formulations containing these drugs and developed self-nano emulsifying drug delivery system (SNEDDS). Results: The method was linear in the concentration range of 50-250 ng mL1 for curcumin and 800-1600 ng mL À1 for quercetin, respectively. The limit of detection (LOD) and limit of quantification (LOQ) were 11.76 and 35.6 ng mL À1 for curcumin, respectively, while LOD and LOQ for quercetin were 32.94 and 99.76 ng/mL À1 , respectively. The percentage recovery that was found to be in the range of 95-105% with relative standard deviation less than 2% indicating the accuracy and precision of method for both the drugs. Further, the validated method was found specific to detect presence of both the drugs in extracts, marketed formulations and developed SNEDDS. Conclusions: The developed method showed excellent specificity, linearity, accuracy and precision. Thus, it can be further explored to detect curcumin and quercetin in biological samples as well as other marketed formulations.

Bentonite seams of varying thicknesses, from a few millimetres to about 30 cm, are found at depths of 200-300 m within the Belle Fourche Formation and Second White Specks unit in the Cretaceous Colorado Group in the Cold Lake area of Alberta, which is one of four exploration sites for extraction of heavy oil in Alberta. Thermal heavy oil recovery processes in oil sand reservoirs, such as cyclic steam stimulation and steam-assisted gravity drainage, cause casing impairment and failure in the overlying Colorado Group shales. Based on the results from triaxial compression and direct shear box tests on bentonite seam samples, the material displays not only low stiffness and friction angle, but also pronounced creep. Results from numerical analyses of case studies illustrate that the shear slip mechanism along these bentonite seams is admissible in the field under steam stimulation processes. The slip mechanism is mainly attributed to the huge contrast in deformation moduli and creep between the soft bentonite seam and the stiff Colorado Group shales, rather than due to the low frictional resistance derived from the bentonite seam.

SUMMARYProjects in the Artificial Intelligence course have evolved over the years. Along the way, they have taken several forms, including small-scale LISP/Prolog projects, larger-scale object-oriented projects in CLOS/C++, projects organized around games, and more recently, projects organized around the concept of agents. All along, educators have attempted to make the projects more appealing and instructive at the same time.In this panel, we will examine three disparate approaches for making AI projects more instructive and engaging: •The first approach organizes all the projects around a central theme, in this case, machine learning; •The second approach uses inexpensive robots as the platform for traditional projects; •The third approach moves to a software platform that enables working with advanced or simulated robots as well. All three approaches have been evaluated, and make supplementary materials available for use by interested faculty. MACHINE LEARNING -INGRID RUSSELLWe will present a suite of adaptable hands-on projects that can be closely integrated into a one term AI course. Our work unifies the Artificial Intelligence (AI) course around the theme of machine learning and creates an adaptable framework for presenting core AI concepts around that theme. Machine learning is inherently connected with the AI core topics and provides methodology and technology to enhance real-world applications within many of these topics. Machine learning is now considered as a technology for both software development (especially suitable for difficult-toprogram applications or for customizing software) and building intelligent software (i.e., a tool for AI programming). Our projects emphasize the relationship between AI and computer science in general, and software development in particular and highlight the bridge that machine learning provides between AI technology and modern software engineering. Each project involves the design and development of a learning system which will enhance a particular commonly-deployed application. In an introductory course one wishes to impart a wide variety of topics efficiently, indexing the major areas of the field. A machine learning application can be rapidly prototyped, allowing learning to be grounded in engaging experience without limiting the important breadth of an introductory course. The projects span several applications including Web user profiling, character recognition, the N-Puzzle problem, the jeopardy dice game Pig, Web document classification, and the popular board game Clue.We will present our projects as well as our experiences using them. Evaluation results indicate that the projects enhanced the student learning experience in the introductory AI course and that students demonstrated a better understanding of fundamental AI concepts such as Knowledge Representation and Search. Students were better motivated to learn the fundamental concepts both of AI and machine learning. The projects also stimulate students' interest in additional AI and machine learning related ...