Archive for the ‘Learning’ Category
8
Jul
Posted by cognitivecomputing in Education, Learning, Web / Web 2.0. Tagged: educational technology, problem-based learning. Leave a comment
Problem-based learning (PBL) is a constructivist pedagogy in which students learn in small groups by working on real-world problems. Despite its many benefits, however, this pedagogy is still not widely used in K-16 classrooms, especially with large numbers of students. Traditional human-facilitated PBL places intense demands on faculty to facilitate problem-solving sessions with small groups of students; on the other hand, most educational technologies do not provide PBL’s collaborative problem-solving experience.
We propose a cognitive model of the problem-based learning process. We present a software environment called CaseBook that allows instructors to author and share problems and provides students with a pedagogically-sound PBL experience based on the cognitive model. CaseBook has been used in high school and undergraduatefrom two studies in actual classrooms.
Read the paper:
A Cognitive Model of Problem-Based Learning and its Application to Educational Software Design
by Ashwin Ram, Preetha Ram, Jennifer Holzmann, Chris Sprague
International Conference on e-Learning (eLearn-07), Lisbon, Portugal, July 2007. Also presented at Eleventh International Conference on Human-Computer Interaction (INTERACT-07), Panel on Human-Centric e-Learning, Rio de Janeiro, Brazil, September 2007.
www.cc.gatech.edu/faculty/ashwin/papers/er-07-05.pdf
27
Jun
Posted by cognitivecomputing in Health & Wellness, Language, Learning, Web / Web 2.0. Tagged: healthcare, natural language, semantic memory. Leave a comment
NLM’s Unified Medical Language System (UMLS) is a very large ontology of biomedical and health data. In order to be used effectively for knowledge processing, it needs to be customized to a specific domain. In this paper, we present techniques to automatically discover domain-specific concepts, discover relationships between these concepts, build a context map from these relationships, link these domain concepts with the best-matching concept identifiers in UMLS using our context map and UMLS concept trees, and finally assign categories to the discovered relationships. This specific domain ontology of terms and relationships using evidential information can serve as a basis for applications in analysis, reasoning and discovery of new relationships. We have automatically built an ontology for the Nuclear Cardiology domain as a testbed for our techniques.
Read the paper:
Domain Ontology Construction from Biomedical Text
by Saurav Sahay, Baoli Li, Ernie Garcia, Eugene Agichtein, Ashwin Ram
International Conference on Artificial Intelligence (ICAI-07), Las Vegas, NV, June 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-10.pdf
8
May
Posted by cognitivecomputing in Agents, Game AI, Learning, Talks. Tagged: believable agents, case-based reasoning, games, interactive drama, rts games. 1 comment
Computer games are an increasingly popular application for Artificial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial games. Although games are typically associated with entertainment, there are many “serious” applications of gaming, including military, corporate, and advertising applications. There are also so-called “humane” gaming applications for medical training, educational games, and games that reflect social consciousness or advocate for a cause. Game AI is the effort of going beyond scripted interactions, however complex, into the arena of truly interactive systems that are responsive, adaptive, and intelligent. Such systems learn about the player(s) during game play, adapt their own behaviors beyond the pre-programmed set provided by the game author, and interactively develop and provide a richer experience to the player(s).
The long-term goal of our research is to develop artificial intelligence techniques that can have a significant impact in the game industry. We present a list of challenges and research opportunities in developing techniques that can be used by computer game developers. We discuss three Case Based Reasoning (CBR) approaches to achieve adaptability in games: automatic behavior adaptation for believable characters; drama management and user modeling for interactive stories; and strategic behavior planning for real-time strategy games.
Read the paper:
Artificial Intelligence for Adaptive Computer Games
by Ashwin Ram, Santi Ontañón, Manish Mehta
Invited talk at the 20th International FLAIRS Conference on Artificial Intelligence (FLAIRS-07), Special Track on Case-Based Reasoning, Key West, FL, May 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-04.pdf
8
May
Posted by cognitivecomputing in Health & Wellness, Language, Learning. Tagged: healthcare, natural language. Leave a comment
We propose a semi-supervised method to extract rule sentences from medical abstracts. Medical rules are sentences that give interesting and non-trivial relationship between medical entities. Mining such medical rules is important since the rules thus extracted can be used as inputs to an expert system or in many more other ways. The technique we suggest is based on paraphrasing a set of seed sentences and populating a pattern dictionary of paraphrases of rules. We match the patterns against the new abstract and rank the sentences.
Read the paper:
Detecting Medical Rule Sentences with Semi-Automatically Derived Patterns: A Pilot Study
by Shreekanth Karvaje, Bharat Ravisekar, Baoli Li, Ernie Garcia, Ashwin Ram
International Symposium on Bioinformatics Research and Applications ( ISBRA-07), Atlanta, GA, May 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-07.pdf
1
May
Posted by cognitivecomputing in Agents, Learning. Tagged: case-based reasoning, multistrategy learning. Leave a comment
We present an argumentation framework for learning agents (AMAL) designed for two purposes: (1) for joint deliberation, and (2) for learning from communication. The AMAL framework is completely based on learning from examples: the argument preference relation, the argument generation policy, and the counterargument generation policy are case-based techniques.
For joint deliberation, learning agents share their experience by forming a committee to decide upon some joint decision. We experimentally show that the argumentation among committees of agents improves both the individual and joint performance. For learning from communication, an agent engages into arguing with other agents in order to contrast its individual hypotheses and receive counterexamples; the argumentation process improves their learning
scope and individual performance.
Read the paper:
Learning and Joint Deliberation through Argumentation in Multi-Agent Systems
by Santi Ontañón and Enric Plaza
in Autonomous Agents and Multi-Agent Systems (AAMAS 2007), pp. 971-978
www.cc.gatech.edu/faculty/ashwin/papers/er-07-19.pdf
11
Jan
Posted by cognitivecomputing in Game AI, Learning. Tagged: case-based reasoning, games, planning, real-time cbr, rts games. 2 comments
The goal of transfer learning is to use the knowledge acquired in a set of source tasks to improve performance in a related but previously unseen target task. In this paper, we present a multi-layered architecture named CAse-Based Reinforcement Learner (CARL). It uses a novel combination of Case-Based Reasoning (CBR) and Reinforcement Learning (RL) to achieve transfer while playing against the Game AI across a variety of scenarios in MadRTS, a commercial Real-Time Strategy game. Our experiments demonstrate that CARL not only performs well on individual tasks but also exhibits significant performance gains when allowed to transfer knowledge from previous tasks.
Read the paper:
Transfer Learning in Real-Time Strategy Games Using Hybrid CBR/RL
by Manu Sharma, Michael Holmes, Juan Santamaria, Arya Irani, Charles Isbell, Ashwin Ram
International Joint Conference on Artificial Intelligence (IJCAI-07), Hyderabad, India, January 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-01.pdf
8
Jan
Posted by cognitivecomputing in Agents, Game AI, Learning. Tagged: believable agents, games, interactive drama, meta-reasoning, planning, virtual worlds. 1 comment
Typically, autonomous believable agents are implemented using static, hand-authored reactive behaviors or scripts. This hand-authoring allows designers to craft expressive behavior for characters, but can lead to excessive authorial burden, as well as result in characters that are brittle to changing world dynamics.
In this paper we present an approach for the runtime adaptation of reactive behaviors for autonomous believable characters. Extending transformational planning, our system allows autonomous characters to monitor and reason about their behavior execution, and to use this reasoning to dynamically rewrite their behaviors. In our evaluation, we transplant two characters in a sample tag game from the original world they were written for into a different one, resulting in behavior that violates the author intended personality. The reasoning layer successfully adapts the character’s behaviors so as to bring its long-term behavior back into agreement with its personality.
Towards Runtime Behavior Adaptation for Embodied Characters
by Peng Zang, Manish Mehta, Michael Mateas, Ashwin Ram
International Joint Conference on Artificial Intelligence (IJCAI-07), Hyderabad, India, January 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-02.pdf
1
Jan
Posted by cognitivecomputing in Agents, Learning. Tagged: case-based reasoning, multistrategy learning. Leave a comment
We present a proactive communication approach that allows CBR agents to gauge the strengths and weaknesses of other CBR agents. The communication protocol allows CBR agents to learn from communicating with other CBR agents in such a way that each agent is able to retain certain cases provided by other agents that are able to improve their individual performance (without need to disclose all the contents of each case base). The selection and retention of cases is modeled as a case bartering process, where each individual CBR agent autonomously decides which cases offers for bartering and which offered barters accepts. Experimental evaluations show that the sum of all these individual decisions result in a clear improvement in individual CBR agent performance with only a moderate increase of individual case bases.
Read the paper:
Case-Based Learning from Proactive Communication
by Santi Ontañón and Enric Plaza
International Joint Conference on Artificial Intelligence (IJCAI 2007), pp. 999-1004
www.cc.gatech.edu/faculty/ashwin/papers/er-07-18.pdf
27
Jul
Posted by cognitivecomputing in Learning. Tagged: semantic memory. Leave a comment
Traditional supervised learning techniques do not address online learning problems such as concept drift, due to the fact that learning is offine when using these methods. Associative neural networks using Hebbian learning rules show robust performance in classification tasks involving concept drift. Biologically plausible neural networks represent a set of computational models designed to be more strongly related to biological neuron models. In this paper, we apply a biologically inspired plasticity model of synapse dynamics to a concept drift classification problem. The motivation for this method is to provide more biologically plausible networks in cognitive tasks.
Read the paper:
A Synapse Plasticity Model for Conceptual Drift Problems
by Chip Mappus, Ashwin Ram
28th Annual Conference of the Cognitive Science Society (CogSci-06), Vancouver, BC, July 2006
www.cc.gatech.edu/faculty/ashwin/papers/er-06-01.pdf
15
May
Posted by cognitivecomputing in Education, Learning, Web / Web 2.0. Tagged: educational technology, problem-based learning. Leave a comment
Problem-based learning (PBL) is an educational approach that allows students to improve problem solving and critical thinking skills while learning science. However, PBL requires significant teacher time and expertise to develop problems and facilitate small-group problem-solving sessions. With advances in technology, PBL can be used in today’s classrooms in an effective and scalable manner.
CaseBook is an interactive computer system that allows for easy integration of PBL into the K-16 curriculum. Through a simple web-based interface, teachers enter and edit their case materials. As students work through cases, CaseBook guides them through a 3-stage process in which they analyze, learn and reflect. Students may work independently, or a small group of students may work together and share a Team Notebook, which is used to record facts, ideas, and issues about the case as they progress. Students assess their progress through self and group reflection and through teacher feedback.
We report on the use of CaseBook for a microbiology case in a high school classroom. The results suggest that CaseBook is effective for both advanced and remedial students. As the technological capacity of students and classrooms increase, it is only appropriate to use this technology to implement novel methods of teaching that will provide students the skills they need post- graduation.
Read the paper:
CaseBook: A Problem-Based Learning Online Environment For High School Microbiology
by JL Holzman, G Louizi, SC Fowler, E Lindsey, JJ Harrigan, P Ram, A Ram
12th American Society for Microbiology (ASM) Conference for Undergraduate Educators, Atlanta, GA, May 2006
www.cc.gatech.edu/faculty/ashwin/papers/er-05-06.doc.pdf
www.cc.gatech.edu/faculty/ashwin/papers/er-05-06.pdf
View my profile.svg){kind=logo}
Read my bio
Follow me
OpenStudy with me
Cognitive Computing 