13 Aug

Multi-Plan Retrieval and Adaptation in an Experience-Based Agent

Posted by cognitivecomputing in Agents, Learning. Tagged: , , , , .

The real world has many properties that present challenges for the design of intelligent agents: it is dynamic, unpredictable, and independent, poses poorly structured problems, and places bounds on the resources available to agents. Agents that opearate in real worlds need a wide range of capabilities to deal with them: memory, situation analysis, situativity, resource-bounded cognition, and opportunism.

We propose a theory of experience-based agency which specifies how an agent with the ability to richly represent and store its experiences could remember those experiences with a context-sensitive, asynchronous memory, incorporate those experiences into its reasoning on demand with integration mechanisms, and usefully direct memory and reasoning through the use of a utility-based metacontroller. We have implemented this theory in an architecture called NICOLE and have used it to address the problem of merging multiple plans during the course of case-based adaptation in least-committment planning.

Read the paper:

Multi-Plan Retrieval and Adaptation in an Experience-Based Agent

by Ashwin Ram, Anthony Francis

In Case-Based Reasoning: Experiences, Lessons, and Future Directions, D.B. Leake, editor, AAAI Press, 1996
www.cc.gatech.edu/faculty/ashwin/papers/er-96-06.pdf

24 Jul

The Role of Student Tasks in Accessing Cognitive Media Types

Posted by cognitivecomputing in Education. Tagged: , , .

We believe that identifying media by their cognitive roles (e.g., definition, explanation, pseudo-code, visualization) can improve comprehension and usability in hypermedia systems designed for learning. We refer to media links organized around their cognitive role as cognitive media types (Recker, Ram, Shikano, Li, & Stasko, 1995). Our hypothesis is that the goals that students bring to the learning task will affect how they will use the hypermedia support system (Ram & Leake, 1995).

We explored student use of a hypermedia system based on cognitive media types where students performed different orienting tasks: undirected, browsing in order to answer specific questions, problem-solving, and problem-solving with prompted self-explanations. We found significant differences in use behavior between problem-solving and browsing students, though no learning differences.

Read the paper:

The Role of Student Tasks in Accessing Cognitive Media Types

by Mike Byrne, Mark Guzdial, Preetha Ram, Rich Catrambone, Ashwin Ram, John Stasko, Gordon Shippey, Florian Albrecht

Second International Conference on the Learning Sciences (ICLS-96), Evanson, IL, July 1996
www.cc.gatech.edu/faculty/ashwin/papers/er-96-03.pdf

24 Jul

Exploring Interface Options in Multimedia Educational Environments

Posted by cognitivecomputing in Education. Tagged: , .

Multimedia technology presents several options to the developers of computer-based learning environments. For instance, it is common to organize information by its physical characteristics. However, organizize information based on how users understand the material might improve comprehension. This theory of cognitive media – media organized by cognitive characteristics – was examined in studies using the AlgoNet system, a multimedia learning environment (Recker, Ram, Shikano, Li, & Stasko, 1995). To explore several interface options, AlgoNet2, a second version of AlgoNet, was created with the same domain information, but several new interface concepts. Students in an introductory programming class used AlgoNet2 to solve a problem involving graph theory. Students’ performance and comments suggest that many students lack effective learning strategies and those that do employ effective learning strategies are unaware of them.

Read the paper:

Exploring Interface Options in Multimedia Educational Environments

by Gordon Shippey, Ashwin Ram, Florian Albrecht, Janis Roberts, Mark Guzdial, Rich Catrambone, Mike Byrne, John Stasko

Second International Conference on the Learning Sciences (ICLS-96), Evanson, IL, July 1996
www.cc.gatech.edu/faculty/ashwin/papers/er-96-02.pdf

13 Jul

The Role of Ontology in Creative Understanding

Posted by cognitivecomputing in Language. Tagged: , , , .

Successful creative understanding requires that a reasoner be able to manipulate known concepts in order to understand novel ones. A major problem arises, however, when one considers exactly how these manipulations are to be bounded. If a bound is imposed which is too loose, the reasoner is likely to create bizarre understandings rather than useful creative ones. On the other hand, if the bound is too tight, the reasoner will not have the flexibility needed to deal with a wide range of creative understanding experiences. Our approach is to make use of a principled ontology as one source of reasonable bounding. This allows our creative understanding theory to have good explanatory power about the process while allowing the computer implementation of the theory (the ISAAC system) to be flexible without being bizarre in the task domain of reading science fiction short stories.

Read the paper:

The Role of Ontology in Creative Understanding

by Kenneth Moorman, Ashwin Ram

18th Annual Conference of the Cognitive Science Society (CogSci-96), San Diego, CA, July 1996
www.cc.gatech.edu/faculty/ashwin/papers/er-96-01.pdf

3 Jul

Dynamically Adjusting Concepts to Accommodate Changing Contexts

Posted by cognitivecomputing in Learning.

In concept learning, objects in a domain are grouped together based on similarity as determined by the attributes used to describe them. Existing concept learners require that this set of attributes be known in advance and presented in entirety before learning begins. Additionally, most systems do not possess mechanisms for altering the attribute set after concepts have been learned. Consequently, a veridical attribute set relevant to the task for which the concepts are to be used must be supplied at the onset of learning, and in turn, the usefulness of the concepts is limited to the task for which the attributes were originally selected.

In order to efficiently accommodate changing contexts, a concept learner must be able to alter the set of descriptors without discarding its prior knowledge of the domain. We introduce the notion of attribute-incrementation, the dynamic modification of the attribute set used to describe instances in a problem domain. We have implemented the capability in a concept learning system that has been evaluated along several dimensions using an existing concept formation system for comparison.

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Dynamically Adjusting Concepts to Accommodate Changing Contexts

by Mark Devaney, Ashwin Ram

ICML-96 Workshop on Learning in Context Sensitive Domains, Bari, Italy, July 1996
www.cc.gatech.edu/faculty/ashwin/papers/er-96-07.pdf

1 Jun

Evaluating the Structural Organization of a Hypermedia Learning Environment using GOMS Model Analysis

Posted by cognitivecomputing in Education. Tagged: , .

Network-accessible hypermedia environments offer the potential for radically changing the nature of education by providing students with self-paced access to digital repositories of course information. However, much research is still required to identify ways to best organize, present, and index multimedia information to facilitate use and learning by students. We have been developing a theory of design for educational multimedia, which is based on cognitive aspects of the users of that information. Design based on “cognitive media types” appeals to the particular cognitive aspects of learners. In contrast, design based on physical media types appeals to particular symbol systems or sensory modalities.

To evaluate our theory of cognitive media types, we have taken a 3-pronged approach: design, empirical evaluation, and analysis of student models. In this paper, we focus on the third component of our approach: a model of student usage and learning with cognitive media. This model, based on the GOMS methodology, helps us better understand the usability of our system, and how it may support and hinder student learning. Furthermore, our user model provides feedback on our theory of cognitive media, and offers suggestions for the design of effective hypermedia learning environments.

Evaluating the Structural Organization of a Hypermedia Learning Environment using GOMS Model Analysis

by Terry Shikano, Mimi Recker, Ashwin Ram

World Conference on Educational Multimedia and Hypermedia, Boston, MA, June 1996
www.cc.gatech.edu/faculty/ashwin/papers/er-96-04.rtf

15 May

Introspective Multistrategy Learning: Constructing a Learning Strategy under Reasoning Failure

Posted by cognitivecomputing in Language, Learning. Tagged: , , , , , .

The thesis put forth by this dissertation is that introspective analyses facilitate the construction of learning strategies. Furthermore, learning is much like nonlinear planning and problem solving. Like problem solving, it can be specified by a set of explicit learning goals (i.e., desired changes to the reasoner’s knowledge); these goals can be achieved by constructing a plan from a set of operators (the learning algorithms) that execute in a knowledge space. However, in order to specify learning goals and to avoid negative interactions between operators, a reasoner requires a model of its reasoning processes and knowledge.

With such a model, the reasoner can declaratively represent the events and causal relations of its mental world in the same manner that it represents events and relations in the physical world. This representation enables introspective self-examination, which contributes to learning by providing a basis for identifying what needs to be learned when reasoning fails. A multistrategy system possessing several learning algorithms can decide what to learn, and which algorithm(s) to apply, by analyzing the model of its reasoning. This introspective analysis therefore allows the learner to understand its reasoning failures, to determine the causes of the failures, to identify needed knowledge repairs to avoid such failures in the future, and to build a learning strategy (plan).

Thus, the research goal is to develop both a content theory and a process theory of introspective multistrategy learning and to establish the conditions under which such an approach is fruitful. Empirical experiments provide results that support the claims herein. The theory was implemented in a computational model called Meta-AQUA that attempts to understand simple stories. The system uses case-based reasoning to explain reasoning failures and to generate sets of learning goals, and it uses a standard non-linear planner to achieve these goals.

Evaluating Meta-AQUA with and without learning goals generated results indicating that computational introspection facilitates the learning process. In particular, the results lead to the conclusion that the stage that posts learning goals is a necessary stage if negative interactions between learning methods are to be avoided and if learning is to remain effective.

Read the thesis:

Introspective multistrategy learning: Constructing a learning strategy under reasoning failure

by Michael T. Cox

PhD Thesis, Technical Report GIT-CC-96/06, College of Computing, Georgia Institute of Technology, Atlanta, GA, 1996
www.cc.gatech.edu/faculty/ashwin/papers/git-cc-96-06.pdf

15 May

Learning Adaptive Reactive Agents

Posted by cognitivecomputing in Agents, Learning, Robotics. Tagged: , , .

An autonomous agent is an intelligent system that has an ongoing interaction with a dynamic external world. It can perceive and act on the world through a set of limited sensors and effectors. Its most important characteristic is that it is forced to make decisions sequentially, one after another, during its entire “life”. The main objective of this dissertation is to study algorithms by which an autonomous agents can learn, using their own experience, to perform sequential decision-making efficiently and autonomously. The dissertation describes a framework for studying autonomous sequential decision-making consisting of three main elements: the agent, the environment, and the task. The agent attempts to control the environment by perceiving the environment and choosing actions in a sequential fashion. The environment is a dynamic system characterized by a state and its dynamics, a function that describes the evolution of the state given the agent’s actions. A task is a declarative description of the desired behavior the agent should exhibit as it interacts with the environment. The ultimate goal of the agent is to learn a policy or strategy for selecting actions that maximizes its expected benefit as defined by the task.

The dissertation focuses on sequential decision-making when the environment is characterized by continuous states and actions, and the agent has imperfect perception, incomplete knowledge, and limited computational resources. The main characteristic of the approach proposed in this dissertation is that the agent uses its previous experiences to improve estimates of the long-term benefit associated with the execution of specific actions. The agent uses these estimates to evaluate how desirable is to execute alternative actions and select the one that best balances the short- and long-term consequences, taking special consideration of the expected benefit associated with actions that accomplish new learning while making progress on the task.

The approach is based on novel methods that are specifically designed to address the problems associated with continuous domains, imperfect perception, incomplete knowledge, and limited computational resources. The approach is implemented using case-based techniques and extensively evaluated in simulated and real systems including autonomous mobile robots, pendulum swinging and balancing controllers, and other non-linear dynamic system controllers.

Read the thesis:

Learning Adaptive Reactive Agents

by Juan Carlos Santamaria

PhD Thesis, College of Computing, Georgia Institute of Technology, Atlanta, GA, 1996
www.cc.gatech.edu/faculty/ashwin/papers/git-cc-97-08.ps.Z

15 Oct

Structuring On-The-Job Troubleshooting Performance to Aid Learning

Posted by cognitivecomputing in Education. Tagged: , , , , , .

This paper describes a methodology for aiding the learning of troubleshooting tasks in the course of an engineer’s work. The approach supports learning in the context of actual, on-the-job troubleshooting and, in addition, supports performance of the troubleshooting task in tandem. This approach has been implemented in a computer tool called WALTS (Workspace for Aiding and Learning Troubleshooting).

This method aids learning by helping the learner structure his or her task into the conceptual components necessary for troubleshooting, giving advice about how to proceed, suggesting candidate hypotheses and solutions, and automatically retrieving cognitively relevant media. WALTS includes three major components: a structured dynamic workspace for representing knowledge about the troubleshooting process and the device being diagnosed; an intelligent agent that facilitates the troubleshooting process by offering advice; and an intelligent media retrieval tool that automatically presents candidate hypotheses and solutions, relevant cases, and various other media. WALTS creates resources for future learning and aiding of troubleshooting by storing completed troubleshooting instances in a self-populating database of troubleshooting cases.

The methodology described in this paper is partly based on research in problem-based learning, learning by doing, case-based reasoning, intelligent tutoring systems, and the transition from novice to expert. The tool is currently implemented in the domain of remote computer troubleshooting.

Read the paper:

Structuring On-The-Job Troubleshooting Performance to Aid Learning

by Brian Minsk, Hari Balakrishnan, Ashwin Ram

World Conference on Engineering Education, Minneapolis, MN, October 1995
www.cc.gatech.edu/faculty/ashwin/papers/er-95-06.pdf

1 Aug

Learning as Goal-Driven Inference

Posted by cognitivecomputing in Learning. Tagged: , .

Developing an adequate and general computational model of adaptive, multistrategy, and goal-oriented learning is a fundamental long-term objective for machine learning research for both theoretical and pragmatic reasons. We outline a proposal for developing such a model based on two key ideas. First, we view learning as an active process involving the formulation of learning goals during the performance of a reasoning task, the prioritization of learning goals, and the pursuit of learning goals using multiple learning strategies. The second key idea is to model learning as a kind of inference in which the system augments and reformulates its knowledge using various types of primitive inferential actions, known as knowledge transmutations.

Read the paper:

Learning as Goal-Driven Inference

by Ryszard Michalski, Ashwin Ram

In A. Ram & D. Leake (eds.), Goal-Driven Learning, chapter 21, MIT Press/Bradford Books, 1995
www.cc.gatech.edu/faculty/ashwin/papers/er-95-05.pdf
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