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Michael T. Cox

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8 papers

2 author rows

IJCAI Conference 2016 Conference Paper

Informed Expectations to Guide GDA Agents in Partially Observable Environments

Dustin Dannenhauer
Hector Munoz-Avila
Michael T. Cox

Goal Driven Autonomy (GDA) is an agent model for reasoning about goals while acting in a dynamic environment. Since anomalous events may cause an agent's current goal to become invalid, GDA agents monitor the environment for such anomalies. When domains are both partially observable and dynamic, agents must reason about sensing and planning actions. Previous GDA work evaluated agents in domains that were partially observable, but does not address sensing actions with associated costs. Furthermore, partial observability still enabled generation of a grounded plan to reach the goal. We study agents where observability is more limited: the agent cannot generate a grounded plan because it does not know which future actions will be available until it explores more of the environment. We present a formalism of the problem that includes sensing costs, a GDA algorithm using this formalism, an examination of four methods of expectations under this formalism, and an implementation of the algorithm and empirical study.

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KER Journal 2005 Journal Article

Case-based planning

Michael T. Cox
Héctor Muñoz-Avila
Ralph Bergmann

We briefly examine case-based planning starting with the seminal work of Hammond. Derivational analogy represents an important shift of technical emphasis that helped mature the techniques. The choice of abstraction level is equally important. We conclude by discussing theoretical underpinnings and by providing some pointers to current directions.

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KER Journal 2005 Journal Article

Case-based reasoning-inspired approaches to education

Janet L. Kolodner
Michael T. Cox
PEDRO A. GONZÁLEZ-CALERO

This commentary briefly reviews work on the application of case-based reasoning (CBR) to the design and construction of educational approaches and computer-based teaching systems. The CBR cognitive model is at the core of constructivist learning approaches such as Goal-based Scenarios and Learning by Design. Case libraries can play roles as intelligent resources while learning and frameworks for articulating one's understanding. More recently, CBR techniques have been applied to design and construction of simulation-based learning systems and serious games. The main ideas of CBR are explained and pointers to relevant references are provided, both for finished work and on-going research.

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AIJ Journal 2005 Journal Article

Metacognition in computation: A selected research review

Michael T. Cox

Various disciplines have examined the many phenomena of metacognition and have produced numerous results, both positive and negative. I discuss some of these aspects of cognition about cognition and the results concerning them from the point of view of the psychologist and the computer scientist, and I attempt to place them in the context of computational theories. I examine metacognition with respect to both problem solving (e. g. , planning) and to comprehension (e. g. , story understanding) processes of cognition.

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ICAPS Conference 2005 Conference Paper

Planning as Mixed-Initiative Goal Manipulation

Michael T. Cox
Chen Zhang

Mixed-initiative planning systems attempt to integrate human and AI planners so that the synthesis results in high quality plans. In the AI community, the dominant model of planning is search. In state-space planning, search consists of backward and forward chaining through the effects and preconditions of operator representations. Although search is an acceptable mechanism to use in performing automated planning, we present an alternative model to present to the user at the interface of a mixed-initiative planning system. That is we propose to model planning as a goal manipulation task. Here planning involves moving goals through a hyperspace in order to reach equilibrium between available resources and the constraints of a dynamic environment. The users can establish and "steer" goals through a visual representation of the planning domain. They can associate resources with particular goals and shift goals along various dimensions in response to changing conditions as well as change the structure of previous plans. Users need not know details of the underlying technology, even when search is used within. Here we empirically examine user performance under both alternatives and see that many users do better with the alternative model.

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AIJ Journal 1999 Journal Article

Introspective multistrategy learning: On the construction of learning strategies

Michael T. Cox
Ashwin Ram

A central problem in multistrategy learning systems is the selection and sequencing of machine learning algorithms for particular situations. This is typically done by the system designer who analyzes the learning task and implements the appropriate algorithm or sequence of algorithms for that task. We propose a solution to this problem which enables an AI system with a library of machine learning algorithms to select and sequence appropriate algorithms autonomously. Furthermore, instead of relying on the system designer or user to provide a learning goal or target concept to the learning system, our method enables the system to determine its learning goals based on analysis of its successes and failures at the performance task. The method involves three steps: Given a performance failure, the learner examines a trace of its reasoning prior to the failure to diagnose what went wrong (blame assignment); given the resultant explanation of the reasoning failure, the learner posts explicitly represented learning goals to change its background knowledge (deciding what to learn); and given a set of learning goals, the learner uses nonlinear planning techniques to assemble a sequence of machine learning algorithms, represented as planning operators, to achieve the learning goals (learning-strategy construction). In support of these operations, we define the types of reasoning failures, a taxonomy of failure causes, a second-order formalism to represent reasoning traces, a taxonomy of learning goals that specify desired change to the background knowledge of a system, and a declarative task-formalism representation of learning algorithms. We present the Meta-AQUA system, an implemented multistrategy learner that operates in the domain of story understanding. Extensive empirical evaluations of Meta-AQUA show that it performs significantly better in a deliberative, planful mode than in a reflexive mode in which learning goals are ablated and, furthermore, that the arbitrary ordering of learning algorithms can lead to worse performance than no learning at all. We conclude that explicit representation and sequencing of learning goals is necessary for avoiding negative interactions between learning algorithms that can lead to less effective learning.

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ICAPS Conference 1998 Conference Paper

Rationale-Based Monitoring for Planning in Dynamic Environments

Manuela Veloso
Martha E. Pollack
Michael T. Cox

Wedescribe a framework for planning in dynamic environments. A central question is how to focus the sensing performed by such a system, so that it responds appropriately to relevant changes, but does not attempt to monitor all the changesthat could possibly occur in the world. To achieve the required balance, we introduce rationale. based monitors, whichrepresent the features of the world state that are included in the plan rationale, i. e., the reasons for the plannln~ decisions so far made. Rationalebased monitors capture information both about the plan currently under developmentand the alternative choices that were found but not pursued. Wediscuss the plan transformations that mayresult from the firing of a rationale-based monitor, for examplewhenan alternative choice is detected. Wehave implemented the generation of and response to rationale-based monitoring within the Prodigy planner, and we describe experimentsthat showthe feasibility of our approach.

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AAAI Conference 1994 Short Paper

Case-Based Introspection

Michael T. Cox

To effectively reason about one’s own knowledge, goals, and reasoning requires an ability to explicitly introspect. A computational model of introspection is a second-order theory that contains a formal language for representing first-order processes and that processes instances of this representation. The reasoning algorithm used to perform such processing is similar to the algorithm used to reason about events and processes represented in the original domain: case-based reasoning.

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