Fault Recognition in the Presence of Error With NURBS-Based Metamodels

2009 ◽  
Author(s):  
Cameron J. Turner
Keyword(s):  
Theoretical Foundation ◽  
Condition Based Maintenance ◽  
On Demand ◽  
Model Based ◽  
Sources Of Error ◽  
Model Free ◽  
Fault Recognition ◽  
Simulated System ◽  
Free Model ◽  
The Impact

Condition-based maintenance (CBM) offers the possibility of replacing the predominant maintenance-as-scheduled paradigm with a maintenance-on-demand paradigm. In all CBM algorithms, faults must first be recognized, then characterized and finally reconciled. Multiple CBM methods have been proposed, including model-free, model-based and metamodel-based methods. However, the signals from real systems are obscured by sources of error. This research examines the impact of error upon a metamodel-based CBM approach using a simulated system to reveal the significance of error in the all-important step of fault recognition. The use of a simulated system allows control of the type and magnitude of both the error and of the fault signals allowing their significance to be evaluated. As a result of this research, a stronger theoretical foundation metamodel-based CBM techniques is established and several promising behaviors are identified.

scholarly journals Model-free, Model-based, and General Intelligence

2018 ◽  
Author(s):  
Hector Geffner
Keyword(s):  
Intelligent Systems ◽  
Human Mind ◽  
Writing Programs ◽  
Scalable Algorithms ◽  
Model Based ◽  
Model Free ◽  
Considerable Success ◽  
Black Boxes ◽  
Free Model ◽  
Intelligent Behavior

During the 60s and 70s, AI researchers explored intuitions about intelligence by writing programs that displayed intelligent behavior. Many good ideas came out from this work but programs written by hand were not robust or general. After the 80s, research increasingly shifted to the development of learners capable of inferring behavior and functions from experience and data, and solvers capable of tackling well-defined but intractable models like SAT, classical planning, Bayesian networks, and POMDPs. The learning approach has achieved considerable success but results in black boxes that do not have the flexibility, transparency, and generality of their model-based counterparts. Model-based approaches, on the other hand, require models and scalable algorithms. Model-free learners and model-based solvers have indeed close parallels with Systems 1 and 2 in current theories of the human mind: the first, a fast, opaque, and inflexible intuitive mind; the second, a slow, transparent, and flexible analytical mind. In this paper, I review developments in AI and draw on these theories to discuss the gap between model-free learners and model-based solvers, a gap that needs to be bridged in order to have intelligent systems that are robust and general.

scholarly journals Credit assignment to state-independent task representations and its relationship with model-based decision making

2019 ◽  
Vol 116 (32) ◽  
pp. 15871-15876 ◽  
Author(s):  
Nitzan Shahar ◽  
Rani Moran ◽  
Tobias U. Hauser ◽  
Rogier A. Kievit ◽  
Daniel McNamee ◽  
...  
Keyword(s):  
Decision Task ◽  
Credit Assignment ◽  
Free System ◽  
Assigned Value ◽  
Model Based ◽  
Independent Task ◽  
Model Free ◽  
Motor Representations ◽  
The Impact ◽  
Task Representations

Model-free learning enables an agent to make better decisions based on prior experience while representing only minimal knowledge about an environment’s structure. It is generally assumed that model-free state representations are based on outcome-relevant features of the environment. Here, we challenge this assumption by providing evidence that a putative model-free system assigns credit to task representations that are irrelevant to an outcome. We examined data from 769 individuals performing a well-described 2-step reward decision task where stimulus identity but not spatial-motor aspects of the task predicted reward. We show that participants assigned value to spatial-motor representations despite it being outcome irrelevant. Strikingly, spatial-motor value associations affected behavior across all outcome-relevant features and stages of the task, consistent with credit assignment to low-level state-independent task representations. Individual difference analyses suggested that the impact of spatial-motor value formation was attenuated for individuals who showed greater deployment of goal-directed (model-based) strategies. Our findings highlight a need for a reconsideration of how model-free representations are formed and regulated according to the structure of the environment.

scholarly journals A note on the analysis of two-stage task results: how changes in task structure affect what model-free and model-based strategies predict about the effects of reward and transition on the stay probability

2017 ◽  
Author(s):  
Carolina Feher da Silva ◽  
Todd A. Hare
Keyword(s):  
Scientific Progress ◽  
Task Structure ◽  
Decision Task ◽  
Two Stage ◽  
Model Based ◽  
Model Free ◽  
Task Dependence ◽  
Design Choice ◽  
Important Means ◽  
The Impact

AbstractMany studies that aim to detect model-free and model-based influences on behavior employ two-stage behavioral tasks of the type pioneered by Daw and colleagues in 2011. Such studies commonly modify existing two-stage decision paradigms in order to better address a given hypothesis, which is an important means of scientific progress. It is, however, critical to fully appreciate the impact of any modified or novel experimental design features on the expected results. Here, we use two concrete examples to demonstrate that relatively small changes in the two-stage task design can substantially change the pattern of actions taken by model-free and model-based agents as a function of the reward outcomes and transitions on previous trials. In the first, we show that, under specific conditions, purely model-free agents will produce the reward by transition interactions typically thought to characterize model-based behavior on a two-stage task. The second example shows that model-based agents’ behavior is driven by a main effect of transition-type in addition to the canonical reward by transition interaction whenever the reward probabilities of the final states do not sum to one. Together, these examples emphasize the task-dependence of model-free and model-based behavior and highlight the benefits of using computer simulations to determine what pattern of results to expect from both model-free and model-based agents performing a given two-stage decision task in order to design choice paradigms and analysis strategies best suited to the current question.

scholarly journals Modeling and prediction of pressure injury in hospitalized patients using artificial intelligence

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Christine Anderson ◽  
Zerihun Bekele ◽  
Yongkai Qiu ◽  
Dana Tschannen ◽  
Ivo D. Dinov
Keyword(s):  
Artificial Intelligence ◽  
At Risk ◽  
Hospitalized Patients ◽  
Surgical Services ◽  
Model Based ◽  
Model Free ◽  
Healthcare Needs ◽  
Patients At Risk ◽  
Hospital Acquired ◽  
The Impact

Abstract Background Hospital-acquired pressure injuries (PIs) induce significant patient suffering, inflate healthcare costs, and increase clinical co-morbidities. PIs are mostly due to bed-immobility, sensory impairment, bed positioning, and length of hospital stay. In this study, we use electronic health records and administrative data to examine the contributing factors to PI development using artificial intelligence (AI). Methods We used advanced data science techniques to first preprocess the data and then train machine learning classifiers to predict the probability of developing PIs. The AI training was based on large, incongruent, incomplete, heterogeneous, and time-varying data of hospitalized patients. Both model-based statistical methods and model-free AI strategies were used to forecast PI outcomes and determine the salient features that are highly predictive of the outcomes. Results Our findings reveal that PI prediction by model-free techniques outperform model-based forecasts. The performance of all AI methods is improved by rebalancing the training data and by including the Braden in the model learning phase. Compared to neural networks and linear modeling, with and without rebalancing or using Braden scores, Random forest consistently generated the optimal PI forecasts. Conclusions AI techniques show promise to automatically identify patients at risk for hospital acquired PIs in different surgical services. Our PI prediction model provide a first generation of AI guidance to prescreen patients at risk for developing PIs. Clinical impact This study provides a foundation for designing, implementing, and assessing novel interventions addressing specific healthcare needs. Specifically, this approach allows examining the impact of various dynamic, personalized, and clinical-environment effects on PI prevention for hospital patients receiving care from various surgical services.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Model-Based and Model-Free Social Cognition

2019 ◽  
Author(s):  
Leor M Hackel ◽  
Jeffrey Jordan Berg ◽  
Björn Lindström ◽  
David Amodio
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Learning Strategies ◽  
Memory Systems ◽  
Learning Task ◽  
Financial Advisors ◽  
Model Based ◽  
Model Free ◽  
Systems Model ◽  
Task Assessment

Do habits play a role in our social impressions? To investigate the contribution of habits to the formation of social attitudes, we examined the roles of model-free and model-based reinforcement learning in social interactions—computations linked in past work to habit and planning, respectively. Participants in this study learned about novel individuals in a sequential reinforcement learning paradigm, choosing financial advisors who led them to high- or low-paying stocks. Results indicated that participants relied on both model-based and model-free learning, such that each independently predicted choice during the learning task and self-reported liking in a post-task assessment. Specifically, participants liked advisors who could provide large future rewards as well as advisors who had provided them with large rewards in the past. Moreover, participants varied in their use of model-based and model-free learning strategies, and this individual difference influenced the way in which learning related to self-reported attitudes: among participants who relied more on model-free learning, model-free social learning related more to post-task attitudes. We discuss implications for attitudes, trait impressions, and social behavior, as well as the role of habits in a memory systems model of social cognition.

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