The Experts below are selected from a list of 2622 Experts worldwide ranked by ideXlab platform
Peter Pirolli - One of the best experts on this subject based on the ideXlab platform.
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Counterfactual Reasoning as a key for explaining adaptive behavior in a changing environment
Biologically Inspired Cognitive Architectures, 2014Co-Authors: Jaehyon Paik, Yunfeng Zhang, Peter PirolliAbstract:Abstract It is crucial for animals to detect changes in their surrounding environment, and reinforcement learning is one of the well-known processes to explain the change detection behavior. However, reinforcement learning itself cannot fully explain rapid, relatively immediate changes in strategy in response to abrupt environment changes. A previous model employed reinforcement learning and Counterfactual Reasoning to explain adaptive behavior observed in a changing market simulation environment. In this paper, we used the same model mechanisms to simulate data from two additional tasks that require participants, who played the role of intelligence analysts, to detect the changes of a computer-controlled adversary’s tactics based on intelligence evidence and feedback. The results show that our model captures participants’ adaptive behavior accurately, which further supports our previous conclusion that Counterfactual Reasoning is a missing piece for explaining adaptive behavior in a changing environment.
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reinforcement learning and Counterfactual Reasoning explain adaptive behavior in a changing environment
Cognitive Science, 2014Co-Authors: Yunfeng Zhang, Jaehyon Paik, Peter PirolliAbstract:Animals routinely adapt to changes in the environment in order to survive. Though reinforcement learning may play a role in such adaptation, it is not clear that it is the only mechanism involved, as it is not well suited to producing rapid, relatively immediate changes in strategies in response to environmental changes. This research proposes that Counterfactual Reasoning might be an additional mechanism that facilitates change detection. An experiment is conducted in which a task state changes over time and the participants had to detect the changes in order to perform well and gain monetary rewards. A cognitive model is constructed that incorporates reinforcement learning with Counterfactual Reasoning to help quickly adjust the utility of task strategies in response to changes. The results show that the model can accurately explain human data and that Counterfactual Reasoning is key to reproducing the various effects observed in this change detection paradigm.
William Yang Wang - One of the best experts on this subject based on the ideXlab platform.
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sscr iterative language based image editing via self supervised Counterfactual Reasoning
Empirical Methods in Natural Language Processing, 2020Co-Authors: Xin Eric Wang, Scott T Grafton, Miguel P Eckstein, William Yang WangAbstract:Iterative Language-Based Image Editing (ILBIE) tasks follow iterative instructions to edit images step by step. Data scarcity is a significant issue for ILBIE as it is challenging to collect large-scale examples of images before and after instruction-based changes. Yet, humans still accomplish these editing tasks even when presented with an unfamiliar image-instruction pair. Such ability results from Counterfactual thinking, the ability to think about possible alternatives to events that have happened already. In this paper, we introduce a Self-Supervised Counterfactual Reasoning (SSCR) framework that incorporates Counterfactual thinking to overcome data scarcity. SSCR allows the model to consider out-of-distribution instructions paired with previous images. With the help of cross-task consistency (CTC), we train these Counterfactual instructions in a self-supervised scenario. Extensive results show that SSCR improves the correctness of ILBIE in terms of both object identity and position, establishing a new state of the art (SOTA) on two IBLIE datasets (i-CLEVR and CoDraw). Even with only 50\% of the training data, SSCR achieves a comparable result to using complete data.
Mark Meerum Terwogt - One of the best experts on this subject based on the ideXlab platform.
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understanding of emotions based on Counterfactual Reasoning in children with autism spectrum disorders
Autism, 2014Co-Authors: Sander Begeer, Marc De Rosnay, Patty Lunenburg, Hedy Stegge, Mark Meerum TerwogtAbstract:The understanding of emotions based on Counterfactual Reasoning was studied in children with high-functioning autism spectrum disorders (n = 71) and in typically developing children (n = 71), aged 6-12 years. Children were presented with eight stories about two protagonists who experienced the same positive or negative outcome, either due to their own action or by default. Relative to the comparison group, children with high-functioning autism spectrum disorder were poor at explaining emotions based on downward Counterfactual Reasoning (i.e. contentment and relief). There were no group differences in upward Counterfactual Reasoning (i.e. disappointment and regret). In the comparison group, second-order false-belief Reasoning was related to children's understanding of second-order Counterfactual emotions (i.e. regret and relief), while children in the high-functioning autism spectrum disorder group relied more on their general intellectual skills. Results are discussed in terms of the different functions of Counterfactual Reasoning about emotion and the cognitive style of children with high-functioning autism spectrum disorder.
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brief report additive and subtractive Counterfactual Reasoning of children with high functioning autism spectrum disorders
Journal of Autism and Developmental Disorders, 2009Co-Authors: Sander Begeer, Patty Lunenburg, Mark Meerum Terwogt, Hedy SteggeAbstract:The development of additive (‘If only I had done…’) and subtractive (‘If only I had not done….’) Counterfactual Reasoning was examined in children with High Functioning Autism Spectrum Disorders (HFASD) (n = 72) and typically developing controls (n = 71), aged 6–12 years. Children were presented four stories where they could generate Counterfactuals based on a given consequent (e.g., ‘you left muddy footprints in the kitchen. How could that have been prevented?’). Children with HFASD increasingly used subtractive Counterfactuals as they got older, but controls showed an increase in additive Counterfactuals, which may be linked to their growing adaptive and flexible skills. Children with HFASD likely develop different strategies for their Counterfactual Reasoning. The role of IQ and ideational fluency will be discussed.
Ed Snelson - One of the best experts on this subject based on the ideXlab platform.
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Counterfactual Reasoning and learning systems the example of computational advertising
Journal of Machine Learning Research, 2013Co-Authors: Leon Bottou, Jonas Peters, Joaquin Quinonerocandela, Denis X Charles, Max D Chickering, Elon Portugaly, Dipankar Ray, Patrice Y Simard, Ed SnelsonAbstract:This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such predictions allow both humans and algorithms to select the changes that would have improved the system performance. This work is illustrated by experiments on the ad placement system associated with the Bing search engine.
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Counterfactual Reasoning and learning systems
Journal of Machine Learning Research, 2012Co-Authors: Leon Bottou, Jonas Peters, Joaquin Quinonerocandela, Denis X Charles, Max D Chickering, Elon Portugaly, Dipankar Ray, Patrice Y Simard, Ed SnelsonAbstract:This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such predictions allow both humans and algorithms to select the changes that would have improved the system performance. This work is illustrated by experiments carried out on the ad placement system associated with the Bing search engine.
Sander Begeer - One of the best experts on this subject based on the ideXlab platform.
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understanding of emotions based on Counterfactual Reasoning in children with autism spectrum disorders
Autism, 2014Co-Authors: Sander Begeer, Marc De Rosnay, Patty Lunenburg, Hedy Stegge, Mark Meerum TerwogtAbstract:The understanding of emotions based on Counterfactual Reasoning was studied in children with high-functioning autism spectrum disorders (n = 71) and in typically developing children (n = 71), aged 6-12 years. Children were presented with eight stories about two protagonists who experienced the same positive or negative outcome, either due to their own action or by default. Relative to the comparison group, children with high-functioning autism spectrum disorder were poor at explaining emotions based on downward Counterfactual Reasoning (i.e. contentment and relief). There were no group differences in upward Counterfactual Reasoning (i.e. disappointment and regret). In the comparison group, second-order false-belief Reasoning was related to children's understanding of second-order Counterfactual emotions (i.e. regret and relief), while children in the high-functioning autism spectrum disorder group relied more on their general intellectual skills. Results are discussed in terms of the different functions of Counterfactual Reasoning about emotion and the cognitive style of children with high-functioning autism spectrum disorder.
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brief report additive and subtractive Counterfactual Reasoning of children with high functioning autism spectrum disorders
Journal of Autism and Developmental Disorders, 2009Co-Authors: Sander Begeer, Patty Lunenburg, Mark Meerum Terwogt, Hedy SteggeAbstract:The development of additive (‘If only I had done…’) and subtractive (‘If only I had not done….’) Counterfactual Reasoning was examined in children with High Functioning Autism Spectrum Disorders (HFASD) (n = 72) and typically developing controls (n = 71), aged 6–12 years. Children were presented four stories where they could generate Counterfactuals based on a given consequent (e.g., ‘you left muddy footprints in the kitchen. How could that have been prevented?’). Children with HFASD increasingly used subtractive Counterfactuals as they got older, but controls showed an increase in additive Counterfactuals, which may be linked to their growing adaptive and flexible skills. Children with HFASD likely develop different strategies for their Counterfactual Reasoning. The role of IQ and ideational fluency will be discussed.
