içgörü - Artificial Intelligence - # Explainable Goal Recognition

Explainable Goal Recognition Using Weight of Evidence (WoE): A Human-Centered Approach

Q: How could the XGR model be extended to handle more complex and dynamic environments beyond the game domains studied?

The eXplainable Goal Recognition (XGR) model could be extended to handle more complex and dynamic environments by incorporating several enhancements. First, integrating real-time data streams from sensors or user interactions could allow the model to adapt to rapidly changing conditions, such as those found in smart homes or autonomous vehicles. This would involve developing a more sophisticated Goal Markov Decision Process (Goal MDP) that can account for continuous state changes and probabilistic transitions, rather than relying solely on discrete states as in the game domains. Second, the model could benefit from incorporating multi-agent interactions, where the behavior of multiple agents influences goal recognition. This would require the development of a collaborative framework that allows the XGR model to analyze and interpret the actions of various agents in relation to one another, enhancing its ability to infer goals in environments where agents may have conflicting or cooperative objectives. Additionally, the XGR model could be enhanced by integrating machine learning techniques that allow it to learn from past interactions and user feedback. By employing reinforcement learning or online learning algorithms, the model could continuously improve its goal recognition capabilities and the quality of its explanations based on user interactions and outcomes in real-world scenarios. Finally, incorporating contextual information, such as user preferences, environmental factors, and historical data, could provide a richer basis for generating explanations. This would enable the XGR model to produce more nuanced and relevant explanations that align with the specific needs and expectations of users in complex environments.

Q: What are the potential limitations of using the Weight of Evidence (WoE) concept for generating explanations, and how could these be addressed?

While the Weight of Evidence (WoE) concept offers a robust framework for generating explanations, it does have potential limitations. One significant limitation is its reliance on the availability and quality of data. If the data used to calculate WoE is sparse or biased, the resulting explanations may be misleading or inaccurate. To address this, it is crucial to ensure that the data used for training and evaluation is comprehensive and representative of the various scenarios the model may encounter. Implementing data augmentation techniques or utilizing transfer learning from related domains could help mitigate this issue. Another limitation is the potential for oversimplification in the explanations generated by WoE. The model may focus on the most statistically significant evidence while neglecting other relevant factors that could provide a more holistic understanding of the agent's behavior. To counter this, the XGR model could incorporate a multi-faceted approach to explanation generation, combining WoE with other explanatory frameworks, such as causal inference or counterfactual reasoning, to enrich the context and depth of the explanations. Furthermore, the WoE framework may struggle with dynamic environments where the relationships between actions and goals are not static. In such cases, the model could be enhanced by integrating temporal reasoning capabilities, allowing it to account for how the significance of evidence may change over time. This could involve developing a temporal WoE model that considers the sequence and timing of actions in relation to goal recognition.

Q: How might the XGR model's explanations be integrated with other AI systems to enhance human-AI collaboration in high-stakes decision-making scenarios?

Integrating the XGR model's explanations with other AI systems can significantly enhance human-AI collaboration, particularly in high-stakes decision-making scenarios. One approach is to create a unified interface that combines the XGR model with decision support systems, allowing users to receive real-time explanations alongside actionable insights. This integration would enable decision-makers to understand the rationale behind AI predictions, fostering trust and facilitating more informed choices. Additionally, the XGR model could be embedded within multi-modal AI systems that utilize various data sources, such as visual, auditory, and textual information. By providing explanations that draw on diverse inputs, the model can offer a more comprehensive understanding of the situation at hand. For instance, in medical diagnosis, the XGR model could explain the reasoning behind a diagnosis while also integrating patient data, medical history, and relevant literature, thereby supporting healthcare professionals in making critical decisions. Moreover, the XGR model's explanations could be utilized in training and simulation environments, where human operators can practice decision-making in simulated high-stakes scenarios. By providing explanations for AI actions during training, users can learn to interpret AI behavior and develop strategies for effective collaboration in real-world situations. Finally, incorporating feedback mechanisms that allow users to provide input on the explanations generated by the XGR model can create a feedback loop that enhances the model's performance over time. This iterative process would enable the model to adapt to user preferences and improve the relevance and clarity of its explanations, ultimately leading to more effective human-AI collaboration in high-stakes decision-making contexts.

Temel Kavramlar

The core message of this work is to develop an explainable goal recognition (GR) model, called XGR, that generates human-understandable explanations for predicted goals by leveraging the Weight of Evidence (WoE) concept and insights from human-agent studies on how people explain others' behavior.

Özet

This paper proposes a human-centered approach to explainable goal recognition (GR). The key contributions are:

A conceptual framework for GR explanations, derived from two human-agent studies in the Sokoban and StarCraft game domains. The framework identifies 11 key concepts that people use to explain others' behavior, such as observational markers, plans, goals, and causal/conditional/contrastive relationships.
The development of the eXplainable Goal Recognition (XGR) model, which generates explanations for GR agents using the Weight of Evidence (WoE) concept. The model focuses on providing explanations for 'why' and 'why not' questions regarding predicted goals.
Computational evaluation of the XGR model on 8 GR benchmark domains, as well as three user studies. The first study assesses the efficiency of generating human-like explanations in the Sokoban domain. The second examines the perceived explainability of the model in the same domain. The third evaluates the model's effectiveness in supporting decision-making in the domain of illegal fishing detection.

The results demonstrate that the XGR model significantly enhances user understanding, trust, and decision-making compared to baseline models, highlighting its potential to improve human-agent collaboration.

Özeti Özelleştir

Yapay Zeka ile Yeniden Yaz

Alıntıları Oluştur

Kaynağı Çevir

Başka Bir Dile

Zihin Haritası Oluştur

kaynak içeriğinden

Kaynak

arxiv.org

İstatistikler

"The player positioned itself on top of the box, leading me to believe it is going to push down on the box to reach goal 2."
"If the player keeps pushing the two boxes together, it would be impossible for box 2 to be put back onto a goal."
"Given the player's last move, box 1 belongs on goal 3"
"The player would have taken different steps if position 1 was the goal"

Alıntılar

"It might just do old classic seven gate [a game strategy]."
"This is exactly what I was talking about, you do something to try to force them."
"It's actually going to look for a run by here with this scan it looks like but unfortunately unable to find it with the ravager here poking away."

Önemli Bilgiler Şuradan Elde Edildi

Towards Explainable Goal Recognition Using Weight of Evidence (WoE): A Human-Centered Approach

by Abeer Alsheh... : arxiv.org 09-19-2024

https://arxiv.org/pdf/2409.11675.pdf

Towards Explainable Goal Recognition Using Weight of Evidence (WoE): A Human-Centered Approach

Daha Derin Sorular

How could the XGR model be extended to handle more complex and dynamic environments beyond the game domains studied?

The eXplainable Goal Recognition (XGR) model could be extended to handle more complex and dynamic environments by incorporating several enhancements. First, integrating real-time data streams from sensors or user interactions could allow the model to adapt to rapidly changing conditions, such as those found in smart homes or autonomous vehicles. This would involve developing a more sophisticated Goal Markov Decision Process (Goal MDP) that can account for continuous state changes and probabilistic transitions, rather than relying solely on discrete states as in the game domains.
Second, the model could benefit from incorporating multi-agent interactions, where the behavior of multiple agents influences goal recognition. This would require the development of a collaborative framework that allows the XGR model to analyze and interpret the actions of various agents in relation to one another, enhancing its ability to infer goals in environments where agents may have conflicting or cooperative objectives.
Additionally, the XGR model could be enhanced by integrating machine learning techniques that allow it to learn from past interactions and user feedback. By employing reinforcement learning or online learning algorithms, the model could continuously improve its goal recognition capabilities and the quality of its explanations based on user interactions and outcomes in real-world scenarios.
Finally, incorporating contextual information, such as user preferences, environmental factors, and historical data, could provide a richer basis for generating explanations. This would enable the XGR model to produce more nuanced and relevant explanations that align with the specific needs and expectations of users in complex environments.

What are the potential limitations of using the Weight of Evidence (WoE) concept for generating explanations, and how could these be addressed?

While the Weight of Evidence (WoE) concept offers a robust framework for generating explanations, it does have potential limitations. One significant limitation is its reliance on the availability and quality of data. If the data used to calculate WoE is sparse or biased, the resulting explanations may be misleading or inaccurate. To address this, it is crucial to ensure that the data used for training and evaluation is comprehensive and representative of the various scenarios the model may encounter. Implementing data augmentation techniques or utilizing transfer learning from related domains could help mitigate this issue.
Another limitation is the potential for oversimplification in the explanations generated by WoE. The model may focus on the most statistically significant evidence while neglecting other relevant factors that could provide a more holistic understanding of the agent's behavior. To counter this, the XGR model could incorporate a multi-faceted approach to explanation generation, combining WoE with other explanatory frameworks, such as causal inference or counterfactual reasoning, to enrich the context and depth of the explanations.
Furthermore, the WoE framework may struggle with dynamic environments where the relationships between actions and goals are not static. In such cases, the model could be enhanced by integrating temporal reasoning capabilities, allowing it to account for how the significance of evidence may change over time. This could involve developing a temporal WoE model that considers the sequence and timing of actions in relation to goal recognition.

How might the XGR model's explanations be integrated with other AI systems to enhance human-AI collaboration in high-stakes decision-making scenarios?

Integrating the XGR model's explanations with other AI systems can significantly enhance human-AI collaboration, particularly in high-stakes decision-making scenarios. One approach is to create a unified interface that combines the XGR model with decision support systems, allowing users to receive real-time explanations alongside actionable insights. This integration would enable decision-makers to understand the rationale behind AI predictions, fostering trust and facilitating more informed choices.
Additionally, the XGR model could be embedded within multi-modal AI systems that utilize various data sources, such as visual, auditory, and textual information. By providing explanations that draw on diverse inputs, the model can offer a more comprehensive understanding of the situation at hand. For instance, in medical diagnosis, the XGR model could explain the reasoning behind a diagnosis while also integrating patient data, medical history, and relevant literature, thereby supporting healthcare professionals in making critical decisions.
Moreover, the XGR model's explanations could be utilized in training and simulation environments, where human operators can practice decision-making in simulated high-stakes scenarios. By providing explanations for AI actions during training, users can learn to interpret AI behavior and develop strategies for effective collaboration in real-world situations.
Finally, incorporating feedback mechanisms that allow users to provide input on the explanations generated by the XGR model can create a feedback loop that enhances the model's performance over time. This iterative process would enable the model to adapt to user preferences and improve the relevance and clarity of its explanations, ultimately leading to more effective human-AI collaboration in high-stakes decision-making contexts.