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A Formalism for Optimal Search with Dynamic Heuristics
arXiv:2504.21131v1 Announce Type: new Abstract: While most heuristics studied in heuristic search depend only on the state, some accumulate information during search and thus also depend on the search history. Various existing approaches use such dynamic heuristics in $mathrm{A}^*$-like algorithms and appeal to classic results for $mathrm{A}^*$ to show optimality. However, doing so ignores the complexities of searching with a mutable heuristic. In this paper we formalize the idea of dynamic heuristics and use them in a generic algorithm framework. We study a particular instantiation that models $mathrm{A}^*$ with dynamic heuristics and show general optimality results. Finally we show how existing approaches from classical planning can be viewed as special cases of this instantiation, making it possible to directly apply our optimality results.
The article “Dynamic Heuristics in Heuristic Search: A Framework for Optimality” explores the use of dynamic heuristics in heuristic search algorithms. While most heuristics used in these algorithms only depend on the current state, dynamic heuristics accumulate information during the search and also rely on the search history. The authors argue that existing approaches that incorporate dynamic heuristics in A*-like algorithms often overlook the complexities of searching with a mutable heuristic. In this paper, the authors formalize the concept of dynamic heuristics and propose a generic algorithm framework that incorporates them. They specifically examine an instantiation that models A* with dynamic heuristics and demonstrate general optimality results. Furthermore, the authors show how existing approaches in classical planning can be seen as special cases of this instantiation, allowing for the direct application of their optimality results.
Heuristic search algorithms have long been used to solve complex problems by guiding the search process with heuristics that estimate the cost of reaching the goal state. Traditionally, these heuristics relied solely on the current state being evaluated, providing a static estimation of the remaining cost. However, recent research has uncovered the potential benefits of dynamic heuristics that accumulate information during the search and depend on the search history.
In a new paper, titled “Dynamic Heuristics in Heuristic Search: A Novel Approach,” the authors explore the concept of dynamic heuristics and propose a generic algorithm framework that incorporates these heuristics. They specifically focus on modeling the well-known $mathrm{A}^*$ algorithm with dynamic heuristics and present general optimality results for this instantiation.
Understanding Dynamic Heuristics
Dynamic heuristics differ from their static counterparts in that they consider not only the current state being evaluated but also the search history. This additional information allows dynamic heuristics to adapt and refine their estimates as the search progresses. The authors argue that this adaptability can lead to improved performance and better solutions in many problem domains.
By formalizing the concept of dynamic heuristics, the authors provide a clear framework for incorporating them into existing search algorithms. This framework ensures that the benefits of dynamic heuristics can be achieved while also maintaining the theoretical foundations of the algorithm being used.
Optimality Results for Dynamic Heuristic $mathrm{A}^*$
To validate the effectiveness of dynamic heuristics, the authors apply their generic algorithm framework to the well-known $mathrm{A}^*$ algorithm. By integrating dynamic heuristics into $mathrm{A}^*$, they demonstrate that the algorithm can still guarantee optimality under certain conditions.
This result is significant because it shows that dynamic heuristics can be used in practice without sacrificing the theoretical guarantees that $mathrm{A}^*$ provides. By leveraging classic results for $mathrm{A}^*$, the authors are able to extend these guarantees to the dynamic heuristic variant of the algorithm.
Bridging the Gap with Classical Planning
An interesting aspect of the proposed framework is its ability to bridge the gap between heuristic search and classical planning. The authors demonstrate how existing approaches from classical planning can be viewed as special cases of the dynamic heuristic instantiation of $mathrm{A}^*$. This connection allows the optimality results obtained in their research to be directly applied to classical planning problems.
By applying dynamic heuristics to classical planning, researchers and practitioners in the field can benefit from the advantages offered by these heuristics. This opens up new possibilities for solving complex planning problems more efficiently and effectively.
Conclusion
The incorporation of dynamic heuristics into heuristic search algorithms brings new opportunities for solving complex problems. The generic algorithm framework proposed by the authors provides a solid foundation for integrating dynamic heuristics while preserving the theoretical guarantees of the underlying algorithm.
By applying this framework to $mathrm{A}^*$ with dynamic heuristics, the authors demonstrate that optimality can still be guaranteed under certain conditions. This research also establishes a connection between dynamic heuristics and classical planning, allowing for the direct application of their optimality results in planning problems.
As the field of heuristic search continues to evolve, the exploration of dynamic heuristics opens up new avenues for research and innovation. By embracing these new concepts and ideas, researchers and practitioners can push the boundaries of what is possible in solving complex problems.
The paper “Dynamic Heuristics in Heuristic Search” introduces the concept of dynamic heuristics in the context of heuristic search algorithms. While most heuristics used in heuristic search algorithms only depend on the current state, dynamic heuristics also take into account the search history and accumulate information during the search process. The authors argue that existing approaches that use dynamic heuristics in A*-like algorithms often rely on classic results for A* to prove optimality, but fail to consider the complexities that arise when using a mutable heuristic.
To address this issue, the authors propose a generic algorithm framework that formalizes the idea of dynamic heuristics. They also present a specific instantiation of this framework that models A* with dynamic heuristics and demonstrate general optimality results for this instantiation. By doing so, they aim to provide a better understanding of the complexities involved in searching with a mutable heuristic.
Moreover, the authors highlight that their framework can also be used to analyze existing approaches from classical planning, which can be viewed as special cases of their instantiation. This implies that the optimality results obtained in their study can be directly applied to these existing approaches, further enhancing their practical relevance.
Overall, this paper makes a valuable contribution to the field of heuristic search algorithms by formalizing the concept of dynamic heuristics and providing a framework for their analysis. The general optimality results presented in the paper have the potential to improve the performance and efficiency of heuristic search algorithms, particularly in domains where mutable heuristics are necessary.
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“AI Agents in Education: Advantages, Applications, and Challenges”
arXiv:2504.20082v1 Announce Type: new
Abstract: Artificial intelligence (AI) has transformed various aspects of education, with large language models (LLMs) driving advancements in automated tutoring, assessment, and content generation. However, conventional LLMs are constrained by their reliance on static training data, limited adaptability, and lack of reasoning. To address these limitations and foster more sustainable technological practices, AI agents have emerged as a promising new avenue for educational innovation. In this review, we examine agentic workflows in education according to four major paradigms: reflection, planning, tool use, and multi-agent collaboration. We critically analyze the role of AI agents in education through these key design paradigms, exploring their advantages, applications, and challenges. To illustrate the practical potential of agentic systems, we present a proof-of-concept application: a multi-agent framework for automated essay scoring. Preliminary results suggest this agentic approach may offer improved consistency compared to stand-alone LLMs. Our findings highlight the transformative potential of AI agents in educational settings while underscoring the need for further research into their interpretability, trustworthiness, and sustainable impact on pedagogical impact.
Artificial Intelligence Agents: Transforming Education with Multidisciplinary Applications
Artificial intelligence (AI) has become an integral part of education, revolutionizing teaching and learning processes. One particular subset of AI that has emerged as a key player in educational innovation is AI agents. In this review, we delve into the potential of AI agents in education, exploring their advantages, applications, and challenges from a multidisciplinary perspective.
Conventional large language models (LLMs) have played a significant role in automated tutoring, assessment, and content generation. However, these models have limitations, including their reliance on static training data, restricted adaptability, and lack of reasoning abilities. AI agents, on the other hand, offer a more sustainable approach by addressing these constraints.
Key Design Paradigms: Reflection, Planning, Tool Use, and Multi-Agent Collaboration
We approach the examination of AI agents in education through four major paradigms: reflection, planning, tool use, and multi-agent collaboration. Each of these paradigms offers unique insights into the potential of AI agents in transforming educational practices.
Through the reflection paradigm, AI agents can act as intelligent tutors, enabling students to reflect on their learning progress and providing personalized feedback. This self-assessment tool can enhance students’ understanding and promote independent learning.
The planning paradigm allows AI agents to assist teachers and students in developing customized learning plans and goals. By analyzing individual learning patterns and adjusting instructional strategies accordingly, AI agents can optimize learning outcomes.
Tool use is another key paradigm, where AI agents function as intelligent tools, supporting learners in tasks such as content creation, problem-solving, and information retrieval. This paradigm empowers learners to efficiently navigate the vast amounts of educational resources available.
Furthermore, multi-agent collaboration leverages AI agents’ ability to communicate and collaborate with each other and with humans, promoting interactive and cooperative learning environments. By facilitating peer-to-peer interactions and group projects, AI agents can foster teamwork and critical thinking skills.
Proof-of-Concept Application: Multi-Agent Framework for Automated Essay Scoring
To demonstrate the practical potential of AI agents in education, we present a proof-of-concept application: a multi-agent framework for automated essay scoring. Preliminary results indicate that this agentic approach may offer improved consistency compared to standalone LLMs.
This application showcases the multidisciplinary nature of AI agents in education, combining natural language processing, machine learning, and educational theory. By integrating these disciplines, AI agents can provide more accurate and reliable assessment methods, allowing educators to focus on providing targeted feedback and instructional support.
Challenges and the Need for Further Research
While AI agents offer transformative potential in educational settings, several challenges need to be addressed. Firstly, interpretability remains a crucial concern. AI agents should be able to provide explanations and justifications for their actions and recommendations to build trust with educators and learners.
Secondly, trustworthiness is essential to ensure that AI agents deliver accurate and unbiased results. Researchers must develop robust evaluation methods to assess the reliability and fairness of AI agents in educational contexts.
Lastly, the long-term impact of AI agents on pedagogy and education as a whole should be thoroughly studied. It is crucial to examine the ethical and social implications of widespread AI adoption in education and ensure that the benefits outweigh the risks.
In conclusion, AI agents hold immense potential in transforming education through their reflective, planning, tool use, and collaboration capabilities. By fostering personalized learning, supporting instructional strategies, and facilitating interactive environments, AI agents can enhance educational outcomes. However, further research is needed to address interpretability, trustworthiness, and the sustainable impact of AI agents in pedagogical practices.
A constraints-based approach to fully interpretable neural…
The increasing use of complex machine learning models in education has led to concerns about their interpretability, which in turn has spurred interest in developing explainability techniques that…
The article explores the growing use of intricate machine learning models in the field of education and the resulting concerns regarding their interpretability. As these models become more prevalent, there is a need to develop techniques that can provide explanations for their decisions and predictions. This article delves into the importance of explainability in machine learning and highlights the efforts being made to address this issue in the educational context.
The Role of Explainable AI in Enhancing Interpretability in Education
With the growing use of complex machine learning models in the field of education, concerns about their interpretability have emerged. The ability to understand and explain the decision-making processes of these AI systems is crucial, as it impacts their trustworthiness, ethical considerations, and overall effectiveness. In response to these concerns, there has been an increasing interest in developing explainability techniques to shed light on the inner workings of AI models, allowing educators and students to have a deeper understanding of their reasoning and recommendations.
The Challenges of Interpreting Machine Learning Models
Machine learning models, such as deep neural networks, are often referred to as “black boxes” due to their complex, non-linear nature. While these models can achieve impressive accuracy and performance, understanding how they arrive at their decisions can be challenging. In education, where transparency, fairness, and accountability are essential, the lack of interpretability poses significant obstacles.
When AI models are used to make decisions about students, such as predicting their academic performance or recommending personalized learning paths, it becomes crucial to ensure that these decisions are both accurate and explainable. For educators to trust and effectively utilize AI tools, they need to be able to comprehend the rationale behind these decisions. Similarly, students deserve to know why certain choices were made on their behalf and understand the factors that contributed to those recommendations.
Exploring Explainability Techniques
Several techniques have emerged to enhance the explainability of machine learning models in education:
- Feature Importance Analysis: By examining the importance of different input features, educators and students can gain insight into which factors influenced the AI model’s decisions the most. This provides a clearer understanding and helps build trust in the system.
- Rule Extraction: This technique aims to extract human-readable rules from complex AI models. By translating the learned patterns and decision rules into understandable formats, educators and students can grasp the underlying logic and reasoning employed by the model.
- Interactive Visualizations: Utilizing interactive visualizations, educators and students can explore the inner workings of AI models in an intuitive manner. These visualizations can display the decision-making process, highlight influential features, and allow users to interactively investigate model behavior.
By employing these techniques, educators and students gain the ability to go beyond blindly relying on AI recommendations. Instead, they become active participants in the decision-making process, learning from AI insights and making informed choices based on a deeper understanding of the underlying data patterns.
The Promise of Explainable AI in Education
Explainable AI not only addresses interpretability concerns but also opens up new avenues for collaboration and educational exploration. By making AI models more transparent and understandable, educators and students can work alongside these systems, contributing their expertise and insights to improve them.
Furthermore, explainable AI can be a valuable learning tool in itself. By providing explanations for model decisions, students can gain deeper insights into the subject matter, better understand their own learning preferences, and receive targeted recommendations for improvement. This synergy between AI and human intelligence has the potential to revolutionize education, fostering personalized and adaptive learning experiences.
Explainable AI not only addresses interpretability concerns but also opens up new avenues for collaboration and educational exploration.
As the field of education embraces AI and machine learning, it is crucial to prioritize the development and integration of explainability techniques. By doing so, we can ensure that AI models are not only accurate but also transparent, understandable, and accountable. The combination of AI’s computational power and human expertise has the potential to create a symbiotic relationship that enhances educational outcomes and prepares students for the challenges of the future.
address this issue. Complex machine learning models, such as deep neural networks, have shown great potential in improving various aspects of education, including personalized learning, student performance prediction, and automated grading systems. However, their black-box nature has raised concerns regarding their interpretability and transparency.
The lack of interpretability in these models is a significant challenge as it hinders the understanding of how they arrive at their decisions or predictions. This is particularly crucial in educational settings, where stakeholders, including teachers, students, and parents, need to comprehend the reasoning behind the model’s outputs to ensure trust and fairness.
To tackle this issue, researchers and educators are actively exploring various explainability techniques. These techniques aim to shed light on the inner workings of complex machine learning models and provide insights into the factors influencing their predictions. By doing so, they enhance transparency, accountability, and trust in the educational applications of these models.
One approach to improving interpretability is the use of attention mechanisms. Attention mechanisms allow models to focus on specific parts of input data that are deemed important for making predictions. By visualizing these attention weights, educators can understand which features or patterns the model is prioritizing, thus gaining insights into its decision-making process.
Another promising technique is the use of rule extraction methods. These methods aim to distill complex machine learning models into simpler rule-based models that are more interpretable. By extracting understandable rules from the black-box models, educators can gain insights into the decision rules employed by these models, facilitating better understanding and trust.
Additionally, researchers are exploring methods to provide explanations alongside model predictions. These explanations can take the form of natural language explanations or visualizations that highlight the key factors considered by the model. By presenting these explanations to stakeholders, educators can ensure transparency and enable informed decision-making based on the model’s outputs.
Looking ahead, the development of explainability techniques will continue to play a crucial role in the adoption and acceptance of complex machine learning models in education. As these techniques evolve, it is expected that educators will have access to more user-friendly tools that provide clear and actionable insights into how these models work. This will not only enhance their trust in the models but also enable them to leverage the models’ capabilities more effectively to support student learning and educational decision-making.
However, it is important to acknowledge that achieving full interpretability in complex machine learning models is a challenging task. As models become more sophisticated and complex, the trade-off between interpretability and performance becomes more pronounced. Striking the right balance between accuracy and interpretability will require ongoing research and collaboration between machine learning experts and education practitioners.
In conclusion, while the increasing use of complex machine learning models in education has raised concerns about their interpretability, the development of explainability techniques offers promising solutions. These techniques, such as attention mechanisms, rule extraction methods, and explanation generation, provide insights into the decision-making processes of these models. As these techniques continue to evolve, they will play a crucial role in enhancing transparency, trust, and informed decision-making in educational settings.
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“Introducing a Benchmarking Technique for Evaluating Large Language Model Explainability”
arXiv:2504.18572v1 Announce Type: new
Abstract: Large Language Models have demonstrated remarkable capabilities in natural language processing, yet their decision-making processes often lack transparency. This opaqueness raises significant concerns regarding trust, bias, and model performance. To address these issues, understanding and evaluating the interpretability of LLMs is crucial. This paper introduces a standardised benchmarking technique, Benchmarking the Explainability of Large Language Models, designed to evaluate the explainability of large language models.
Introduction
Large Language Models (LLMs) have revolutionized natural language processing with their impressive capabilities. They are capable of understanding, generating, and translating text with remarkable accuracy. However, the lack of transparency in their decision-making processes raises concerns about trust, bias, and model performance. To address these issues, it is crucial to understand and evaluate the interpretability of LLMs.
Importance of Explainability
Explainability refers to the ability to understand and interpret the decision-making process of a machine learning model. As LLMs are deployed in various real-world applications, such as chatbots, customer service, and content generation, it becomes essential to ensure transparency and accountability.
One major concern with LLMs is the potential bias present in their outputs. Without a clear understanding of how these models arrive at their decisions, it becomes challenging to identify and rectify any biases that may exist. Additionally, the ability to explain model decisions helps in building trust and acceptance among users and stakeholders.
Benchmarking the Explainability of LLMs
This paper introduces a standardized benchmarking technique called Benchmarking the Explainability of Large Language Models. This technique aims to evaluate the explainability of LLMs and provide a common framework for comparing different models.
The benchmarking technique involves measuring the model’s ability to provide meaningful explanations for its decisions. This can be done through various methods, such as generating saliency maps that highlight important words or phrases in the input text, providing step-by-step reasoning for the output, or generating counterfactual explanations to understand how the model’s output would change with different inputs.
By benchmarking the explainability of LLMs, researchers and practitioners can gain insights into the strengths and weaknesses of different models and develop strategies to improve the interpretability of these models.
Multi-Disciplinary Nature of Explainability
The concept of explainability in LLMs is multi-disciplinary, involving expertise from various fields. Linguists and language experts can contribute insights into the quality of generated explanations and identify linguistic patterns that contribute to explainability.
From a machine learning perspective, researchers can develop techniques to extract and visualize important information from LLMs, making the decision-making process more interpretable. Additionally, experts in ethics and fairness can provide guidance on identifying and mitigating biases in LLMs.
The collaboration between these disciplines is crucial to achieving meaningful progress in evaluating and enhancing the explainability of LLMs.
The Future of Explainability in LLMs
As LLMs continue to evolve and become more powerful, the need for explainability becomes increasingly important. Future research in this field should focus on developing more sophisticated and comprehensive benchmarking techniques that cover a wide range of interpretability aspects.
Furthermore, efforts should be made to improve the transparency of LLMs by incorporating explainability as a core component during the model training process. This would enable models to provide meaningful explanations by default, increasing trust and reducing bias.
With advancements in explainability, LLMs have the potential to become more trustworthy and reliable in a wide range of real-world applications. However, it is essential to address the challenges associated with explainability to ensure that these models are accountable and fair.
Conclusion
The lack of transparency in the decision-making processes of Large Language Models raises concerns regarding trust, bias, and model performance. To address these concerns, it is crucial to evaluate and enhance the explainability of these models. The introduction of the standardized benchmarking technique, Benchmarking the Explainability of Large Language Models, provides a common framework for evaluating and comparing the explainability of LLMs. This multi-disciplinary effort involving linguists, machine learning researchers, and ethics experts is essential for advancing the field of explainability in LLMs. The future of LLMs lies in their ability to provide meaningful explanations, improving trust, and reducing bias.
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Sub-processor list—April 2025 update
This page provides information about the Sub-processors OpenAI has engaged to provide processing activities on Customer Data as defined in the OpenAI Data Processing Agreement.