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Open accessProceedings ArticleDOI
09 Mar 2009
13 Citations
User models can be useful for improving dialogue management.
This paper describes a novel method by which a dialogue agent can learn to choose an optimal dialogue strategy.
Open accessProceedings ArticleDOI
Heriberto Cuayáhuitl, Seunghak Yu 
24 Aug 2017
13 Citations
Our findings are useful for training larger-scale neural-based spoken dialogue systems.
What we can say is modern artificial dialogue systems do talk.
We show that key qualities of the generated dialogue can be manipulated by the features used for training the agent.

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How to use chat Gpt to improve vocabulay?
4 answers
To enhance vocabulary using ChatGPT, engage in group chat discussions like those facilitated by WhatsApp. ChatGPT, powered by GPT-3, can provide personalized assistance, feedback, and interactive support, boosting motivation and engagement. Utilize ChatGPT's ability to simulate human-like conversations and generate responses in a conversational manner to learn medical terminology effectively. Additionally, leverage ChatGPT's potential in generating teaching materials, assessments, and scoring assessments for educational purposes. By incorporating ChatGPT in vocabulary learning through group chat discussions, students can significantly improve their vocabulary achievement, as shown in studies employing pre-test and post-test designs. Embracing ChatGPT in educational settings can revolutionize vocabulary learning by providing individualized feedback and interactive learning experiences.
How to achieve goals?
4 answers
To achieve goals effectively, one must understand the psychological and behavioral processes involved in goal setting, striving, and attainment. The pursuit of goals is driven by specific motivations encoded in the brain, particularly in the amygdala, which plays a crucial role in adaptive outcome-seeking behavior. Additionally, utilizing mechanisms such as target collection systems, ultrasonic transducers, and control mechanisms can aid in tracking and achieving goals efficiently. In the context of autonomous agents, predicting and controlling future state distributions through goal-conditioned reinforcement learning is essential. By learning conditional probability density functions over future states and optimizing functionals of a policy's future state distribution, one can increase the likelihood of reaching specific goal states.
How does self improvement help in learning?
4 answers
Self-improvement plays a crucial role in enhancing learning outcomes across various domains. In the realm of reinforcement learning, the Self Improvement Based REwards (SIBRE) approach rewards an agent's improvement over its past performance, leading to faster convergence to optimal policies in certain environments. In the context of online learning, self-efficacy improvement positively influences participation and learning efficacy, as demonstrated through prediction models and clustering algorithms. Moreover, adaptive instructional systems, particularly self-improving systems, have been pivotal in achieving educational effectiveness comparable to human tutors, emphasizing the importance of continual self-improvement in learning technologies. By encouraging metacognitive practices, gaining perspective, and facilitating formative exercises, self-assessment fosters deep learning and valid grading practices, especially when combined with peer or instructor assessments.
How can language models be leveraged to enhance the learning process?
5 answers
Language models can be leveraged to enhance the learning process in various ways. They can enrich programming error messages with explanations and suggestions, making them more novice-friendly. By incorporating knowledge embeddings derived from language models, representations of long-tail entities in knowledge graphs can be improved, enhancing performance in tasks like link prediction and triple classification. Additionally, language models can be enhanced with sensory information and image vectors to capture holistic linguistic meaning, leading to improved performance in tasks like the GLUE benchmark and Visual Dialog benchmark. Furthermore, leveraging contrastive representations in language models can aid in generating coherent long texts with preserved structure and consistency, showcasing competitive performance in text generation tasks.
How does the effectiveness of communication in addressing ICT change resistance vary across different industries and organizations?
5 answers
Effective communication plays a crucial role in addressing resistance to change in various industries and organizations. Research emphasizes that communication is a key success factor in change management, aiding in conflict resolution and overcoming resistance. Studies suggest that organizational communication significantly influences resistance to change, with well-informed employees being more likely to adopt change processes. Furthermore, the presence of a charismatic leader who can bridge the gap between management and employees through effective communication is vital for successful change implementation. Therefore, tailored communication strategies, including transparent and timely messaging, are essential for mitigating resistance to change across different sectors, ensuring alignment with organizational goals and culture.
What are the previous research on vision-based and visual cable detection and following using autonomous underwater vehicles (auvs)?
5 answers
Previous research has focused on vision-based object detection and tracking for Autonomous Underwater Vehicles (AUVs). Xu et al. proposed a real-time object detection algorithm for underwater targets using a lightweight CNN model and contrast limited adaptive histogram equalization to enhance image quality. Widjaja et al. developed an AUV with automatic object detection and tracking systems using vision-based methods, achieving efficient motion parameters for tracking guidance. Zhu et al. introduced an end-to-end MARC framework for autonomous control of Visual Underwater Vehicles (VUVs) through perception and decision modules, demonstrating obstacle-avoidance navigation with high accuracy. Additionally, marine obstacle detection based on visual images has been proposed for autonomous navigation, utilizing a target detection algorithm for multiple types of obstacles with real-time processing capabilities.
How to care for critically ill patients polyneuropathy in the picu. critical illness polyneuropathy in the PICU?
5 answers
To care for critically ill patients with polyneuropathy in the PICU, early recognition and management are crucial. Patients may develop complete motor weakness after a prolonged ICU stay, necessitating specialized care. Critical illness polyneuropathy (CIP) is a common complication in ICU patients, requiring novel management approaches. PICU physicians, experienced in acute respiratory distress syndrome care, can provide guidance on managing adult patients with coronavirus disease 2019 in a surge capacity. Communication among providers during transitions of care is vital for critically ill children, including intra-ICU handoffs and transfers between different care settings. Overall, a multidisciplinary approach involving physicians, nurses, therapists, and specialists is essential for the successful care of critically ill patients with polyneuropathy in the PICU.
What commercial games are actually using reinforcement learning?
4 answers
Commercial games like Ubisoft's Roller Championsand multiplayer deathmatch first-person shooters utilizing platforms such as ViZDoom, DeepMind Lab, and Unity’s ML-Agentshave incorporated reinforcement learning (RL) for Artificial Intelligence (AI) design. In Roller Champions, RL systems are employed to train AI models quickly to adapt to gameplay changes, develop sophisticated strategies, and assist in game balance. On the other hand, in multiplayer deathmatch games, RL agents are created using various platforms and training architectures to compete against humans, potentially enhancing gameplay compared to traditional AI. These examples showcase the practical application of RL in modern video game development, demonstrating its effectiveness in enhancing AI capabilities and overall gaming experience.
How can information geometry be used in machine learning?
5 answers
Information geometry, a field focusing on the geometric structures of probability distributions, plays a crucial role in machine learning applications. By treating parametric families of distributions as Riemannian manifolds equipped with metrics like the Fisher metric tensor, information geometry provides a framework for studying statistical models and their applications in various information sciences. Machine learning leverages information geometry concepts to analyze and process data efficiently. For instance, machine learning algorithms have been successfully applied to study mathematical structures, formulate conjectures, and aid in problem-solving by utilizing artificial intelligence techniques like neural networks and support vector machines. Additionally, information geometry can offer insights into skill discovery algorithms in reinforcement learning, showcasing how geometric perspectives can enhance learning processes in AI systems.
What can transformers learn in context?
5 answers
Transformers have shown remarkable capabilities in in-context learning, such as predicting per-token and next-token sequences without parameter updates. They can be trained to mimic ordinary least squares in linear regression tasks and exhibit robustness to distribution shifts, although they are sensitive to covariate distribution changes. Additionally, transformers excel in decision-making tasks through supervised pretraining, adapting decision-making strategies to new tasks and unknown structures. They can efficiently implement algorithms like gradient descent when trained over random instances, with single attention layers mimicking preconditioned gradient descent iterations. These findings highlight the diverse and powerful learning capabilities of transformers in various contexts, ranging from predictive modeling to decision-making algorithms.
Are there any good papers that compare different AI tools?
5 answers
Several papers compare different AI tools in various applications. For instance, one study compared fuzzy logic and artificial neural networks (ANN) in cloud computing-based resource scheduling, finding that fuzzy logic outperformed ANN. Another research paper analyzed AI tools for spam review detection, highlighting the trust differences between younger and older adults in Text-AI and Behaviour-AI tools. Additionally, a study focused on AI tools combating information disorder, ranking existing AI systems based on accuracy and comprehensiveness, revealing varying levels of development among the examined tools. Furthermore, a paper compared machine learning algorithms using performance parameters like R squared and Mean Square Error, aiming to identify the best model for analyzing JAVA projects. Lastly, a review paper discussed AI algorithms and applications for smart machine tools, categorizing them into learning algorithms and discussing their strengths, weaknesses, challenges, and future trends.