This research explores the potential of Large Language Models (LLMs) to utilize psychometric values, specifically personality information, within the context of video game character development. Affective Computing (AC) systems quantify a Non-Player character's (NPC) psyche, and an LLM can take advantage of the system's information by using the values for prompt generation. The research shows an LLM can consistently represent a given personality profile, thereby enhancing the human-like characteristics of game characters. Repurposing a human examination, the International Personality Item Pool (IPIP) questionnaire, to evaluate an LLM shows that the model can accurately generate content concerning the personality provided. Results show that the improvement of LLM, such as the latest GPT-4 model, can consistently utilize and interpret a personality to represent behavior.

This article was published as a part of the Data Science Blogathon. Hierarchical clustering is one of the most famous clustering techniques used in unsupervised machine learning. K-means and hierarchical clustering are the two most popular and effective clustering algorithms. The working mechanism they apply in the backend allows them to provide such a high level of performance. In this article, we will discuss hierarchical clustering and its types, its working mechanisms, its core intuition, the pros and cons of using this clustering strategy and conclude with some fundamentals to remember for this practice.

K-Nearest Neighbor(KNN) algorithm is a poplar model and falls under the Supervised Learning and it can be used to solve both classification and regression problems. In this article, I would be giving you a detailed explanation and how this model works. K-Nearest Neighbor is one of the simplest Machine Learning algorithms based on Supervised Learning technique. KNN algorithm assumes the similarity between the new data and available data and put the new case into the category that is most similar to the available categories. The value of the K is very important.

Facial emotion detection is a common issue focused on in the field of cognitive science. An attempt to understand what exactly we as humans see in each other that gives us insight into other emotions is a challenge we can approach from an artificial intelligence side. While I don't have enough experience in psychology or even artificial intelligence to determine these factors, we can always start off by building a model to determine at least the start of this question. Fer2013 is a dataset with pictures of individuals labeled with the emotions of anger, happiness, surprise, disgust, and sadness. When testing humans on the dataset to correctly identify the facial expression of a set of pictures within the set, the accuracy is 65%.

Despite the success of supervised machine learning and deep learning, there's a school of thought that says that unsupervised learning has even greater potential. The learning of a supervised learning system is limited by its training; i.e., a supervised learning system can learn only those tasks that it's trained for. By contrast, an unsupervised system could theoretically achieve "artificial general intelligence," meaning the ability to learn any task a human can learn. If the biggest problem with supervised learning is the expense of labeling the training data, the biggest problem with unsupervised learning (where the data is not labeled) is that it often doesn't work very well. Nevertheless, unsupervised learning does have its uses: It can sometimes be good for reducing the dimensionality of a data set, exploring the pattern and structure of the data, finding groups of similar objects, and detecting outliers and other noise in the data.

Machine learning is a branch of artificial intelligence that includes algorithms for automatically creating models from data. At a high level, there are four kinds of machine learning: supervised learning, unsupervised learning, reinforcement learning, and active machine learning. Since reinforcement learning and active machine learning are relatively new, they are sometimes omitted from lists of this kind. You could also add semi-supervised learning to the list, and not be wrong. Supervised learning starts with training data that are tagged with the correct answers (target values).

"What gets measured, gets managed " – Peter Drucker The most important aim of all the clustering techniques is to group together the similar data points. K-means clustering algorithm is an unsupervised machine learning algorithm. It is a method of vector quantization that aims at grouping the similar data by minimizing the squared error function. You can apply k-means to any clustering problem provided you are having proper feature vector (Vector-Space Model) from data points and a similarity/distance measure that can measure similarity/distance between the feature vectors. The k-means clustering algorithm is used when you have unlabeled data (i.e., data without defined categories or groups).

The $k$-Means clustering problem on $n$ points is NP-Hard for any dimension $d\ge 2$, however, for the 1D case there exist exact polynomial time algorithms. Previous literature reported an $O(kn^2)$ time dynamic programming algorithm that uses $O(kn)$ space. We present a new algorithm computing the optimal clustering in only $O(kn)$ time using linear space. For $k = \Omega(\lg n)$, we improve this even further to $n 2^{O(\sqrt{ \lg \lg n \lg k})}$ time. We generalize the new algorithm(s) to work for the absolute distance instead of squared distance and to work for any Bregman Divergence as well.

Pairwise data in empirical sciences typically violate metricity, either due to noise or due to fallible estimates, and therefore are hard to analyze by conventional machine learning technology. In this paper we therefore study ways to work around this problem. First, we present an alternative embedding to multidimensional scaling (MDS) that allows us to apply a variety of classical machine learning and signal processing algorithms. The class of pairwise grouping algorithms which share the shift-invariance property is statistically invariant under this embedding procedure, leading to identical assignments of objects to clusters. Based on this new vectorial representation, denoising methods are applied in a second step. Both steps provide a theoretically well controlled setup to translate from pairwise data to the respective denoised metric representation. We demonstrate the practical usefulness of our theoretical reasoning by discovering structure in protein sequence data bases, visibly improving performance upon existing automatic methods. 1 Introduction Unsupervised grouping or clustering aims at extracting hidden structure from data (see e.g.

Pairwise data in empirical sciences typically violate metricity, either due to noise or due to fallible estimates, and therefore are hard to analyze by conventional machine learning technology. In this paper we therefore study ways to work around this problem. First, we present an alternative embedding to multidimensional scaling (MDS) that allows us to apply a variety of classical machine learning and signal processing algorithms. The class of pairwise grouping algorithms which share the shift-invariance property is statistically invariant under this embedding procedure, leading to identical assignments of objects to clusters. Based on this new vectorial representation, denoising methods are applied in a second step. Both steps provide a theoretically well controlled setup to translate from pairwise data to the respective denoised metric representation. We demonstrate the practical usefulness of our theoretical reasoning by discovering structure in protein sequence data bases, visibly improving performance upon existing automatic methods. 1 Introduction Unsupervised grouping or clustering aims at extracting hidden structure from data (see e.g.