This paper describes an architecture for an agent to learn and reason about affordances. In this architecture, Answer Set Prolog, a declarative language, is used to represent and reason with incomplete domain knowledge that includes a representation of affordances as relations defined jointly over objects and actions. Reinforcement learning and decision-tree induction based on this relational representation and observations of action outcomes are used to interactively and cumulatively (a) acquire knowledge of affordances of specific objects being operated upon by specific agents; and (b) generalize from these specific learned instances. The capabilities of this architecture are illustrated and evaluated in two simulated domains, a variant of the classic Blocks World domain, and a robot assisting humans in an office environment.

A Decision Tree follows a tree-like structure (hence the name) whereby a node represents a specific attribute, a branch represents a decision rule, and leaf nodes represent an outcome. We will show this structure later so you can see what we mean but you can imagine it is like one of the decision trees you used to draw in high school maths, just on a far more complicated scale. The algorithm itself works by splitting the data according to different attributes at each node while attempting to reduce a selection measure (often the Gini index). In essence, the aim of a Decision Tree classifier is to split the data according to attributes while being able to classify the data accurately into predefined groups (our target variable). For this decision tree implementation we will use the iris dataset from sklearn which is relatively simple to understand and is easy to implement.

With the increasing demand for personalization in clinical decision support system, one of the most challenging tasks is effective patient preferences elicitation. In the context of the MobiGuide project, within a medical application related to atrial fibrillation, a decision support system has been developed for both doctors and patients. In particular, we support shared decision-making, by integrating decision tree models with a dedicated tool for utility coefficients elicitation. In this paper we focus on the decision problem regarding the choice of anticoagulant therapy for low risk non-valvular atrial fibrillation patients. In addition to the traditional methods, such as time trade-off and standard gamble, an alternative way for preferences elicitation is proposed, exploiting patients' self-reported data in health-related social media as the main source of information.

Online social media platforms generally attempt to mitigate hateful expressions, as these comments can be detrimental to the health of the community. However, automatically identifying hateful comments can be challenging. We manually label 5,143 hateful expressions posted to YouTube and Facebook videos among a dataset of 137,098 comments from an online news media. We then create a granular taxonomy of different types and targets of online hate and train machine learning models to automatically detect and classify the hateful comments in the full dataset. Our contribution is twofold: 1) creating a granular taxonomy for hateful online comments that includes both types and targets of hateful comments, and 2) experimenting with machine learning, including Logistic Regression, Decision Tree, Random Forest, Adaboost, and Linear SVM, to generate a multiclass, multilabel classification model that automatically detects and categorizes hateful comments in the context of online news media. We find that the best performing model is Linear SVM, with an average F1 score of 0.79 using TF-IDF features. We validate the model by testing its predictive ability, and, relatedly, provide insights on distinct types of hate speech taking place on social media.

Now, that we know the basic building blocks of a decision tree, we need to know how to grow one. Creating a decision tree describes the process of dividing the input space into several distinct, non-overlapping sub-spaces. In order to divide the input space, we have to test all features and threshold values to find the optimal split that minimizes our cost function. Once we obtain the best split, we can continue to grow our tree recursively. The process is termed recursive since each sub-space may be split an indefinite number of times until a stopping criterion (e.g.

In this tutorial, we will explore one of the most rampantly used and fundamental machine learning models, decision tree (DT). A decision tree is a very powerful model which can help us to classify labeled data and make predictions. It also enlightens us with lots of information about the data and most importantly, it's effortlessly easy to interpret. If you are a software engineer, you would probably know "If-else" conditions, and we all love it because it's very simple to understand, imagine, and code. A decision tree can be thought of as nothing but a "nested if-else classifier."

Decision trees are pretty powerful algorithms. They are, probably, one of the first non-linear algorithms that you may learn when you get into Data Science and Machine Learning. Decision Trees can work around non-linear patterns and make sense of some of the wildest relationships between target and features. In that sense, they have a huge advantage over linear models such as linear or logistic regressions. While they have this huge ability of capturing more complex relationships of features and target, they are also really prone to overfit.

Uplift modeling is crucial in various applications ranging from marketing and policy-making to personalized recommendations. The main objective is to learn optimal treatment allocations for a heterogeneous population. A primary line of existing work modifies the loss function of the decision tree algorithm to identify cohorts with heterogeneous treatment effects. Another line of work estimates the individual treatment effects separately for the treatment group and the control group using off-the-shelf supervised learning algorithms. The former approach that directly models the heterogeneous treatment effect is known to outperform the latter in practice. However, the existing tree-based methods are mostly limited to a single treatment and a single control use case, except for a handful of extensions to multiple discrete treatments. In this paper, we fill this gap in the literature by proposing a generalization to the tree-based approaches to tackle multiple discrete and continuous-valued treatments. We focus on a generalization of the well-known causal tree algorithm due to its desirable statistical properties, but our generalization technique can be applied to other tree-based approaches as well. We perform extensive experiments to showcase the efficacy of our method when compared to other methods.

Tree-based methods are simple and useful for interpretation since the underlying mechanisms are considered quite similar to human decision-making. The methods involve stratifying or segmenting the predictor space into a number of simpler regions. When making a prediction, we simply use the mean or mode of the region the new observation belongs to as a response value. Since the splitting rules to segment the predictor space can be best described by a tree-based structure, the supervised learning algorithm is called a Decision Tree. Decision trees can be used for both regression and classification tasks.

Learning arguments is highly relevant to the field of explainable artificial intelligence. It is a family of symbolic machine learning techniques that is particularly human-interpretable. These techniques learn a set of arguments as an intermediate representation. Arguments are small rules with exceptions that can be chained to larger arguments for making predictions or decisions. We investigate the learning of arguments, specifically the learning of arguments from a 'case model' proposed by Verheij [34]. The case model in Verheij's approach are cases or scenarios in a legal setting. The number of cases in a case model are relatively low. Here, we investigate whether Verheij's approach can be used for learning arguments from other types of data sets with a much larger number of instances. We compare the learning of arguments from a case model with the HeRO algorithm [15] and learning a decision tree.