Randy Goebel

• Ph.D. The University of British Columbia
• Professor (Full) at University of Alberta

Motion planning is a fundamental problem in autonomous driving and perhaps the most challenging to comprehensively evaluate because of the associated risks and expenses of real-world deployment. Therefore, simulations play an important role in efficient development of planning algorithms. To be effective, simulations must be accurate and realistic,...

Reasoning is an essential component of human intelligence as it plays a fundamental role in our ability to think critically, support responsible decisions, and solve challenging problems. Traditionally, AI has addressed reasoning in the context of logic-based representations of knowledge. However, the recent leap forward in natural language process...

Developing the capacity to effectively search for requisite datasets is an urgent requirement to assist data users in identifying relevant datasets considering the very limited available metadata. For this challenge, the utilization of third-party data is emerging as a valuable source for improvement. Our research introduces a new architecture for...

Business reliance on algorithms is becoming ubiquitous, and companies are increasingly concerned about their algorithms causing major financial or reputational damage. High-profile cases include Google’s AI algorithm for photo classification mistakenly labelling a black couple as gorillas in 2015 (Gebru 2020 In The Oxford handbook of ethics of AI,...

A fundamental component of user-level social media language based clinical depression modelling is depression symptoms detection (DSD). Unfortunately, there does not exist any DSD dataset that reflects both the clinical insights and the distribution of depression symptoms from the samples of self-disclosed depressed population. In our work, we desc...

From connected sensors in soils, on animals or crops, and on drones, to various software and services that are available, “smart” technologies are changing the way farming is carried out. These technologies allow producers to look beyond what the eye can see by collecting non-traditional data and then using analytics tools to improve both food sust...

We summarize the 10th Competition on Legal Information Extraction and Entailment. In this tenth edition, the competition included four tasks on case law and statute law. The case law component includes an information retrieval task (Task 1), and the confirmation of an entailment relation between an existing case and a selected unseen case (Task 2)....

The challenge of information overload in the legal domain increases every day. The COLIEE competition has created four challenge tasks that are intended to encourage the development of systems and methods to alleviate some of that pressure: a case law retrieval (Task 1) and entailment (Task 2), and a statute law retrieval (Task 3) and entailment (T...

Autonomous driving has achieved significant milestones in research and development over the last two decades. There is increasing interest in the field as the deployment of autonomous vehicles (AVs) promises safer and more ecologically friendly transportation systems. With the rapid progress in computationally powerful artificial intelligence (AI)...

We present LawGiBa, a proof-of-concept demonstration system for legal assistance that combines GPT, legal knowledge bases, and Prolog’s logic programming structure to provide explanations for legal queries. This novel combination effectively and feasibly addresses the hallucination issue of large language models (LLMs) in critical domains, such as...

Digital technology applications in agriculture and biology are a dynamic area of research interest, with topics including, but not limited to, agriculture, data collection, data mining, bioinformatics, genomics and phenomics, as well as applications of machine learning and artificial intelligence [...]

Interpretation methods for learned models used in natural language processing (NLP) applications usually provide support for local (specific) explanations, such as quantifying the contribution of each word to the predicted class. But they typically ignore the potential interaction amongst those word tokens. Unlike currently popular methods, we prop...

The end-to-end learning ability of self-driving vehicles has achieved significant milestones over the last decade owing to rapid advances in deep learning and computer vision algorithms. However, as autonomous driving technology is a safety-critical application of artificial intelligence (AI), road accidents and established regulatory principles ne...

Negation is a fundamental aspect of natural language, playing a critical role in communication and comprehension. Our study assesses the negation detection performance of Generative Pre-trained Transformer (GPT) models, specifically GPT-2, GPT-3, GPT-3.5, and GPT-4. We focus on the identification of negation in natural language using a zero-shot pr...

The popular Proximal Policy Optimization (PPO) algorithm approximates the solution in a clipped policy space. Does there exist better policies outside of this space? By using a novel surrogate objective that employs the sigmoid function (which provides an interesting way of exploration), we found that the answer is "YES", and the better policies ar...

Advances in optical sensing technology have led to new approaches to monitoring and determining crop seed vigor. In order to improve crop performance to secure reliable yield and food supply, calibrating seed vigor, purity, germination rate, and clarity is very critical to the future of the agriculture/horticulture industry. Traditional methods of...

We examine how well the state-of-the-art (SOTA) models used in legal reasoning support abductive reasoning tasks. Abductive reasoning is a form of logical inference in which a hypothesis is formulated from a set of observations, and that hypothesis is used to explain the observations. The ability to formulate such hypotheses is important for lawyer...

We present a summary of the 9th Competition on Legal Information Extraction and Entailment (COLIEE 2022). The competition consists of four tasks on case law and statute law. The case law component includes an information retrieval task (Task 1), and the confirmation of an entailment relation between an existing case and an unseen case (Task 2). The...

The challenge of information overload in the legal domain increases every day. The COLIEE competition has created four challenges which are intended to encourage the development of systems and methods to alleviate some of that pressure: a case law retrieval (Task 1) and entailment (Task 2), and a statute law retrieval (Task 3) and entailment (Task...

Interpretability is becoming an expected and even essential characteristic in GDPR Europe. In the majority of existing work on natural language processing (NLP), interpretability has focused on the problem of explanatory responses to questions like “Why p?” (identifying the causal attributes that support the prediction of "p.)” This type of local e...

We present an empirical analysis of basic and depression specific multi-emotion mining in Tweets, using state of the art multi-label classifiers. We choose our basic emotions from a hybrid emotion model consisting of the commonly identified emotions from four highly regarded psychological models. Moreover, we augment that emotion model with new emo...

Driving SMARTS is a regular competition designed to tackle problems caused by the distribution shift in dynamic interaction contexts that are prevalent in real- world autonomous driving (AD). The proposed competition supports methodologically diverse solutions, such as reinforcement learning (RL) and offline learning methods, trained on a combinati...

We describe the development of a model to detect user-level clinical depression based on a user's temporal social media posts. Our model uses a Depression Symptoms Detection (DSD) model, which is trained on the largest existing samples of clinician annotated tweets for clinical depression symptoms. We subsequently use our DSD model to extract clini...

Given a graph G=(V,E)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G = (V, E)$$\end{document}, the 3-path partition problem is to find a minimum collection of vertex-...

A fundamental component of user-level social media language based clinical depression modelling is depression symptoms detection (DSD). Unfortunately, there does not exist any DSD dataset that reflects both the clinical insights and the distribution of depression symptoms from the samples of self-disclosed depressed population. In our work, we desc...

Multiprocessor scheduling, also called scheduling on parallel identical machines to minimize the makespan, is a classic optimization problem which has been extensively studied. Scheduling with testing is an online variant, where the processing time of a job is revealed by an extra test operation, otherwise the job has to be executed for a given upp...

The success of statistical machine learning from big data, especially of deep learning, has made artificial intelligence (AI) very popular. Unfortunately, especially with the most successful methods, the results are very difficult to comprehend by human experts. The application of AI in areas that impact human life (e.g., agriculture, climate, fore...

Path cover is a well-known intractable problem that finds a minimum number of vertex disjoint paths in a given graph to cover all the vertices. We show that a variant, in which the objective is to minimize the number of length-0 paths, is polynomial-time solvable. We further show that another variant, to minimize the total number of length-0 and le...

We summarize the 8th Competition on Legal Information Extraction and Entailment. In this edition, the competition included five tasks on case law and statute law. The case law component includes an information retrieval Task (Task 1), and the confirmation of an entailment relation between an existing case and an unseen case (Task 2). The statute la...

We describe the techniques applied by the University of Alberta (UA) team in the most recent Competition on Legal Information Extraction and Entailment (COLIEE 2021). We participated in retrieval and entailment tasks for both case law and statute law; we applied a transformer-based approach for the case law entailment task, an information retrieval...

Autonomous driving has achieved a significant milestone in research and development over the last decade. There is increasing interest in the field as the deployment of self-operating vehicles on roads promises safer and more ecologically friendly transportation systems. With the rise of computationally powerful artificial intelligence (AI) techniq...

There has been growing interest in the development and deployment of autonomous vehicles on modern road networks over the last few years, encouraged by the empirical successes of powerful artificial intelligence approaches (AI), especially in the applications of deep and reinforcement learning. However, there have been several road accidents with `...

The rapid growth of research in explainable artificial intelligence (XAI) follows on two substantial developments. First, the enormous application success of modern machine learning methods, especially deep and reinforcement learning, have created high expectations for industrial, commercial, and social value. Second, the emerging and growing conce...

We present a summary of the 7th Competition on Legal Information Extraction and Entailment. The competition consists of four tasks on case law and statute law. The case law component includes an information retrieval task (Task 1), and the confirmation of an entailment relation between an existing case and an unseen case (Task 2). The statute law c...

We develop a method to identify entailment relationships in the texts of case law documents, in the context of two tasks of the Competition on Legal Information Extraction and Entailment (COLIEE 2020). The first task consists in, given a 1) base case, 2) a text fragment from that base case, and 3) the list of paragraphs from a noticed case, identif...

We analyze the process of creating word embedding feature representations designed for a learning task when annotated data is scarce, for example, in depressive language detection from Tweets. We start with a rich word embedding pre-trained from a large general dataset, which is then augmented with embeddings learned from a much smaller and more sp...

We focus on exploring various approaches of Zero-Shot Learning (ZSL) and their explainability for a challenging yet important supervised learning task notorious for training data scarcity, i.e. Depression Symptoms Detection (DSD) from text. We start with a comprehensive synthesis of different components of our ZSL modeling and analysis of our groun...

In this paper, we present empirical analysis on basic and depression specific multi-emotion mining in Tweets with the help of state of the art multi-label classifiers. We choose our basic emotions from a hybrid emotion model consisting of the common emotions from four highly regarded psychological models of emotions. Moreover, we augment that emoti...

We follow up an earlier studied multiple-task parallel-machine scheduling model that captures the core challenges in MapReduce scheduling, with the optimization goal to minimize the total job completion time. The problem is a novel generalization of the classic two-stage flow-shop scheduling, in which we are given a set of jobs each is associated w...

We summarize the evaluation of the 6th Competition on Legal Information Extraction/Entailment (COLIEE 2019). The competition consists of four tasks: two on case law and two on statute law. The case law component includes an information retrieval task (Task 1), and the confirmation of an entailment relation between an existing case and an unseen cas...

The development of theory, frameworks and tools for Explainable AI (XAI) is a very active area of research these days, and articulating any kind of coherence on a vision and challenges is itself a challenge. At least two sometimes complementary and colliding threads have emerged. The first focuses on the development of pragmatic tools for increasin...

We discuss and analyze the process of creating word embedding feature representations specifically designed for a learning task when annotated data is scarce, like depressive language detection from Tweets. We start from rich word embedding pre-trained from a general dataset, then enhance it with embedding learned from a domain specific but relativ...

Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for t...

We summarize the evaluation of the 5th Competition on Legal Information Extraction/Entailment 2018 (COLIEE-2018). The COLIEE-2018 tasks include two tasks in each of statute law and case law. The case law component includes an information retrieval (Task 1), and the confirmation of an entailment relation between an existing case and an unseen case (...

We study open-shop scheduling for unit jobs under precedence constraints, where if one job precedes another job then it has to be finished before the other job can start to be processed. For the three-machine open-shop to minimize the makespan, we first present a simple 5/3-approximation algorithm based on a partition of the job set into agreeable...

We investigate the maximum happy vertices (MHV) problem and its complement, the minimum unhappy vertices (MUHV) problem. In order to design better approximation algorithms, we introduce the supermodular and submodular multi-labeling (Sup-ML and Sub-ML) problems and show that MHV and MUHV are special cases of Sup-ML and Sub-ML, respectively, by rewr...

We propose a Multi-task learning approach for Abstractive Text Summarization (MATS), motivated by the fact that humans have no difficulty performing such task because they have the capabilities of multiple domains. Specifically, MATS consists of three components: (i) a text categorization model that learns rich category-specific text representation...

Given a graph G=(V,E), we seek for a collection of vertex disjoint paths each of order at most 3 that together cover all the vertices of V. The problem is called 3-path partition, and it has close relationships to the well-known path cover problem and the set cover problem. The general k-path partition problem for a constant k≥3 is NP-hard, and it...

A mixed shop is a manufacturing infrastructure designed to process a mixture of a set of flow-shop jobs and a set of open-shop jobs. Mixed shops are in general much more complex to schedule than flow-shops and open-shops, and have been studied since the 1980's. We consider the three machine proportionate mixed shop problem denoted as M3|prpt|Cmax,...

We tackle the complex problem of determining entailment relationships between case law documents, one of the tasks in the Competition on Legal Information Extraction and Entailment (COLIEE). With input of an entailed fragment from a case coupled with a candidate entailing paragraph from a noticed case, our approach relies on four main components: (...

Our Yes/No statute law question answering system combines components for both statute law information retrieval and confirmation of textual entailment between statues and legal questions. We describe a statute law question answering system that exploits TF-IDF and a language model for information retrieval, and inter-paragraph entailment. We have e...

Professor Koichi Furukawa, an eminent computer scientist and former Editor-in-Chief of the New Generation Computing journal, passed away on January 31, 2017. His passing was a surprise, and we were all shocked and saddened by the news. To remember the deceased, this article reviews the great career and contributions of Professor Koichi Furukawa, fo...

Given a graph \(G = (V, E)\), the 3-path partition problem is to find a minimum collection of vertex-disjoint paths each of order at most 3 to cover all the vertices of V. It is different from but closely related to the well-known 3-set cover problem. The best known approximation algorithm for the 3-path partition problem was proposed recently and...

Given a graph $G = (V, E)$, the $3$-path partition problem is to find a minimum collection of vertex-disjoint paths each of order at most $3$ to cover all the vertices of $V$. It is different from but closely related to the well-known $3$-set cover problem. The best known approximation algorithm for the $3$-path partition problem was proposed recen...

Duplicate question detection is an ongoing challenge in community question answering because semantically equivalent questions can have significantly different words and structures. In addition, the identification of duplicate questions can reduce the resources required for retrieval, when the same questions are not repeated. This study compares th...

We investigate a single machine rescheduling problem that arises from an unexpected machine unavailability, after the given set of jobs has already been scheduled to minimize the total weighted completion time. Such a disruption is represented as an unavailable time interval and is revealed to the production planner before any job is processed; the...

A mixed shop is a manufacturing infrastructure designed to process a mixture of a set of flow-shop jobs and a set of open-shop jobs. Mixed shops are in general much more complex to schedule than flow-shops and open-shops, and have been studied since the 1980's. We consider the three machine proportionate mixed shop problem denoted as $M3 \mid prpt...

Explainable AI is not a new field. Since at least the early exploitation of C.S. Pierce’s abductive reasoning in expert systems of the 1980s, there were reasoning architectures to support an explanation function for complex AI systems, including applications in medical diagnosis, complex multi-component design, and reasoning about the real world. S...

Path cover is a well-known intractable problem whose goal is to find a minimum number of vertex disjoint paths in a given graph to cover all the vertices. We show that a variant, where the objective function is not the number of paths but the number of length-0 paths (that is, isolated vertices), turns out to be polynomial-time solvable. We further...

Every day a large volume of legal documents are produced, and lawyers need support for their analysis, especially in corporate litigation. Typically, corporate litigation has the aim of finding evidence for or against the litigation claims. Identifying the critical legal points within large volumes of legal text is time consuming and costly, but re...

Path cover is a well-known intractable problem that finds a minimum number of vertex disjoint paths in a given graph to cover all the vertices. We show that a variant, where the objective function is not the number of paths but the number of length-$0$ paths (that is, isolated vertices), turns out to be polynomial-time solvable. We further show tha...

Introduction Rising costs and the rapidly increasing volume of findings from research in health care are driving the demand for comprehensive information to inform the allocation of resources. Health technology assessment (HTA) applies rigorous processes to provide high-quality synthesized information to policymakers and healthcare payers. HTA invo...

The practical impact of deep learning on complex supervised learning problems has been significant, so much so that almost every Artificial Intelligence problem, or at least a portion thereof, has been somehow recast as a deep learning problem. The applications appeal is significant, but this appeal is increasingly challenged by what some call the...

We present a method for explaining the image classification predictions of deep convolution neural networks, by highlighting the pixels in the image which influence the final class prediction. Our method requires the identification of a heuristic method to select parameters hypothesized to be most relevant in this prediction, and here we use Kullba...

This Volume is a result of workshop 15w2181 “Advances in interactive knowledge discovery and data mining in complex and big data sets” at the Banff International Research Station for Mathematical Innovation and Discovery. The workshop was dedicated to bring together experts with diverse backgrounds but with one common goal: to understand intelligen...

We consider a communication scheduling problem that arises within wireless sensor networks, where data is accumulated by the sensors and transferred directly to a central base station. One may choose to compress the data collected by a sensor, to decrease the data size for transmission, but the cost of compression must be considered. The goal is to...

In the parallel k-stage flow-shops problem, we are given m identical k-stage flow-shops and a set of jobs. Each job can be processed by any one of the flow-shops but switching between flow-shops is not allowed. The objective is to minimize the makespan, which is the finishing time of the last job. This problem generalizes the classical parallel ide...

Our legal question answering system combines legal information retrieval and textual entailment, and exploits paraphrasing and sentence-level analysis of queries and legal statutes. We have evaluated our system using the training data from the competition on legal information extraction/entailment (COLIEE)-2016. The competition focuses on the legal...

Our legal question answering system combines legal information retrieval and textual entailment, and exploits semantic information using a logic-based representation. We have evaluated our system using the data from the competition on legal information extraction/entailment (COLIEE)-2017. The competition focuses on the legal information processing...

Our legal question answering system combines legal information retrieval and textual entailment, and we describe a legal question answering system that exploits a deep convolutional neural network. We have evaluated our system using the training/test data from the competition on legal information extraction/entailment (COLIEE). The competition focu...

We investigate a single machine rescheduling problem that arises from an unexpected machine unavailability, after the given set of jobs has already been scheduled to minimize the total weighted completion time. Such a disruption is represented as an unavailable time interval and is revealed to the production planner before any job is processed; the...

The BIRS Workshop “Advances in interactive Knowledge Discovery and Data Mining in complex and big data sets” (15w2181) in July 2015 in Banff, Canada, was dedicated to stimulate a cross-domain integrative machine learning approach and appraisal of “hot topics” towards tackling the grand challenge of reaching a level of useful and useable computation...

We present the evaluation of legal question answering of the Competition on Legal Information Extraction/Entailment (COLIEE)-2016. The COLIEE-2016 task consists of three sub-tasks: legal information retrieval (subtask 1), recognizing entailment between articles and queries (sub-task 2), and combination of the previous two sub-tasks (sub-task 3). Pa...

In the parallel k-stage flow-shops problem, we are given m identical k-stage flow-shops and a set of jobs. Each job can be processed by any one of the flow-shops but switching between flow-shops is not allowed. The objective is to minimize the makespan, which is the finishing time of the last job. This problem generalizes the classical parallel ide...

In this paper, we investigate the submodular multi-labeling (Sub-ML) problem, a more general version of the submodular multiway partition (Sub-MP), which captures many cut problems as special cases, including the edge-/node-weighted multiway cut and the hypergraph multiway cut. We also study the complement of Sub-ML, the supermodular multi-labeling...

We propose a new open question answering framework for question answering over a knowledge base (KB). Our system uses both a curated KB, Freebase, and one that is extracted automatically by an open information extraction model, IE KB. Our system consists of only one layer of paraphrase, compared to the three layers used in a previous open question...

We present the details on evaluation of legal question answering of the Competition on Legal Information Extraction/Entailment (COLIEE)-2015. The COLIEE-2015 task consists of three sub-tasks: legal information retrieval (sub-task 1), recognizing entailment between articles and queries (sub-task 2), and combination of the previous two sub-tasks (sub...

We investigate a scheduling problem with job delivery coordination in which the machine has a maintenance time interval. The goal is to minimize the makespan. In the problem, each job needs to be processed on the machine non-preemptively for a certain time, and then transported to a distribution center; transportation is by one vehicle with a limit...