<p dir="ltr">Vehicle weaving describes the lane-changing actions of vehicles, which is a critical aspect of traffic management and road design. This study focused on the weaving behavior of vehicles occurring between ramp merge and diverge areas. Weaving in these areas causes congestion and increases the risk of accidents, especially during heavy traffic. Redesigning such areas for enhanced safety requires a comprehensive analysis of the traffic conditions. Obtaining the weaving pattern is a challenge in the traffic industry. To address this challenge, we leveraged AI and image processing technology to develop algorithms for quantitative analysis of weaving using surveillance videos at the consecutive ramp merge and diverge areas. This approach can also determine the weaving patterns of passenger cars and trucks respectively. The experimental results captured the lane-based weaving behavior of around 30% of vehicles in the favorable areas. The captured weaving data is used as weaving data samples to derive an overall analysis of a weaving location. Remarkably, our approach can reduce the manual processing time for weaving analysis by more than 90%, making this highly practical for use.</p>

Axiom pinpointing has been introduced in description logics (DLs) to help the used understand the reasons why consequences hold by computing minimal subsets of the knowledge base that have the consequence in consideration. Several pinpointing algorithms have been described as extensions of the standard tableau-based reasoning algorithms for deciding consequences from DL knowledge bases. Although these extensions are based on similar ideas, they are all introduced for a particular tableau-based algorithm for a particular DL, using specific traits of them. In the past, we have developed a general approach for extending tableau-based algorithms into pinpointing algorithms. In this paper we explore some issues of termination of general tableaux and their pinpointing extensions. We also define a subclass of tableaux that allows the use of so-called blocking conditions, which stop the execution of the algorithm once a pattern is found, and adapt the pinpointing extensions accordingly, guaranteeing its correctness and termination.

In the area of Description Logic (DL) based knowledge representation, research on reasoning w.r.t. general terminologies has mainly focused on very expressive DLs. Recently, though, it was shown for the DL EL, providing only the constructors conjunction and existential restriction, that the subsumption problem w.r.t. cyclic terminologies can be decided in polynomial time, a surprisingly low upper bound. In this paper, we show that even admitting general concept inclusion (GCI) axioms and role hierarchies in EL terminologies preserves the polynomial time upper bound for subsumption. We also show that subsumption becomes co-NP hard when adding one of the constructors number restriction, disjunction, and `allsome', an operator used in the DL k-rep. An interesting implication of the first result is that reasoning over the widely used medical terminology snomed is possible in polynomial time.

The Description Logic EL is an inexpressive knowledge representation language, which nevertheless has recently drawn considerable attention in the knowledge representation and the ontology community since, on the one hand, important inference problems such as the subsumption problem are polynomial. On the other hand, EL is used to define large biomedical ontologies. Unification in Description Logics has been proposed as a novel inference service that can, for example, be used to detect redundancies in ontologies. In a recent paper, we have shown that unification in EL is NP-complete, and thus of a complexity that is considerably lower than in other Description Logics of comparably restricted expressive power. In this paper, we introduce a new NP-algorithm for solving unification problem in EL, which is based on a reduction to satisfiability in propositional logic (SAT). The advantage of this new algorithm is, on the one hand, that it allows us to employ highly optimized state of the art SAT solverswhen implementing an EL-unification algorithm. On the other hand, this reduction provides us with a proof of the fact that EL-unification is in NP that is much simpler than the one given in our previous paper on EL-unification.

El trabajo realizado en esta tesis pertenece al área de argumentación en inteligencia artificial. La representación de conocimiento en un formalismo basado en argumentación se realiza a través de la especificación de argumentos, cada uno en favor de una conclusión a partir de ciertas premisas. Dado que estas conclusiones pueden estar en contradicción, se producen ataques entre los argumentos. Luego, la evaluación de toda la información presente podría dar preponderancia a algunos argumentos por sobre aquellos que los contradicen, produciendo un conjunto de conclusiones que se considera ran garantizadas. El objetivo principal de esta tesis es la definición de un nuevo marco argumentativo capaz de manejar dinámica de conocimiento. En este sentido, se da una representación no sólo a los argumentos, sino que también se introduce la noción de evidencia como entidades especiales dentro del sistema. En cada instante, el conjunto de evidencia se corresponde con la situación actual, dándole contexto al marco argumentativo. La plausibilidad de los argumentos en un instante dado depende exclusivamente de la evidencia disponible. Cuando la evidencia es suficiente para dar soporte a un argumento, éste se denominará activo. También se considera la posibilidad de que algunos argumentos se encuentren activos aun sin encontrar soporte directamente desde la evidencia, ya que podrían hacerlo a través de las conclusiones de otros argumentos activos. Estas conexiones entre argumentos dan lugar a lo que en esta tesis se denomina estructura argumental, proveyendo una visión un tanto más compleja que la usual en cuanto a la representación de conocimiento argumentativo. Los resultados obtenidos en esta tesis permitirán estudiar la dinámica de conocimiento en sistemas argumentativos. En la actualidad, ya se han publicado artáculos que presentan un formalismo que combina argumentación y la teoría clásica de revisión de creencias. En esta línea de investigación se denen operadores de cambio que se aplican sobre el marco argumentativo abstracto dinámico y tienen como objetivo alcanzar cierto estado del sistema; por ejemplo, garantizar un argumento determinado. Por otra parte, este marco también permitirá estudiar métodos para acelerar el computo de garantía a partir del proceso de razonamiento realizado en estados anteriores.

yes / This paper aims to demystify the hype and attention on chatbots and its association with conversational artificial intelligence. Both are slowly emerging as a real presence in our lives from the impressive technological developments in machine learning, deep learning and natural language understanding solutions. However, what is under the hood, and how far and to what extent can chatbots/conversational artificial intelligence solutions work – is our question. Natural language is the most easily understood knowledge representation for people, but certainly not the best for computers because of its inherent ambiguous, complex and dynamic nature. We will critique the knowledge representation of heavy statistical chatbot solutions against linguistics alternatives. In order to react intelligently to the user, natural language solutions must critically consider other factors such as context, memory, intelligent understanding, previous experience, and personalized knowledge of the user. We will delve into the spectrum of conversational interfaces and focus on a strong artificial intelligence concept. This is explored via a text based conversational software agents with a deep strategic role to hold a conversation and enable the mechanisms need to plan, and to decide what to do next, and manage the dialogue to achieve a goal. To demonstrate this, a deep linguistically aware and knowledge aware text based conversational agent (LING-CSA) presents a proof-of-concept of a non-statistical conversational AI solution.

<p dir="ltr">Recent advancements in representation learning have significantly enhanced the analysis of relational data across various domains, including social networks, bioinformatics, and recommendation systems. In general, these methods assume that the training and test datasets come from the same distribution, an assumption that often fails in real-world scenarios due to evolving data, privacy constraints, and limited resources. The task of out-of-distribution (OOD) extrapolation emerges when the distribution of test data differs from that of the training data, presenting a significant, yet unresolved challenge within the field. This dissertation focuses on developing robust representations for effective OOD extrapolation, specifically targeting relational data types like graphs and sets. For successful OOD extrapolation, it's essential to first acquire a representation that is adequately expressive for tasks within the distribution. In the first work, we introduce Set Twister, a permutation-invariant set representation that generalizes and enhances the theoretical expressiveness of DeepSets, a simple and widely used permutation-invariant representation for set data, allowing it to capture higher-order dependencies. We showcase its implementation simplicity and computational efficiency, as well as its competitive performances with more complex state-of-the-art graph representations in several graph node classification tasks. Secondly, we address OOD scenarios in graph classification and link prediction tasks, particularly when faced with varying graph sizes. Under causal model assumptions, we derive approximately invariant graph representations that improve extrapolation in OOD graph classification task. Furthermore, we provide the first theoretical study of the capability of graph neural networks for inductive OOD link prediction and present a novel representation model that produces structural pairwise embeddings, maintaining predictive accuracy for OOD link prediction as the test graph size increases. Finally, we investigate the impact of environmental data as a confounder between input and target variables, proposing a novel approach utilizing an auxiliary dataset to mitigate distribution shifts. This comprehensive study not only advances our understanding of representation learning in OOD contexts but also highlights potential pathways for future research in enhancing model robustness across diverse applications.</p>

Modern autonomous systems should be able to interact with their surroundings in a flexible yet safe manner. To guarantee this behavior, such systems must learn how to approach unseen entities in their environment through the inference of relationships between actions and objects, called affordances. This research project introduces a neuro-symbolic AI system capable of inferring affordances using attribute detection and knowledge representation as its core principles. The attribute detection module employs a visuo-lingual image captioning model to extract the key object attributes of a scene, while the cognitive knowledge module infers the affordances of those attributes using conditional probability. The practical capabilities of the neuro-symbolic AI system are assessed by implementing a simulated robot system that interacts within the problem space of jars and bottles. The neuro-symbolic AI system is evaluated through its caption-inferring capabilities using image captioning and machine translation metrics. The scores registered in the evaluation show a successful attribute captioning rate of more than 71%. The robot simulation is evaluated within a Unity virtual environment by interacting with 50 jars and bottles, equally divided between lifting and twisting affordances. The robot system successfully interacts with all the objects in the scene due to the robustness of the architecture but fails in the inference process 24 out of the 50 iterations. Contrary to previous works approaching the problem as a classification task, this study shows that affordance inference can be successfully implemented using a cognitive visuo-lingual method. The study’s results justify further study into the use of neuro-symbolic AI approaches to affordance inference.

Description Logics form a family of powerful ontology languages widely used by academics and industry experts to capture and intelligently manage knowledge about the world. A key advantage of Description Logics is their amenability to automated reasoning that enables the deduction of knowledge that has not been explicitly stated. However, in order to ensure decidability of automated reasoning algorithms, suitable restrictions are usually enforced on the shape of structures that are expressible using Description Logics. As a consequence, Description Logics fall short of expressive power when it comes to representing cyclic structures, which abound in life sciences and other disciplines. The objective of this thesis is to explore ontology languages that are better suited for the representation of structured objects. It is suggested that an alternative approach which relies on nonmonotonic existential rules can provide a promising candidate for modelling such domains. To this end, we have built a comprehensive theoretical and practical framework for the representation of structured entities along with a surface syntax designed to allow the creation of ontological descriptions in an intuitive way. Our formalism is based on nonmonotonic existential rules and exhibits a favourable balance between expressive power and computational as well as empirical tractability. In order to ensure decidability of reasoning, we introduce a number of acyclicity criteria that strictly generalise many of the existing ones. We also present a novel stratification condition that properly extends `classical' stratification and allows for capturing both definitional and conditional aspects of complex structures. The applicability of our formalism is supported by a prototypical implementation, which is based on an off-the-shelf answer set solver and is tested over a realistic knowledge base. Our experimental results demonstrate improvement of up to three orders of magnitude in comparison with previous evaluation efforts and also expose numerous modelling errors of a manually curated biochemical knowledge base. Overall, we believe that our work lays the practical and theoretical foundations of an ontology language that is well-suited for the representation of structured objects. From a modelling point of view, our approach could stimulate the adoption of a different and expressive reasoning paradigm for which robustly engineered mature reasoners are available; it could thus pave the way for the representation of a broader spectrum of knowledge. At the same time, our theoretical contributions reveal useful insights into logic-based knowledge representation and reasoning. Therefore, our results should be of value to ontology engineers and knowledge representation researchers alike.

Logic-based formalizations of dynamical systems are central to the field of knowledge representation and reasoning. These formalizations can be used to model agents that act, reason,and perceive in a changing and incompletely known environment. A key aspect of reasoning about agents and their behaviors is the notion of joint ability. A team of agents is jointly able to achieve a goal if despite any incomplete knowledge or even false beliefs about the world or each other, they still know enough to be able to get to a goal state, should they choose to do so. A particularly challenging issue associated with joint ability is how team members can coordinate their actions. Existing approaches often require the agents to communicate to agree on a joint plan. In this thesis, we propose an account of joint ability that supports coordination among agents without requiring communication, and that allows for agents to have incomplete (or even false) beliefs about the world or the beliefs of other agents. We use ideas from game theory to address coordination among agents. We introduce the notion of a strategy for each agent which is basically a plan that the agent knows how to follow. Each agent compares her strategies and iteratively discards those that she believes are not good considering the strategies that the other agents have kept. Our account is developed in the situation calculus, a logical language suitable for representing and reasoning about action and change that is extended to support reasoning about multiple agents. Through several examples involving public, private, and sensing actions, we demonstrate how symbolic proof techniques allow us to reason about team ability despite incomplete specifications about the beliefs of agents.