The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments

In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build articial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges

Episodic Reasoning for Vision-Based Human Action Recognition

Smart Spaces, Ambient Intelligence, and Ambient Assisted Living are environmental paradigms that strongly depend on their capability to recognize human actions. While most solutions rest on sensor value interpretations and video analysis applications, few have realized the importance of incorporating common-sense capabilities to support the recognition process. Unfortunately, human action recognition cannot be successfully accomplished by only analyzing body postures. On the contrary, this task should be supported by profound knowledge of human agency nature and its tight connection to the reasons and motivations that explain it. The combination of this knowledge and the knowledge about how the world works is essential for recognizing and understanding human actions without committing common-senseless mistakes. This work demonstrates the impact that episodic reasoning has in improving the accuracy of a computer vision system for human action recognition. This work also presents formalization, implementation, and evaluation details of the knowledge model that supports the episodic reasoning

OntoPhoto and the Role of Ontology in Organizing Knowledge 79

ABSTRACT: This article is concerned with ontology and its applications in Knowledge Organization (KO) activities. Connections are drawn between efforts in artificial intelligence (AI) to capture the meaning of information and make it accessible to machines and the efforts made in libraries to use KO tools in machine-based record building and search and retrieval systems. The practices used in AI that are of interest here include ontology and ontology-based knowledge representation. In this article their applications in KO are directed towards a particularly problematic document type-the photograph. There are two arguments motivating this article. First, ontology-based KO systems that join AI techniques with library cataloging practices make it possible to utilize higher levels of expressivity when describing photographs. Second, KO systems for photographs that are capable of reasoning over concepts and relationships can potentially provide richer, more relevant search results than systems utilizing word-matching alone. Introduction The goal of this article is to draw connections between efforts in artificial intelligence (AI) to capture the meaning of information and make it accessible to machines and the efforts made in libraries and archives to use Knowledge Organization (KO) tools in machine-based record building and search and retrieval systems. The practices used in AI that are of interest here include ontology and ontology-based knowledge representations. In this article, applications of ontology and knowledge representation in libraries and archives are directed towards a particularly problematic document type, the photograph. There are two arguments motivating this article. The first is that ontology-based KO systems that join AI techniques with library cataloging practices make it possible to utilize higher levels of expressivity when describing photographs. The second argument is that KO systems for photographs that are capable of reasoning over concepts and relationships can potentially provide richer, more relevant search results than systems designed for word-matching alone. I begin by describing some of the problems associated with the traditional models used for representing photographs in library information systems. Following this, I describe the key terms and concepts necessary for understanding the topics introduced in this article-terms commonly found in AI literature, but less often in library-and-information science (LIS) literature. I give particular attention to the notions of ontology, knowledge representation, reasoning, and the distinction made between words and the concepts they represent. Describing an ontology of the photo

Hierarchical Task Network Planning with Common-Sense Reasoning for Multiple-People Behaviour Analysis

Safety on public transport is a major concern for the relevant authorities. We address this issue by proposing an automated surveillance platform which combines data from video, infrared and pressure sensors. Data homogenisation and integration is achieved by a distributed architecture based on communication middleware that resolves interconnection issues, thereby enabling data modelling. A common-sense knowledge base models and encodes knowledge about public-transport platforms and the actions and activities of passengers. Trajectory data from passengers is modelled as a time-series of human activities. Common-sense knowledge and rules are then applied to detect inconsistencies or errors in the data interpretation. Lastly, the rationality that characterises human behaviour is also captured here through a bottom-up Hierarchical Task Network planner that, along with common-sense, corrects misinterpretations to explain passenger behaviour. The system is validated using a simulated bus saloon scenario as a case-study. Eighteen video sequences were recorded with up to six passengers. Four metrics were used to evaluate performance. The system, with an accuracy greater than 90% for each of the four metrics, was found to outperform a rule-base system and a system containing planning alone

Design and implementation of a system for mutual knowledge among cognition-enabled robots

The progressive integration of robots in everyday activities is raising the need for autonomous machines to reason about their actions, the environment and the objects around them. The KnowRob knowledge processing system is specifically designed to bring these competences to autonomous robots, helping them to acquire, reason about and store knowledge. This work presents a framework for enhancing the KnowRob system with mutual knowledge acquisition and reasoning among knowledge-enabled robot

Verbal IQ of a Four-Year Old Achieved by an AI System

Abstract One view of common-sense reasoning ability is that it is the ability to perform those tasks with verbal inputs and outputs that have traditionally been difficult for computer systems, but are easy for fairly young children. We administered the verbal part of the Wechsler Preschool and Primary Scale of Intelligence (WPPSI-III, Third Edition) to the ConceptNet 4 system. The IQ test's questions (e.g., "Why do we shake hands?" or "What do apples and bananas have in common") were translated into ConceptNet 4 inputs using a combination of the simple natural language processing tools that come with ConceptNet together with short Python programs that we wrote. The question-answering primarily used the part of the ConceptNet system that represents the knowledge as a matrix based on spectral methods (AnalogySpace). We found that the system has a Verbal IQ that is average for a four-year-old child, but below average for 5, 6, and 7 yearolds. Large variations from subtest to subtest indicate potential areas of improvement. In particular, results were strongest for the Vocabulary and Similarities subtests, intermediate for the Information subtest, and lowest for the Comprehension and Word Reasoning subtests. Comprehension is the subtest most strongly associated with common sense. Children's verbal IQ tests offer a new, objective, third-party metric for the evaluation and comparison of common-sense AI systems

RELATIONSHIP ANALYSIS OF IMAGE DESCRIPTIONS: AN ONTOLOGICAL, CONTENT ANALYTIC APPROACH

The relationships humans express when describing images have powerful, but poorly understood, effects on how visual information is represented, structured, and processed in information systems. This study evaluates the benefits and difficulties of using content analysis and ontological analysis as predictors of relationship instances and types occurring in image descriptions. A random sample of 36 documented reference transactions from the administrative files of the Pittsburgh Photographic Library is analyzed in light of three describing contexts: image searcher, curator, and cataloger. Through the qualitative and quantitative assessment of image descriptions, the research leads to several key findings and contributions. The most important findings vindicate the claim that recognition, capture, and classification of relationship instances can be empirically grounded utilizing content analysis and ontological tools and methods. Evidence comes in successfully ascertaining and capturing in a Corpus the existence of 1,655 relationship instances. Further, the analysis finds evidence of relationship types and subtypes of relationships whose members share certain recognizable properties in common. The study also brings useful, new insights to the capture of background information surrounding events using situation-templates, introduces methods for formulating case relations and image attributes as binary predicates, and it offers a new, finer-grained definition of relationship. Contributions of this study include a corpus of relationship instances, an ontology of relationship types, and a methodological framework that provides significantly better results than earlier studies in the prediction of relationships (the architecture of which is depicted in Figure 22 on page 102). There are a number of ways this research could be extended and corroborated. First, event analysis ought to be tied to a system of semantic frame analysis. Second, test the content analysis form against other texts, which will result in elaboration of the core ontology of relationship types. Third, expand image description analysis beyond the current domain to include image description in visual ethnography, art history and criticism, and photography practices. Fourth, test how inference engines reason over relationships in knowledge-based environments. Finally, to aid in the analysis of the meanings of relationships, more work is needed in formalizing the ontological concepts used in image descriptions