The central component in the design of our artificial agent behavior generation system, la VIDA , is its drive mechanism. This drive, which we have named an identity profile , is a collection of roles, values, beliefs, and attitudes analogous to and inspired by the human identity. This identity profile acts as an intrinsic source of motivation to drive long-term, autonomous agent behavior. In this paper we explore the representation and function of an early design for the identity profile. We also discuss how the identity profile interacts with a personality inventory and commonsense knowledge graph to assign traits and associated strengths to the agent. These values can then be mapped to three behavior intensities to augment agent actions.
Acquiring knowledge from data and reasoning with the obtained knowledge are both essential processes of successful logical systems. However, most current logical systems assume different algorithms for the two processes. The separation causes serious problems such as knowledge acquisition bottleneck, grounding and commonsense reasoning. This paper gives a simple probabilistic model unifying the two processes. It formalises how data generate models of formal logic and the models generate the truth values of logical formulae. The generated models and truth values are shown to be consistent with maximum likelihood estimation and Fenstad’s theorem, respectively. Probabilistic reasoning on logical formulae is shown to be a reasonable alternative to a logical consequence relation and a paraconsistent consequence relation. This paper contributes to data-based reasoning with linear complexity.
Dementia is a term used to describe a collection of neurodegenerative disorders that impact people across the world. The symptoms of the disease progression include recent memory loss and impairment of cognitive functions that create challenges to individuals living with dementia performing daily tasks, making them dependent on caregivers. Due to social and economic matters, caregivers can be either professional or informal, and in some cases, they are members of the patient’s family. Investigations suggest that caring for individuals living with neurodegenerative disease is stressful physically, mentally, and emotionally. This study aims to suggest a model of an artificial cognitive system to assist caregivers in dementia care. The methodology applied was design science research (DSR). We have no knowledge of any work that attempted to implement a similar approach, which corroborates with the belief that this is still a cutting-edge technology and novel interdisciplinary area. Our proof-of-concept tried involving artificial cognitive aspects to assistance. It handled the data of the patient to provide information from reliable sources to non-professional caregiver’s decision-making regardless of the disease stage. However, it is in a nascent state of development and requires further research to be applied in real scenarios.
Nature has been a great source of inspiration for many inventions and theories. One of the major benefits for this inspiration is perceiving the impossible as possible. The inception of the AI field was no exception with cognitively-inspired approaches with a dream of having an intelligent system that thinks as a human. However, this journey of human intelligence into machine intelligence has been rough and more challenging that resulted in the separation of AI from cognitive studies. In this article, we highlight the main challenges and opportunities for cognitive inspiration for AI development. We then break down the source of inspiration into four abstraction levels in which the researcher may place an inspiration from. These levels then contribute into three main stages for modeling the AI system. The two dimensional mapping from cognitive levels into modeling stages and the relation between them aims to assist the process of cognitively-inspired approaches.
Service robots are expected to reliably make sense of complex, fast-changing environments. From a cognitive standpoint, they need the appropriate reasoning capabilities and background knowledge required to exhibit human-like Visual Intelligence. In particular, our prior work has shown that i) commonsense reasoning is a necessary capability for Visual Intelligence and also that ii) commonsense reasoning crucially requires the ability to reason about spatial relations between objects in the world. In this paper, we first recap our approach to Visual Intelligence in robotics, which is based on a hybrid architecture integrating a deep learning component with commonsense reasoning. We then present a framework for spatial reasoning, which has been designed to support the commonsense reasoning component in our architecture. Differently from prior approaches to qualitative spatial reasoning in robotics, the proposed framework is robust to variations in the robot’s viewpoint and object orientation. In the paper, we also show how this formally-defined framework can be operationalised in an off-the-shelf spatial database.
