Deepak Khemani

This paper applies and extends the results from [22] on agent-update frames and their logic. Several interesting examples of actions for forgery and deception, agent-upgrade and agent-downgrade are considered. Going on from the earlier paper, a second interesting children’s story is modelled using these ideas. A dynamic epistemic logic is defined with all these actions and provided with a complete axiomatization. Decision procedures for satisfiability and model checking follow. A planning-oriented approach is also discussed.

The phrase artificial intelligence has become common in our current day discourse. Fuelled by successes in machine learning, and applications interacting with us in speech and natural language, many commentators have made a leap of faith that behind these successes is a thinking machine, and this has even stoked fears of machines overcoming humankind. In this two-part article, we look at how close we are to the original quest for creating “machines who think”. In the first part, we trace the evolution of mechanical computers and also the notion of the mind up to the era before digital computers appeared on the horizon.

The AI community has always been interested in designing intelligent agents which function in a multi-agent arrangement or a man-machine scenario. More often than not, such settings may require agents to work autonomously (or under intermittent supervision at the least) in partially observable environments. Over the last 10 years or so, the planning community has started looking at this interesting class of problems from an epistemic standpoint, by augmenting the notions of knowledge and beliefs to AI planning. In this paper, we present a system that synthesizes plans from the primary agent's perspective, based on its subjective knowledge, in a multi-agent environment. We adopt a semantic approach to represent the mental model of the primary agent whose uncertainty about the world is represented using Kripke's possible worlds interpretation of epistemic logic. Planning in this logical framework is computationally challenging, and, to the best of our knowledge, most of the existing planners work with the notion of knowledge, instead of an agent's subjective knowledge. We demonstrate the system's capability of projecting beliefs of the primary agent on to others, reasoning about the role of other agents in the prospective plans, and preferring the plans that hinge on the primary agent's capabilities to those which demand others' cooperation. We evaluate our system on the problems discussed in the literature and show that it takes fractions of seconds to search for a plan for a given problem. We also discuss the issues that arise in modeling dynamic domains with the representation our system employs.

In the first week of the year 2020, we got the news that AI now outperforms doctors in detecting breast cancer. This is in line with a continuous stream of news coming from the world of diagnosis and has lent credence to the sentiment that AI is poised to overcome humankind. However, some perceptive observers have commented that recent advances are largely due to the massive increase in both availability of data and computing power. Moreover, it is only a narrow task of classification that has led the news blitz. Classification can be thought of as a stimulus-response process. Human intelligence is much broader. In particular, humans often display a stimulus-deliberation-response cycle. There is much that goes on in the “thinking” phase that was the original aim of AI before the data and speed started dominating applications. The second of the two-part article on AI traces the evolution in the field since the Dartmouth conference, and takes stock of where we are on the road to thinking machines.

The ability of an agent to distinguish the ramification effects of an action from its direct effects adds value to the explainability of its decisions. In this work, we propose to encode the ramification effects of ontic and epistemic actions as single-point update models in an epistemic planning domain modeled with Kripkean semantics of Knowledge and Belief. We call them “mental actions”. We discuss a preliminary approach to realize our idea, and we conclude by pointing out some optimizations as our ongoing pursuit.

Gelatin methacryloyl (GelMA) hydrogels have gained significant recognition as versatile biomaterials in the biomedical domain. GelMA hydrogels emulate vital characteristics of the innate extracellular matrix by integrating cell-adhering and matrix metalloproteinase-responsive peptide motifs. These features enable cellular proliferation and spreading within GelMA-based hydrogel scaffolds. Moreover, GelMA displays flexibility in processing, as it experiences crosslinking when exposed to light irradiation, supporting the development of hydrogels with adjustable mechanical characteristics. The drug delivery landscape has been reshaped by GelMA hydrogels, offering a favorable platform for the controlled and sustained release of therapeutic actives. The tunable physicochemical characteristics of GelMA enable precise modulation of the kinetics of drug release, ensuring optimal therapeutic effectiveness. In tissue engineering, GelMA hydrogels perform an essential role in the design of the scaffold, providing a biomimetic environment conducive to cell adhesion, proliferation, and differentiation. Incorporating GelMA in three-dimensional printing further improves its applicability in drug delivery and developing complicated tissue constructs with spatial precision. Wound healing applications showcase GelMA hydrogels as bioactive dressings, fostering a conducive microenvironment for tissue regeneration. The inherent biocompatibility and tunable mechanical characteristics of GelMA provide its efficiency in the closure of wounds and tissue repair. GelMA hydrogels stand at the forefront of biomedical innovation, offering a versatile platform for addressing diverse challenges in drug delivery, tissue engineering, and wound healing. This review provides a comprehensive overview, fostering an in-depth understanding of GelMA hydrogel’s potential impact on progressing biomedical sciences.