Learning Models for Following Natural Language Directions in Unknown Environments

Natural language offers an intuitive and flexible means for humans to communicate with the robots that we will increasingly work alongside in our homes and workplaces. Recent advancements have given rise to robots that are able to interpret natural language manipulation and navigation commands, but these methods require a prior map of the robot's environment. In this paper, we propose a novel learning framework that enables robots to successfully follow natural language route directions without any previous knowledge of the environment. The algorithm utilizes spatial and semantic information that the human conveys through the command to learn a distribution over the metric and semantic properties of spatially extended environments. Our method uses this distribution in place of the latent world model and interprets the natural language instruction as a distribution over the intended behavior. A novel belief space planner reasons directly over the map and behavior distributions to solve for a policy using imitation learning. We evaluate our framework on a voice-commandable wheelchair. The results demonstrate that by learning and performing inference over a latent environment model, the algorithm is able to successfully follow natural language route directions within novel, extended environments.Comment: ICRA 201

Reimagining the Journal Editorial Process: An AI-Augmented Versus an AI-Driven Future

The editorial process at our leading information systems journals has been pivotal in shaping and growing our field. But this process has grown long in the tooth and is increasingly frustrating and challenging its various stakeholders: editors, reviewers, and authors. The sudden and explosive spread of AI tools, including advances in language models, make them a tempting fit in our efforts to ease and advance the editorial process. But we must carefully consider how the goals and methods of AI tools fit with the core purpose of the editorial process. We present a thought experiment exploring the implications of two distinct futures for the information systems powering today’s journal editorial process: an AI-augmented and an AI-driven one. The AI-augmented scenario envisions systems providing algorithmic predictions and recommendations to enhance human decision-making, offering enhanced efficiency while maintaining human judgment and accountability. However, it also requires debate over algorithm transparency, appropriate machine learning methods, and data privacy and security. The AI-driven scenario, meanwhile, imagines a fully autonomous and iterative AI. While potentially even more efficient, this future risks failing to align with academic values and norms, perpetuating data biases, and neglecting the important social bonds and community practices embedded in and strengthened by the human-led editorial process. We consider and contrast the two scenarios in terms of their usefulness and dangers to authors, reviewers, editors, and publishers. We conclude by cautioning against the lure of an AI-driven, metric-focused approach, advocating instead for a future where AI serves as a tool to augment human capacity and strengthen the quality of academic discourse. But more broadly, this thought experiment allows us to distill what the editorial process is about: the building of a premier research community instead of chasing metrics and efficiency. It is up to us to guard these values

Software project economics: A roadmap

The objective of this paper is to consider research progress in the field of software project economics with a view to identifying important challenges and promising research directions. I argue that this is an important sub-discipline since this will underpin any cost-benefit analysis used to justify the resourcing, or otherwise, of a software project. To accomplish this I conducted a bibliometric analysis of peer reviewed research articles to identify major areas of activity. My results indicate that the primary goal of more accurate cost prediction systems remains largely unachieved. However, there are a number of new and promising avenues of research including: how we can combine results from primary studies, integration of multiple predictions and applying greater emphasis upon the human aspects of prediction tasks. I conclude that the field is likely to remain very challenging due to the people-centric nature of software engineering, since it is in essence a design task. Nevertheless the need for good economic models will grow rather than diminish as software becomes increasingly ubiquitous

Collaborative Verification-Driven Engineering of Hybrid Systems

Hybrid systems with both discrete and continuous dynamics are an important model for real-world cyber-physical systems. The key challenge is to ensure their correct functioning w.r.t. safety requirements. Promising techniques to ensure safety seem to be model-driven engineering to develop hybrid systems in a well-defined and traceable manner, and formal verification to prove their correctness. Their combination forms the vision of verification-driven engineering. Often, hybrid systems are rather complex in that they require expertise from many domains (e.g., robotics, control systems, computer science, software engineering, and mechanical engineering). Moreover, despite the remarkable progress in automating formal verification of hybrid systems, the construction of proofs of complex systems often requires nontrivial human guidance, since hybrid systems verification tools solve undecidable problems. It is, thus, not uncommon for development and verification teams to consist of many players with diverse expertise. This paper introduces a verification-driven engineering toolset that extends our previous work on hybrid and arithmetic verification with tools for (i) graphical (UML) and textual modeling of hybrid systems, (ii) exchanging and comparing models and proofs, and (iii) managing verification tasks. This toolset makes it easier to tackle large-scale verification tasks

Neuroeconomics: a critical reconsideration

Abstract. Understanding more about how the brain functions should help us understand economic behaviour. But some would have us believe that it has done this already, and that insights from neuroscience have already provided insights in economics that we would not otherwise have. Much of this is just academic marketing hype, and to get down to substantive issues we need to identify that fluff for what it is. After we clear away the distractions, what is left? The answer is that a lot is left, but it is still all potential. That is not a bad thing, or a reason to stop the effort, but it does point to the need for a serious reconsideration of what neuroeconomics is and what passes for explanation in this literature. I argue that neuroeconomics can be a valuable field, but not the way it is being developed and “sold ” now. The same is true more generally of behavioural economics, which share