Planning is useful. It lets people take actions that have desirable long-term consequences. But, planning is hard. It requires thinking about consequences, which consumes limited computational and cognitive resources. Thus, people should plan their actions, but they should also be smart about how they deploy resources used for planning their actions. Put another way, people should also "plan their plans". Here, we formulate this aspect of planning as a meta-reasoning problem and formalize it in terms of a recursive Bellman objective that incorporates both task rewards and information-theoretic planning costs. Our account makes quantitative predictions about how people should plan and meta-plan as a function of the overall structure of a task, which we test in two experiments with human participants. We find that people's reaction times reflect a planned use of information processing, consistent with our account. This formulation of planning to plan provides new insight into the function of hierarchical planning, state abstraction, and cognitive control in both humans and machines.

The healthcare industry has seen a sea of change in the last couple of years: from being autonomous to becoming automated, clinician-centric to patient-centric, disjointed to coordinated, reactive to proactive, retrospective to predictive and siloed to aware – if there is one industry which has undergone a complete transformation, it is undoubtedly healthcare. In this modern era, enhancing patient satisfaction requires business administrators to balance resources with demand, optimize workflows, mitigate waste, contain costs, and facilitate collaboration across the healthcare organization. The growing physician and nurse shortage and the increasingly competitive healthcare industry has made it challenging to attract and retain qualified personnel. With doctor shortage expected to reach 120,000 by the end of 2030, handling the increasingly elderly population has become extremely difficult, if not impossible. This is why optimizing workforce management for high-value care has become a top priority for the business administration at any hospital.

Monte-Carlo Tree Search (MCTS) is one of the most-widely used methods for planning, and has powered many recent advances in artificial intelligence. In MCTS, one typically performs computations (i.e., simulations) to collect statistics about the possible future consequences of actions, and then chooses accordingly. Many popular MCTS methods such as UCT and its variants decide which computations to perform by trading-off exploration and exploitation. In this work, we take a more direct approach, and explicitly quantify the value of a computation based on its expected impact on the quality of the action eventually chosen. Our approach goes beyond the "myopic" limitations of existing computation-value-based methods in two senses: (I) we are able to account for the impact of non-immediate (ie, future) computations (II) on non-immediate actions. We show that policies that greedily optimize computation values are optimal under certain assumptions and obtain results that are competitive with the state-of-the-art.

In the recent past, one of the hottest trends in HR technology has been the use of Artificial Intelligence (AI) in facilitating processes and decision-making. Many organizations now are leveraging AI to improve the efficiency of their employee engagement, recruitment, performance management, workforce management, succession planning processes, and other processes as well. AI is particularly well-suited for these roles, and it helps HR professionals to focus on complex issues, and leave mundane tasks to be performed by technology. Although there are many potential use cases of AI, organizations are approaching it with caution and taking one step at a time. A survey conducted by Gartner indicates that 23 percent of organizations who were using some form of AI were doing so in the HR and recruiting domain.

In the recent past, one of the hottest trends in HR technology has been the use of Artificial Intelligence (AI) in facilitating processes and decision-making. Many organizations now are leveraging AI to improve the efficiency of their employee engagement, recruitment, performance management, workforce management, succession planning processes, and other processes as well. AI is particularly well-suited for these roles, and it helps HR professionals to focus on complex issues, and leave mundane tasks to be performed by technology. Although there are many potential use cases of AI, organizations are approaching it with caution and taking one step at a time. A survey conducted by Gartner indicates that 23 percent of organizations who were using some form of AI were doing so in the HR and recruiting domain.

There is increasing demand for automated systems that can fabricate 3D structures. Robotic spatial extrusion has become an attractive alternative to traditional layer-based 3D printing due to a manipulator's flexibility to print large, directionally-dependent structures. However, existing extrusion planning algorithms require a substantial amount of human input, do not scale to large instances, and lack theoretical guarantees. In this work, we present a rigorous formalization of robotic spatial extrusion planning and provide several efficient and probabilistically complete planning algorithms. The key planning challenge is, throughout the printing process, satisfying both stiffness constraints that limit the deformation of the structure and geometric constraints that ensure the robot does not collide with the structure. We show that, although these constraints often conflict with each other, a greedy backward state-space search guided by a stiffness-aware heuristic is able to successfully balance both constraints. We empirically compare our methods on a benchmark of over 40 simulated extrusion problems. Finally, we apply our approach to 3 real-world extrusion problems.

-- Efficient driving in urban traffic scenarios requires foresight. The observation of other traffic participants, and the inference of their possible next actions depending on the own action is considered cooperative prediction and planning. Humans are well equipped with the capability to predict the actions of multiple interacting traffic participants and plan accordingly, without the need to directly communicate with others. Prior work has shown that it is possible to achieve effective cooperative planning without the need for explicit communication. However, the search space for cooperative plans is so large that the vast amount of the computational budget is spent on exploring the search space in unpromising regions that are far away from the solution. T o accelerate the planning process, we combined learned heuristics with a cooperative planning method in order to guide the search towards regions with promising actions, yielding better results at lower computational costs. Cooperative planning methods consider the mutual dependence of actions in multi-agent environments, opposed to methods that reduce multi-agent environments to single-agent environments, with other agents' action being independent of one another.

Leading Customer Support Ticket System and Sales CRM platform Zendesk has invested in Tymeshift. Tymeshift is an Omnichannel Workforce Management (WFM) tool that is made exclusively for Zendesk. Tymeshift will use the new funding to push for growth in new markets. At the time of this investment, David Birchmier, CEO- Tymeshift, shared his vision for the company's future. David said, "We're proud of the organic growth we've achieved and are excited to leverage Zendesk's investment to accelerate our product innovation pace and continue to grow our teams in Fairfield, Iowa, Lisbon, Portugal, and Novi Sad, Serbia. In short, we're focused on making our WFM solution even more comprehensive."

Leading Customer Support Ticket System and Sales CRM platform Zendesk has invested in Tymeshift. Tymeshift is an Omnichannel Workforce Management (WFM) tool that is made exclusively for Zendesk. Tymeshift will use the new funding to push for growth in new markets. At the time of this investment, David Birchmier, CEO- Tymeshift, shared his vision for the company's future. David said, "We're proud of the organic growth we've achieved and are excited to leverage Zendesk's investment to accelerate our product innovation pace and continue to grow our teams in Fairfield, Iowa, Lisbon, Portugal, and Novi Sad, Serbia. In short, we're focused on making our WFM solution even more comprehensive."

This paper reports on the open source project CLAI (Command Line AI), aimed at bringing the power of AI to the command line interface. The platform sets up the CLI as a new environment for AI researchers to conquer by surfacing the command line as a generic environment that researchers can interface to using a simple sense-act API much like the traditional AI agent architecture. In this paper, we discuss the design and implementation of the platform in detail, through illustrative use cases of new end user interaction patterns enabled by this design, and through quantitative evaluation of the system footprint of a CLAI-enabled terminal. We also report on some early user feedback on its features from an internal survey.