Nowadays, robots are programmed with a mix of discrete and continuous low level behaviors by experts in a very time consuming and expensive process. Existing automated planning approaches are either based on hybrid model predictive control techniques, which do not scale well due to time discretization, or temporal planners, which sacrifice plan expressivity by only supporting discretized fixed rates of change in continuous effects. We introduce Scotty, a mixed discrete-continuous generative planner that finds the middle ground between these two. Scotty can reason with linear time evolving effects whose behaviors can be modified by bounded control variables, with no discretization involved. Our planner exploits the expressivity of flow tubes, which compactly encapsulate continuous effects, and the performance of heuristic forward search. The generated solution plans are better suited for robust execution, as executives can use the flexibility in both time and continuous control variables to react to disturbances.

Temporal uncertainty in large-scale logistics forces one to trade off between lost efficiency through built-in slack and costly replanning when deadlines are missed. Due to the difficulty of reasoning about such likelihoods and consequences, a computational framework is needed to quantify and bound the risk of violating scheduling requirements. This work addresses the chance-constrained scheduling problem, where actions' durations are modeled probabilistically. Our solution method uses conflict-directed risk allocation to efficiently compute a scheduling policy. The key insight, compared to previous work in probabilistic scheduling, is to decouple the reasoning about temporal and risk constraints. This decomposes the problem into a separate master and subproblem, which can be iteratively solved much quicker. Through a set of simulated car-sharing scenarios, it is empirically shown that conflict-directed risk allocation computes solutions nearly an order of magnitude faster than prior art, which considers all constraints in a single lump-sum optimization.

Computational sustainability problems, which exist in dynamic environments with high amounts of uncertainty, provide a variety of unique challenges to artificial intelligence research and the opportunity for significant impact upon our collective future. This editorial introduction provides an overview of artificial intelligence for computational sustainability, and introduces the special issue articles that appear in this issue and the previous issue of AI Magazine.

Computational sustainability problems, which exist in dynamic environments with high amounts of uncertainty, provide a variety of unique challenges to artificial intelligence research and the opportunity for significant impact upon our collective future. This editorial introduction provides an overview of artificial intelligence for computational sustainability, and introduces the special issue articles that appear in this issue and the previous issue of AI Magazine.

Scheduling under uncertainty is essential to many autonomous systems and logistics tasks. Probabilistic methods for solving temporal problems exist which quantify and attempt to minimize the probability of schedule failure. These methods are overly conservative, resulting in a loss in schedule utility. Chance constrained formalism address over-conservatism by imposing bounds on risk, while maximizing utility subject to these risk bounds. In this paper we present the probabilistic Simple Temporal Network (pSTN), a probabilistic formalism for representing temporal problems with bounded risk and a utility over event timing. We introduce a constrained optimisation algorithm for pSTNs that achieves compactness and efficiency through a problem encoding in terms of a parameterised STNU and its reformulation as a parameterised STN. We demonstrate through a car sharing application that our chance-constrained approach runs in the same time as the previous probabilistic approach, yields solutions with utility improvements of at least 5% over previous arts, while guaranteeing operation within the specified risk bound.

Introducing robots as teammates in medical, space, and military domains raises interesting and challenging human factors issues that do not necessarily arise in multi-robot coordination. For example, we must consider how to design robots that integrate seamlessly with human group dynamics. An essential quality of a good human partner is her ability to robustly anticipate and adapt to other team members and the environment. Robots should preserve this ability and avoid constraining their human partners’ flexibility to act. This requires that the robot partner be capable of reasoning quickly online, and adapting to the humans’ actions in a temporally fluid way. This paper describes recent advances in dynamic plan execution, and argues that these advances provide a potentially powerful framework for explicitly modeling and efficiently reasoning on temporal information for human-robot interaction. We describe an executive named Chaski that enables a robot to coordinate with a human to execute a shared plan under different models of teamwork. We have applied Chaski to demonstrate teamwork using two Barrett Whole Arm Manipulators, and describe our ongoing work to demonstrate temporally fluid human-robot teaming using the Mobile-Dexterous-Social (MDS) robot.

An essential quality of a good partner is her responsiveness to other team members. Recent work in dynamic plan execution exhibits elements of this quality through the ability to adapt to the temporal uncertainties of others agents and the environment. However, a good teammate also has the ability to adapt on-the-fly through task assignment. We generalize the framework of dynamic execution to perform plan execution with dynamic task assignment as well as scheduling. This paper introduces Chaski, a multi-agent executive for scheduling temporal plans with online task assignment. Chaski enables an agent to dynamically update its plan in response to disturbances in task assignment and the schedule of other agents. The agent then uses the updated plan to choose, schedule and execute actions that are guaranteed to be temporally consistent and logically valid within the multi-agent plan. Chaski is made efficient through an incremental algorithm that compactly encodes all scheduling policies for all possible task assignments. We apply Chaski to perform multi-manipulator coordination using two Barrett Arms within the authors' hardware testbed. We empirically demonstrate up to one order of magnitude improvements in execution latency and solution compactness compared to prior art.

The dispatcher uses the dispatchable form to quickly make dynamic scheduling decisions. As autonomous systems become more capable and common, However, developing flexible executives for plans with they will need to reason about complex tasks and robustly choices, has been more difficult. Kim, Williams, and execute plans in uncertain environments. In previous work, Abramson present an executive called Kirk, which uses a Williams et al. introduced the Reactive Model-Based Programming deliberative planning step to change the execution sequence Language (RMPL), which is designed to allow online (2001). Although their results show improvement engineers to simply and intuitively express the desired behavior over prior planning systems, the latency is still too high for of the system (2003). Then the agent's executive determines tightly coupled systems, for example robots working with the correct sequence of actions to accomplish this humans or walking robots with fast dynamics. Recently, behavior, relieving the programmer of explicitly coding that Shah and Williams extended the compiler and dispatcher logic. RMPL programs often involve temporal constraints model to Temporal Constraint Satisfaction Problems (TCwhich the executives must reason over. SPs), a type of temporal problems with choice, by compactly Kim, Williams, and Abramson previously developed recording the possible set of solutions and efficiently Temporal Plan Networks (TPNs) as a temporal constraint reasoning over the possible options (2008).

A wide range of sensor-rich, networked embedded systems are being created that must operate robustly for years in the face of novel failures by managing complex autonomic processes. Our objective is to revolutionize the way in which we control these new artifacts by creating reactive model-based programming languages that enable everyday systems to reason intelligently and enable machines to explore other worlds. The program's executive automatically coordinates system interactions to achieve these states, entertaining known and potential failures, using models of its constituents and environment. Model-based programming is being generalized to hybrid discrete-continuous systems and the coordination of networks of robotic vehicles.

A wide range of sensor-rich, networked embedded systems are being created that must operate robustly for years in the face of novel failures by managing complex autonomic processes. These systems are being composed, for example, into vast networks of space, air, ground, and underwater vehicles. Our objective is to revolutionize the way in which we control these new artifacts by creating reactive model-based programming languages that enable everyday systems to reason intelligently and enable machines to explore other worlds. A model-based program is state and fault aware; it elevates the programming task to specifying intended state evolutions of a system. The program's executive automatically coordinates system interactions to achieve these states, entertaining known and potential failures, using models of its constituents and environment. At the executive's core is a method, called CONFLICT-DIRECTED A*, which quickly prunes promising but infeasible solutions, using a form of one-shot learning. This approach has been demonstrated on a range of systems, including the National Aeronautics and Space Administration's Deep Space One probe. Model-based programming is being generalized to hybrid discrete-continuous systems and the coordination of networks of robotic vehicles.