How does one deal with the unexpected? Our world is full of surprises and we humans are often able to correctly identify a problem and respond appropriately. Consider a new driver encountering their first traffic circle; a student experiencing a hard drive failure in the middle of an assignment; an unexpected question being asked during a job interview. In situations where we have a goal (i.e., reach a destination or submit a completed assignment), we may need to alter our original plan when the unexpected occurs. Could we enable autonomous artificial intelligent agents to do the same?
A long standing area of artificial intelligence is the field of automated planning. The traditional planning problem is to generate a sequence of actions given a concrete, specific goal (e.g., I will be home at dinnertime) and a set of specific actions (e.g., drive-car, fill-gas-tank, walk, etc). Generating plans that are hopefully efficient and optimal from start to finish under different circumstances (e.g., delayed effects) is an active area of research. After a plan has been generated, and during the execution of the plan, the environment may change. For example, a robot retrieving packages in a warehouse may discover it has dropped its package. Or perhaps another robot has broken down due to a hardware failure and is blocking the path of this robot. How can a robot (or any A.I. agent) know something unexpected has happened without knowing all possible future failures?
Fundamental research on autonomy aims to find general approaches to solve this problem. One approach is to generate expectations: facts that should be true during different stages of a plan's execution. When an expectation is violated, a discrepancy occurs between the expected and perceived facts. A new trend in autonomy is to include goal reasoning capabilities. In the event of a failure, the original goal may no longer be warranted. Perhaps robust autonomous agents need to generate and change their goals in response to a changing environment.
Autonomous systems still have a long way to go and open research questions on autonomous systems remain. Funding agencies consistently seek new research on autonomy for diverse operations ranging from cybersecurity to military and vehicular autonomy. What will autonomous systems be like in the future? Will we achieve autonomous agents that can handle any situation they encounter?
- Dustin Dannenhauer
For social robots to be brought more into widespread use in the fields of companionship, care taking and domestic help, they must be capable of demonstrating social intelligence. In order to be acceptable, they must exhibit socio-communicative skills. Classic approaches to program HRI from observed human-human interactions fails to capture the subtlety of multimodal interactions as well as the key structural differences between robots and humans. The former arises due to a difficulty in quantifying and coding multimodal behaviours, while the latter due to a difference of the degrees of liberty between a robot and a human. However, the notion of reverse engineering from multimodal HRI traces to learn the underlying behavioral blueprint of the robot given multimodal traces seems an option worth exploring. With this spirit, the entire HRI can be seen as a sequence of exchanges of speech acts between the robot and human, each act treated as an action, bearing in mind that the entire sequence is goal-driven. Thus, this entire interaction can be treated as a sequence of actions propelling the interaction from its initial to goal state, also known as a plan in the domain of AI planning. In the same domain, this action sequence that stems from plan execution can be represented as a trace. AI techniques, such as machine learning, can be used to learn behavioral models (also known as symbolic action models in AI), intended to be reusable for AI planning, from the aforementioned multimodal traces. This article reviews recent machine learning techniques for learning planning action models which can be applied to the field of HRI with the intent of rendering robots as socio-communicative.
In order to engender trust in AI, humans must understand what an AI system is trying to achieve, and why. To overcome this problem, the underlying AI process must produce justifications and explanations that are both transparent and comprehensible to the user. AI Planning is well placed to be able to address this challenge. In this paper we present a methodology to provide initial explanations for the decisions made by the planner. Explanations are created by allowing the user to suggest alternative actions in plans and then compare the resulting plans with the one found by the planner. The methodology is implemented in the new XAI-Plan framework.
Urban traffic scenarios often require a high degree of cooperation between traffic participants to ensure safety and efficiency. Observing the behavior of others, humans infer whether or not others are cooperating. This work aims to extend the capabilities of automated vehicles, enabling them to cooperate implicitly in heterogeneous environments. Continuous actions allow for arbitrary trajectories and hence are applicable to a much wider class of problems than existing cooperative approaches with discrete action spaces. Based on cooperative modeling of other agents, Monte Carlo Tree Search (MCTS) in conjunction with Decoupled-UCT evaluates the action-values of each agent in a cooperative and decentralized way, respecting the interdependence of actions among traffic participants. The extension to continuous action spaces is addressed by incorporating novel MCTS-specific enhancements for efficient search space exploration. The proposed algorithm is evaluated under different scenarios, showing that the algorithm is able to achieve effective cooperative planning and generate solutions egocentric planning fails to identify.
In this paper we consider the surgical case sequencing problem (SCSP) under stochastic conditions. In addition to implementing a robust surgical schedule, we investigate the use of a number of reactive strategies that can be used to maintain schedule feasibility. We present a mixed integer nonlinear programming (MINLP) model for the reactive multiple operating room (OR) SCSP that may be suitable for direct implementation on small problem instances. A machine scheduling perspective is considered and the model is equivalent to a resource-constrained parallel-machine scheduling problem with identical machines, machine eligibility restrictions, and machine and job release dates. The explicit objective of the model is to reduce OR idle time, although other common objectives (including time to surgery and overtime) are discussed. The work here is based on a case study of a large Australian public hospital with long surgical waiting lists and high non-elective demand. Results of computational experiments show that the reactive strategies presented in this paper can be used to reduce idle time without putting excessive pressure on surgeons.
In this paper, the multiple operating room (OR) surgical case sequencing problem (SCSP) is addressed. The objective is to maximise total OR utilisation during standard opening hours. The work here is based on a case study of a large Australian public hospital with long surgical waiting lists and high levels of non-elective demand. Due to the complexity of the SCSP and the size of the instances considered herein, heuristic techniques are required to solve the problem. Constructive heuristics are presented based on both a modified block scheduling policy and an open scheduling policy. A number of real-time reactive strategies are presented that can be used to maintain schedule feasibility in the case of disruptions. Results of computational experiments show that the approach presented in this paper can be used to maintain schedule feasibility in real-time, whilst increasing OT utilisation and throughput, and reducing the waiting time of non-elective patients. The framework presented here is applicable to the real-life scheduling of OT departments, and recommendations have been provided regarding implementation of the approach.
Fully observable non-deterministic (FOND) planning is becoming increasingly important as an approach for computing proper policies in probabilistic planning, extended temporal plans in LTL planning, and general plans in generalized planning. In this work, we introduce a SAT encoding for FOND planning that is compact and can produce compact strong cyclic policies. Simple variations of the encodings are also introduced for strong planning and for what we call, dual FOND planning, where some non-deterministic actions are assumed to be fair (e.g., probabilistic) and others unfair (e.g., adversarial). The resulting FOND planners are compared empirically with existing planners over existing and new benchmarks. The notion of "probabilistic interesting problems" is also revisited to yield a more comprehensive picture of the strengths and limitations of current FOND planners and the proposed SAT approach.
Fully observable non-deterministic (FOND) planning is becoming increasingly important as an approach for computing proper policies in probabilistic planning, extended temporal plans in LTL planning, and general plans in generalized planning. In this work, we introduce a SAT encoding for FOND planning that is compact and can produce compact strong cyclic policies. Simple variations of the encodings are also introduced for strong planning and for what we call, dual FOND planning, where some non-deterministic actions are assumed to be fair (e.g., probabilistic) and others unfair (e.g., adversarial). The resulting FOND planners are compared empirically with existing planners over existing and new benchmarks. The notion of ``probabilistic interesting problems'' is also revisited to yield a more comprehensive picture of the strengths and limitations of current FOND planners and the proposed SAT approach.
Adan, Jelle (Eindhoven University of Technology, Nexperia) | Adan, Ivo (Eindhoven University of Technology) | Akcay, Alp (Eindhoven University of Technology) | Dobbelsteen, Rick Van den (Nexperia) | Stokkermans, Joep (Nexperia)
This paper addresses batch scheduling at a back-end semiconductor plant of Nexperia. This complex manufacturing environment is characterized by a large product and batch size variety, numerous parallel machines with large capacity differences, sequence and machine dependent setup times and machine eligibility constraints. A hybrid genetic algorithm is proposed to improve the scheduling process, the main features of which are a local search enhanced crossover mechanism, two additional fast local search procedures and a user-controlled multi-objective fitness function. Testing with real-life production data shows that this multi-objective approach can strike the desired balance between production time, setup time and tardiness, yielding high-quality practically feasible production schedules.
Recent progress in legged locomotion research has produced robots that can perform agile blind-walking with robustness comparable to a blindfolded human. However, this walking approach has not yet been integrated with planners for high-level activities. In this paper, we take a step towards high-level task planning for these robots by studying a planar simulated biped that captures their essential dynamics. We investigate variants of Monte-Carlo Tree Search (MCTS) for selecting an appropriate blind-walking controller at each decision cycle. In particular, we consider UCT with an intelligently selected rollout policy, which is shown to be capable of guiding the biped through treacherous terrain. In addition, we develop a new MCTS variant, called Monte-Carlo Discrepancy Search (MCDS), which is shown to make more effective use of limited planning time than UCT for this domain. We demonstrate the effectiveness of these planners in both deterministic and stochastic environments across a range of algorithm parameters. In addition, we present results for using these planners to control a full-order 3D simulation of Cassie, an agile bipedal robot, through complex terrain.