A large portion of today's industrial manufacturing relies on At Palo Alto Research Center (PARC), researchers recognized the potential business value to designers as well as manufacturers, and this value proposition was validated during project execution by presenting early prototypes of the software to potential users. The objective of PARC's uFab project hence was to create a software tool that, given just a CAD file and a representation of available machines and tools, generates a process plan in real time. While work in this area had been done in the 1980s under the name computer-aided process planning (CAPP) (Alting and Zhang 1989), none of the approaches that were pursued then resulted in a fully automated solution. A major shortcoming of these systems was their reliance on features, recognizable configurations of faces on a part such as pockets, slots, and holes, in order to represent states and actions. Any advances that This reliance on feature-based representations to these domain-specific needs, implementing are specific to domain-independent hindered their broad applicability the actual search used for planning in PDDL, such as the powerful to parts that could not be easily planning was the easy part.

This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI. AI Magazine also maintains a calendar listing that includes nonaffiliated conferences at www.aaai.org/Magazine/calendar.php. IC3K 2016 will be held Twelfth AAAI Conference on Artificial New Orleans, Louisiana USA. FLAIRS-2017 will be held May 22-24, held 24-26 February, 2017 in Porto, 2017 inMarco Island, Florida, USA. IAAI-17 will be held February The 27th International Conference The 16th International Conference 4-9 in San Francisco, California USA. on Automated Planning and Scheduling.

Players in Dear Leader’s Happy Story Time are placed in the role of contestants in a reality TV show where they are forced to audition for roles in the upcoming film of the host, a deranged billionaire who has inexplicably been elected president.  The stories are produced by a story generator that combines stock plots and characters to produce kitsch story outlines.  The players then collaborate to improvise a camp performance of the outline.  The game design provides a context for experimenting with automatic story generation within a narrative game, as well as an opportunity for experimenting with knowledge representation schemes for expressing the tropes of popular narrative.  The story generator uses a higher-order logic for describing tropes, and an HTN planning algorithm based on Nau et al.’s SHOP.

We construct a large scale of causal knowledge in term of Fabula elements by extracting causal links from existing common sense ontology ConceptNet5. We design a Constrained Monte Carlo Tree Search (cMCTS) algorithm that allows users to specify positive and negative concepts to appear in the generated stories. cMCTS can find a believable causal story plot. We show the merits by experiments and discuss the remedy strategies in cMCTS that may generate incoherent causal plots.

This paper presents a new model of cooperative behavior based on the interaction of plan recognition and automated planning.  Based on observations of the actions of an "initiator" agent, a  "supporter" agent uses plan recognition to hypothesize the plans  and goals of the initiator.  The supporter agent then proposes and  plans for a set of subgoals it will achieve to help the initiator.  The approach is demonstrated in an open-source, virtual robot  platform.

In this work, we propose a Monte Carlo Tree Search (MCTS) based approach to procedurally generate Sokoban puzzles. Our method generates puzzles through simulated game play, guaranteeing solvability in all generated puzzles. We perform a user study to infer features that are efficient to compute and are highly correlated with expected puzzle difficulty. We combine several of these features into a data-driven evaluation function for MCTS puzzle creation. The resulting algorithm is efficient and can be run in an anytime manner, capable of quickly generating a variety of challenging puzzles. We perform a second user study to validate the predictive capability of our approach, showing a high correlation between increasing puzzle scores and perceived difficulty.

Monte Carlo Tree Search (MCTS) has become a popular solution for controlling non-player characters. Its use has repeatedly been shown to be capable of creating strong game playing opponents. However, the emergent playstyle of agents using MCTS is not necessarily human-like, believable or enjoyable. AI Factory Spades, currently the top rated Spades game in the Google Play store, uses a variant of MCTS to control non-player characters. In collaboration with the developers, we collected gameplay data from 27,592 games and showed in a previous study that the playstyle of human players significantly differed from that of the non-player characters. This paper presents a method of biasing MCTS using human gameplay data to create Spades playing agents that emulate human play whilst maintaining a strong, competitive performance. The methods of player modelling and biasing MCTS presented in this study are generally applicable to digital games with discrete actions.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Here's a taste of what's to come: In contrast to the usual approach to operating self-driving cars, we did not program any explicit object detection, mapping, path planning or control components into this car. Instead, the car learns on its own to create all necessary internal representations necessary to steer, simply by observing human drivers.

A big challenge in environmental monitoring is the spatiotemporal variation of the phenomena to be observed. To enable persistent sensing and estimation in such a setting, it is beneficial to have a time-varying underlying environmental model. Here we present a planning and learning method that enables an autonomous marine vehicle to perform persistent ocean monitoring tasks by learning and refining an environmental model. To alleviate the computational bottleneck caused by large-scale data accumulated, we propose a framework that iterates between a planning component aimed at collecting the most information-rich data, and a sparse Gaussian Process learning component where the environmental model and hyperparameters are learned online by taking advantage of only a subset of data that provides the greatest contribution. Our simulations with ground-truth ocean data shows that the proposed method is both accurate and efficient.

Partial-order plans (POPs) are attractive because of their least-commitment nature, which provides enhanced plan flexibility at execution time relative to sequential plans. Current research on automated plan generation focuses on producing sequential plans, despite the appeal of POPs. In this paper we examine POP generation by relaxing or modifying the action orderings of a sequential plan to optimize for plan criteria that promote flexibility. Our approach relies on a novel partial weighted MaxSAT encoding of a sequential plan that supports the minimization of deordering or reordering of actions. Using a similar technique, we further demonstrate how to remove redundant actions from the plan, and how to combine this criterion with the objective of maximizing a POP's flexibility. Our partial weighted MaxSAT encoding allows us to compute a POP from a sequential plan effectively. We compare the efficiency of our approach to previous methods for POP generation via sequential-plan relaxation. Our results show that while an existing heuristic approach consistently produces the optimal deordering of a sequential plan, our approach has greater flexibility when we consider reordering the actions in the plan while also providing a guarantee of optimality. We also investigate and confirm the accuracy of the standard flex metric typically used to predict the true flexibility of a POP as measured by the number of linearizations it represents.