Roboticists are putting a tremendous amount of time and effort into finding the right combination of sensors and algorithms that will keep their drones from smashing into things. It's a very difficult problem: With a few exceptions, you've got small platforms that move fast and don't have the payload capability for the kind of sensors or computers that you really need to do real-time avoidance of things like trees or powerlines. And without obstacle avoidance, how will we ever have drones that can deliver new athletic socks to our doorstep in 30 minutes or less? At the University of Pennsylvania's GRASP Lab, where they've been working very very hard at getting quadrotors to fly through windows without running into them, Yash Mulgaonkar, Luis Guerrero-Bonilla, Anurag Makineni, and Professor Vijay Kumar have come up with what seems to be a much simpler solution for navigation and obstacle avoidance with swarms of small aerial robots: Give them a roll cage, and just let them run into whatever is in their way. This kind of "it'll be fine" philosophy is what you find in most small flying insects, like bees: They don't worry all that much about bumbling into stuff, or each other, they just kind of shrug it off and keep on going.

Unavoidable dead-ends are common in many probabilistic planning problems, e.g. when actions may fail or when operating under resource constraints. An important objective in such settings is MaxProb, determining the maximal probability with which the goal can be reached, and a policy achieving that probability. Yet algorithms for MaxProb probabilistic planning are severely underexplored, to the extent that there is scant evidence of what the empirical state of the art actually is. We close this gap with a comprehensive empirical analysis. We design and explore a large space of heuristic search algorithms, systematizing known algorithms and contributing several new algorithm variants. We consider MaxProb, as well as weaker objectives that we baptize AtLeastProb (requiring to achieve a given goal probabilty threshold) and ApproxProb (requiring to compute the maximum goal probability up to a given accuracy). We explore both the general case where there may be 0-reward cycles, and the practically relevant special case of acyclic planning, such as planning with a limited action-cost budget. We design suitable termination criteria, search algorithm variants, dead-end pruning methods using classical planning heuristics, and node selection strategies. We design a benchmark suite comprising more than 1000 instances adapted from the IPPC, resource-constrained planning, and simulated penetration testing. Our evaluation clarifies the state of the art, characterizes the behavior of a wide range of heuristic search algorithms, and demonstrates significant benefits of our new algorithm variants.

We introduce a framework for model learning and planning in stochastic domains with continuous state and action spaces and non-Gaussian transition models. It is efficient because (1) local models are estimated only when the planner requires them; (2) the planner focuses on the most relevant states to the current planning problem; and (3) the planner focuses on the most informative and/or high-value actions. Our theoretical analysis shows the validity and asymptotic optimality of the proposed approach. Empirically, we demonstrate the effectiveness of our algorithm on a simulated multi-modal pushing problem.

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.