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. ICAIL 2017 will be held 12-16 June, Twenty-Ninth Innovative Applications Thirtieth International Florida AI 2017 in London, UK of Artificial Intelligence Conference. IAAI-17 will be held February FLAIRS-2017 will be held May 22-24, 4-9 in San Francisco, California USA. IEA/AIE-2017 will be AAAI 2017 Spring Symposium Series on Automated Planning and Scheduling.

As NASA explores destinations beyond the Moon, the distance between Earth and spacecraft will increase communication delays between astronauts and Mission Control. Today, astronauts coordinate with Mission Control to request assistance and await approval to perform tasks. Many of these coordination tasks require multiple exchanges of information, (for example, taking turns). In the presence of long communication delays, the length of time between turns may lead to inefficiency, or increased mission risk. Future astronauts will need software-based decision aids to enable them to work autonomously from Mission Control. These tools require the right combination of mission operations functions, for example, automated planning and fault management, troubleshooting recommendations, easy to access information, and just-in-time training. Ensuring these elements are properly designed and integrated requires an integrated human factors approach. This article describes a recent demonstration of autonomous mission operations using a novel software-based decision aid onboard the International Space Station. We describe how this new technology changes the way astronauts coordinate with mission control, and how the lessons learned from these early demonstrations will enable the operational autonomy needed to ensure astronauts can safely journey to Mars, and beyond.

ABC president Channing Dungey recently opened up about the changes in the schedule for "Grey's Anatomy," "Scandal" and "How to Get Away With Murder." Last week, it was announced that instead of Jan. 19, all three shows will air on ABC on Jan. 26 to give way to Donald Trump's pre-inauguration special. While speaking with TV Line, Dungey admitted that it was a very tough call for the network to have Shonda Rhimes' three shows bumped to the coming week. "It was a very hard call. Nobody else had been waiting for it bigger than me," she said.

Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in The most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system.

We study the sampling-based planning problem in Markov decision processes (MDPs) that we can access only through a generative model, usually referred to as Monte-Carlo planning. Our objective is to return a good estimate of the optimal value function at any state while minimizing the number of calls to the generative model, i.e. the sample complexity. We propose a new algorithm, TrailBlazer, able to handle MDPs with a finite or an infinite number of transitions from state-action to next states. TrailBlazer is an adaptive algorithm that exploits possible structures of the MDP by exploring only a subset of states reachable by following near-optimal policies. We provide bounds on its sample complexity that depend on a measure of the quantity of near-optimal states. The algorithm behavior can be considered as an extension of Monte-Carlo sampling (for estimating an expectation) to problems that alternate maximization (over actions) and expectation (over next states). Finally, another appealing feature of TrailBlazer is that it is simple to implement and computationally efficient.

Aircraft fuel is without a doubt the biggest cost for any airline and often receives widespread attention, especially when airlines hedge their bets the wrong way. Cathay Pacific reported a HK$4.49 billion fuel-hedging loss in the first half of 2016, which has hurt the airline's profitability. The second biggest expense for an airline is human capital, and researchers from Hong Kong Polytechnic University and University of Nottingham Ningbo China Business School may have found a solution to ease some of Cathays financial woes through an unlikely source – Machine Learning and Data Science. The researchers say that a "poorly designed airline crew schedule can result in unreliable flight schedules, significantly jeopardizing airline operations and profitability if insufficient crew members are available or other glitches occur. For that reason, managing airline crew scheduling and costs are one of the most crucial topics for airlines because it yields enormous economic benefits and ranks as the second highest expenditure after fuel costs."

The AUV three-dimension path planning in complex turbulent underwater environment is investigated in this research, in which static current map data and uncertain static-moving time variant obstacles are taken into account. Robustness of AUVs path planning to this strong variability is known as a complex NP-hard problem and is considered a critical issue to ensure vehicles safe deployment. Efficient evolutionary techniques have substantial potential of handling NP hard complexity of path planning problem as more powerful and fast algorithms among other approaches for mentioned problem. For the purpose of this research Differential Evolution (DE) technique is conducted to solve the AUV path planning problem in a realistic underwater environment. The path planners designed in this paper are capable of extracting feasible areas of a real map to determine the allowed spaces for deployment, where coastal area, islands, static/dynamic obstacles and ocean current is taken into account and provides the efficient path with a small computation time. The results obtained from analyze of experimental demonstrate the inherent robustness and drastic efficiency of the proposed scheme in enhancement of the vehicles path planning capability in coping undesired current, using useful current flow, and avoid colliding collision boundaries in a real-time manner. The proposed approach is also flexible and strictly respects to vehicle's kinematic constraints resisting current instabilities.

We introduce ActorSim, the Actor Simulator, a toolkit for studying situated autonomy. As background, we review three goal-reasoning projects implemented in ActorSim: one project that uses information metrics in foreign disaster relief and two projects that learn subgoal selection for sequential decision making in Minecraft. We then discuss how ActorSim can be used to address cross-disciplinary gaps in several ongoing projects. To varying degrees, the projects integrate concerns within distinct specializations of AI and between AI and other more human-focused disciplines. These areas include automated planning, learning, cognitive architectures, robotics, cognitive modeling, sociology, and psychology.

A central problem in designing and implementing interactive systems---action selection---is also a core research topic in automated planning. While numerous toolkits are available for building end-to-end interactive systems, the tight coupling of representation, reasoning, and technical frameworks found in these toolkits often makes it difficult to compare or change the underlying domain models. In contrast, the automated planning community provides general-purpose representation languages and multiple planning engines that support these languages. We describe our recent work on automated planning for task-based social interaction, using a robot that must interact with multiple humans in a bartending domain.

Monte Carlo Tree Search (MCTS) is a technique to guide search in a large decision space by taking random samples and evaluating their outcome. In this work, we study MCTS methods in the context of the connection calculus and implement them on top of the leanCoP prover. This includes proposing useful proof-state evaluation heuristics that are learned from previous proofs, and proposing and automatically improving suitable MCTS strategies in this context. The system is trained and evaluated on a large suite of related problems coming from the Mizar proof assistant, showing that it is capable to find new and different proofs. To our knowledge, this is the first time MCTS has been applied to theorem proving.