We describe a multi-modal cargo routing (MMCR) domain for modelling military logistics planning problems. These are transport optimisation problems that feature timing constraints, concurrency, capacitated resources, and action costs. We have developed a PDDL domain model, and have released a collection of problem instances along with a software tool to aid in the design and generation of new problem instances. Small instances of this domain stretch the capabilities of existing automated planning procedures, and larger realistic instances are beyond the capabilities of existing automated planning systems. We anticipate that scalable solution procedures for this domain will follow in the footsteps of systems, such as OPTIC and TIMIPLAN, which combine heuristic search concepts with mathematical programming optimisation tools.

In this paper, we exploit Bayesian nonparametric microphone array processing (BNP-MAP) for analyzing the spatio-temporal patterns of the frog chorus. Such analysis in real environments is made more difficult due to unpredictable sound sources including calls of various species of animals. An application of conventional signal processing algorithms has been difficult because these algorithms usually require the number of sound sources in advance. BNP-MAP is developed to cope with auditory uncertainties such as reverberation or unknown number of sounds by using a unified model based on Bayesian nonparametrics. We exploit BNP-MAP for analyzing the sound data of 20 minutes captured by a 7-channel microphone array in a paddy rice field in Oki Island, Japan, and revealed that two individuals of Schlegel's green tree frog (Rhacophorus schlegelii) called alternately with anti-phase. This result is compared with the video data captured by a video camera with 18 units of sound-imaging devices called Firefly deployed along the bank of the rice field. The auditory result provides more detailed patterns of the frog chorus in higher temporal resolutions. This higher resolution enables to analyze fine temporal structures of the frog calls. For example, BNP-MAP reveals the trill-like calling pattern of R. schlegelii.

We propose to introduce social media opinion mining research into the field of computational sustainability. Opinion mining from social media can be a faster and less expensive alternative to traditional survey and polling, on which many sustainability research are based. We describe a framework for such analysis, examine the challenges in our proposed framework and current status of research on those challenges. We also propose some possible research directions for tackling these challenges.

Discovering congestion patterns from table-formed traffic reports is critical for traffic bottleneck analysis. However, patterns mined by existing algorithms often do not satisfy user requirements and are not actionable for traffic management. Traffic officers may not pursue the most frequent patterns but expect mining outcomes showing the dependence between congestion and various kinds of road properties for traffic planning. Such multi-view analysis requires to integrate user preferences of data attributes into pattern mining process. To tackle this problem, we propose a multi-view attributes reduction model for discovering the patterns of user interests, in which user views are interpreted as preferred attributes and formulated by attribute orders. Based on the pattern discovery model, a workflow is built for traffic bottleneck analysis, which consists of data preprocessing, preference representation and congestion pattern mining. Our approach is validated on the reports of road conditions from Shanghai, which shows that the resultant multi-view findings are effective for analyzing congestion causes and traffic management.

With the rapid development of mobile devices, global position system (GPS) and Web 2.0 technologies, location-based social networks (LBSNs) have attracted millions of users to share rich information, such as experiences and tips. Point-of-Interest (POI) recommender system plays an important role in LBSNs since it can help users explore attractive locations as well as help social network service providers design location-aware advertisements for Point-of-Interest. In this paper, we present a brief survey over the task of Point-of-Interest recommendation in LBSNs and discuss some research directions for Point-of-Interest recommendation. We first describe the unique characteristics of Point-of-Interest recommendation, which distinguish Point-of-Interest recommendation approaches from traditional recommendation approaches. Then, according to what type of additional information are integrated with check-in data by POI recommendation algorithms, we classify POI recommendation algorithms into four categories: pure check-in data based POI recommendation approaches, geographical influence enhanced POI recommendation approaches, social influence enhanced POI recommendation approaches and temporal influence enhanced POI recommendation approaches. Finally, we discuss future research directions for Point-of-Interest recommendation.

Reinforcement learning enables a robot to learn behavior through trial-and-error. However, knowledge is usually built from scratch and learning may take a long time. Many approaches have been proposed to transfer the knowledge learned in one task and reuse it in another new similar task to speed up learning in the target task.A very effective knowledge to be transferred is an abstract policy, which generalizes the learned policies in source tasks to extend the domain of tasks that can reuse them.There are inductive and deductive methods to generate abstract policies.However, there is a lack of deeper analysis to assess not only the effectiveness of each type of policy, but also the way in which each policy is used to accelerate the learning in a new task.In this paper we propose two simple inductive methods and we use a deductive method to generate stochastic abstract policies from source tasks. We also propose two strategies to use the abstract policy during learning in a new task: the hard and the soft strategy. We make a comparative analysis between the three types of policies and the two strategies of use in a robotic navigation domain.We show that these techniques are effective in improving the agent learning performance, especially during the early stages of the learning process, when the agent is completely unaware of the new task.

Optimization-based heuristics may offer very good estimates. But, calculatingthem may be time consuming, especially if the optimization problem isintractable. This raises the question of their applicability. This papersummarizes early work from the year 2000 on optimization-based heuristics inthe context of PDBs for the Tile-Puzzle. We show that an admissible heuristicbased on Vertex-Cover (VC) can be calculated in reasonable time over a largecollection of small PDBs. When larger PDBs are involved we suggest the idea ofusing another lookup table that precalculates and stores all possible relevantVC values. This table can be later looked up in a constant time during thesearch. We discuss the conditions under which this idea can be generalized.Experimental results demonstrate the applicability of these two ideas on the15- and 24-Puzzle. The first idea appeared in (Felner, Korf and Hanan, 2004) but the secondidea is presented here for the first time.

We propose a novel online learning method for minimizing regret in large extensive-form games. The approach learns a function approximator online to estimate the regret for choosing a particular action. A no-regret algorithm uses these estimates in place of the true regrets to define a sequence of policies. We prove the approach sound by providing a bound relating the quality of the function approximation and regret of the algorithm. A corollary being that the method is guaranteed to converge to a Nash equilibrium in self-play so long as the regrets are ultimately realizable by the function approximator. Our technique can be understood as a principled generalization of existing work on abstraction in large games; in our work, both the abstraction as well as the equilibrium are learned during self-play. We demonstrate empirically the method achieves higher quality strategies than state-of-the-art abstraction techniques given the same resources.

Auction mechanisms are very well known methods to allocate tasks when several agents are involved. Particularly, multi-attribute auctions are a special mechanism that allows the consideration of task attributes other than prices, such as delivery time or energy consumptions. Incentive compatible mechanisms encourage agents to reveal the attributes which agents estimate truthful, however, these mechanisms by themselves cannot know if such estimations are reliable or not due to uncertainty. Under such circumstances, trust could complement incentive compatibility reducing the risk of losses by the auctioneer. The use of trust in auctions is a well-studied problem; however, most of the works in the literature focus on how to model trust rather on how trust is used in the mechanism. Thus, this paper proposes an easy and systematic way to include a multi-faceted model of trust into multi-attribute auctions. Conversely to other previous works where trust is only used in the winner determination problem, the presented approach uses trust both in deciding the winner of the auction and in the payment to the corresponding bidder. According to the results obtained from the experimentation, the use of trust following the methodology presented in this paper highly reduces the number of winner bids from unreliable bidders and, therefore, the number of tasks executed in worse conditions than the agreed. Complementary, this paper proposes a new trust adaptation method which consists of increasing or decreasing the trust value (depending on whether the task is executed properly or not) according to a simple mathematical function with asymptotes on 0 and 1. This model does not present the rigidity problem present in other models of the literature when it comes to agents that have inconstant performances.

Much AI research on computational sustainability has focused on monitoring, modeling, analysis, and optimization of existing systems and processes. In this article, we present another exciting and promising paradigm for AI research on computational sustainability that emphasizes design of new systems and processes, and, in particular, on biologically inspired design. We first characterize biologically inspired design, then examine its relationship with environmental sustainability, next present a computational model of the process of biologically inspired design, and finally describe a few computational systems for supporting biologically inspired design practice.