She has (which includes a human user), while planning determines equipped the apartment with a series of service robots, the concrete actions that should be carried out in order to sensors and actuators which help her manage some of best support the perceived context. The domain description the physical and cognitive difficulties she has due to formalism used by SAM is based on metric temporal constraints; her age. Her home alerts her if she appears to be overcooking such domains model both the criteria for context inference her meals, and autonomously organizes when and the planning operators used for plan synthesis. The of the user and to contextually synthesize action plans for home recognizes when Malin is sleeping, eating and actuators in the intelligent environment. The knowledge representation scheme used in SAM is based State of the art robotic and sensor systems can be leveraged on Allen's Interval Relations (Allen 1984), augmented with to achieve intelligent functionalities that are useful in a number temporal bounds.

She has (which includes a human user), while planning determines equipped the apartment with a series of service robots, the concrete actions that should be carried out in order to sensors and actuators which help her manage some of best support the perceived context. The domain description the physical and cognitive difficulties she has due to formalism used by SAM is based on metric temporal constraints; her age. Her home alerts her if she appears to be overcooking such domains model both the criteria for context inference her meals, and autonomously organizes when and the planning operators used for plan synthesis. The of the user and to contextually synthesize action plans for home recognizes when Malin is sleeping, eating and actuators in the intelligent environment. The knowledge representation scheme used in SAM is based State of the art robotic and sensor systems can be leveraged on Allen's Interval Relations (Allen 1984), augmented with to achieve intelligent functionalities that are useful in a number temporal bounds.

She has (which includes a human user), while planning determines equipped the apartment with a series of service robots, the concrete actions that should be carried out in order to sensors and actuators which help her manage some of best support the perceived context. The domain description the physical and cognitive difficulties she has due to formalism used by SAM is based on metric temporal constraints; her age. Her home alerts her if she appears to be overcooking such domains model both the criteria for context inference her meals, and autonomously organizes when and the planning operators used for plan synthesis. The of the user and to contextually synthesize action plans for home recognizes when Malin is sleeping, eating and actuators in the intelligent environment. The knowledge representation scheme used in SAM is based State of the art robotic and sensor systems can be leveraged on Allen's Interval Relations (Allen 1984), augmented with to achieve intelligent functionalities that are useful in a number temporal bounds.

She has (which includes a human user), while planning determines equipped the apartment with a series of service robots, the concrete actions that should be carried out in order to sensors and actuators which help her manage some of best support the perceived context. The domain description the physical and cognitive difficulties she has due to formalism used by SAM is based on metric temporal constraints; her age. Her home alerts her if she appears to be overcooking such domains model both the criteria for context inference her meals, and autonomously organizes when and the planning operators used for plan synthesis. The of the user and to contextually synthesize action plans for home recognizes when Malin is sleeping, eating and actuators in the intelligent environment. The knowledge representation scheme used in SAM is based State of the art robotic and sensor systems can be leveraged on Allen's Interval Relations (Allen 1984), augmented with to achieve intelligent functionalities that are useful in a number temporal bounds.

She has (which includes a human user), while planning determines equipped the apartment with a series of service robots, the concrete actions that should be carried out in order to sensors and actuators which help her manage some of best support the perceived context. The domain description the physical and cognitive difficulties she has due to formalism used by SAM is based on metric temporal constraints; her age. Her home alerts her if she appears to be overcooking such domains model both the criteria for context inference her meals, and autonomously organizes when and the planning operators used for plan synthesis. The of the user and to contextually synthesize action plans for home recognizes when Malin is sleeping, eating and actuators in the intelligent environment. The knowledge representation scheme used in SAM is based State of the art robotic and sensor systems can be leveraged on Allen's Interval Relations (Allen 1984), augmented with to achieve intelligent functionalities that are useful in a number temporal bounds.

She has (which includes a human user), while planning determines equipped the apartment with a series of service robots, the concrete actions that should be carried out in order to sensors and actuators which help her manage some of best support the perceived context. The domain description the physical and cognitive difficulties she has due to formalism used by SAM is based on metric temporal constraints; her age. Her home alerts her if she appears to be overcooking such domains model both the criteria for context inference her meals, and autonomously organizes when and the planning operators used for plan synthesis. The of the user and to contextually synthesize action plans for home recognizes when Malin is sleeping, eating and actuators in the intelligent environment. The knowledge representation scheme used in SAM is based State of the art robotic and sensor systems can be leveraged on Allen's Interval Relations (Allen 1984), augmented with to achieve intelligent functionalities that are useful in a number temporal bounds.

She has (which includes a human user), while planning determines equipped the apartment with a series of service robots, the concrete actions that should be carried out in order to sensors and actuators which help her manage some of best support the perceived context. The domain description the physical and cognitive difficulties she has due to formalism used by SAM is based on metric temporal constraints; her age. Her home alerts her if she appears to be overcooking such domains model both the criteria for context inference her meals, and autonomously organizes when and the planning operators used for plan synthesis. The of the user and to contextually synthesize action plans for home recognizes when Malin is sleeping, eating and actuators in the intelligent environment. The knowledge representation scheme used in SAM is based State of the art robotic and sensor systems can be leveraged on Allen's Interval Relations (Allen 1984), augmented with to achieve intelligent functionalities that are useful in a number temporal bounds.

She has (which includes a human user), while planning determines equipped the apartment with a series of service robots, the concrete actions that should be carried out in order to sensors and actuators which help her manage some of best support the perceived context. The domain description the physical and cognitive difficulties she has due to formalism used by SAM is based on metric temporal constraints; her age. Her home alerts her if she appears to be overcooking such domains model both the criteria for context inference her meals, and autonomously organizes when and the planning operators used for plan synthesis. The of the user and to contextually synthesize action plans for home recognizes when Malin is sleeping, eating and actuators in the intelligent environment. The knowledge representation scheme used in SAM is based State of the art robotic and sensor systems can be leveraged on Allen's Interval Relations (Allen 1984), augmented with to achieve intelligent functionalities that are useful in a number temporal bounds.

Multi-modal journey planning, which allows multiple types of transport within a single trip, is becoming increasingly popular, due to a strong practical interest and an increasing availability of data. In real life, transport networks feature uncertainty. Yet, most approaches assume a deterministic environment, making plans more prone to failures such as major delays in the arrival. We model the scenario as a non-deterministic planning problem with continuous time and time-dependent probabilities of non-deterministic effects. We present new hardness results. We introduce a heuristic search planner, based on Weighted AO* (WAO*). The planner includes search enhancements such as sound pruning, based on state dominance, and an admissible heuristic. Focusing on plans that are robust to uncertainty, we demonstrate our ideas on data compiled from real historical data from Dublin, Ireland. Repeated calls to WAO*, with decreasing weights, have a good any-time performance. Our search enhancements play an important role in the planner's performance.

In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditioned on a particular target object. We present a general kernel framework for learning conditional rankings from various types of relational data, where rankings can be conditioned on unseen data objects. We propose efficient algorithms for conditional ranking by optimizing squared regression and ranking loss functions. We show theoretically, that learning with the ranking loss is likely to generalize better than with the regression loss. Further, we prove that symmetry or reciprocity properties of relations can be efficiently enforced in the learned models. Experiments on synthetic and real-world data illustrate that the proposed methods deliver state-of-the-art performance in terms of predictive power and computational efficiency. Moreover, we also show empirically that incorporating symmetry or reciprocity properties can improve the generalization performance.