In aviation industry, free flight is a new concept which implies considering more freedom in the selection and modification of flight paths during flight time. The free flight concept allows pilots choose their own flight paths more efficient, and also plan for their flight with high performance. Although free flight has many advantages such as minimum delays and the reduction of the workload of the air traffic control centers, this concept causes many problems which one of the most important of them are conflicts between different aircrafts. Thus, Conflict Detection and Resolution (CD&R) is a major challenge in air traffic management. In this paper, we presented a model for CD&R between aircrafts in air traffic management using Graph Coloring Problem (GCP) method. In fact, we mapped the congestion area to a corresponding graph, and then addressed to find a reliable and optimal coloring for this graph using one of the new evolutionary algorithms known as Imperialist Competitive Algorithm (ICA) to solve the conflicts. Using ICA for solving GCP is a new method.

Motivated by the success of the translation-based approach for conformant planning, introduced by Palacios and Geffner,  we present two variants of a new compilation scheme from conformant probabilistic planning problems (CPP) to variants of classicalplanning.In CPP, we are given a set of actions -- which we assume to be deterministic in this paper, a distribution over initial states, a goal condition, and a value $0<p\leq 1$. Our task is to find a plan $\pi$ such that the goal probability following the execution of $\pi$ in the initial state is at least $p$. Our firstvariant translates CPP into classicalplanning with resource constraints, in which the resource represents probabilities of failure.  The second variant translates CPPinto cost-optimal classical planning problems, in which costs represents probabilities. Empirically, these techniques show mixed results, performing very well on some domains, and poorly on others. This  indicates that compilation-based technique are a feasible and promising direction for solving CPP problems and, possibly, more general probabilistic planning problems.

The average home computer user needs help in reducing the security risk of their home computer. We are working on an alternative approach from current home security software in which a software agent helps a user manage his/her security risk. Planning is integral to the design of this agent in several ways. First, planning can be used to make the underlying security model manageable by generating attack paths to identify vulnerabilities that are not a problem for a particular user/home computer. Second, planning can be used to identify interventions that can either avoid the vulnerability or mitigate the damage should it occur. In both cases, a central capability is that of generating alternative plans so as to find as many possible ways to trigger the vulnerability and to provide the user with options should the obvious not be acceptable. We describe our security model and our state-based approach to generating alternative plans. We show that the state-based approach can generate more diverse plans than a heuristic-based approach. However, the state-based approach sometimes generates this diversity with better quality at higher search cost.

In this study, we propose a method for learning interactions among different types of objects to devise new plans using these objects. Learning is accomplished by observing a given sequence of events with their timestamps and using spatial information on the initial state of the objects in the environment. We assume that no intermediate state information is available about the states of objects. We have used the Incredible Machine game as a suitable domain for analyzing and learning object interactions. When a knowledge base about relations among objects is provided, interactions to devise new plans are learned to a desired extent. Moreover, using spatial information of objects or temporal information of events makes it feasible to learn the conditional effects of objects on each other. Our analyses show that, integrating spatial and temporal data in a spatio-temporal learning approach gives closer results to that of the knowledge-based approach by providing applicable event models for planning. This is promising because gathering spatio-temporal information does not require great amount of knowledge.

In the multi agent path finding problem (MAPF) paths shouldbe found for several agents, each with a different start andgoal position such that agents do not collide. Previous optimalsolvers applied global A*-based searches. We presenta new search algorithm called Conflict Based Search (CBS).CBS is a two-level algorithm. At the high level, a search isperformed on a tree based on conflicts between agents. At thelow level, a search is performed only for a single agent at atime. In many cases this reformulation enables CBS to examinefewer states than A* while still maintaining optimality.We analyze CBS and show its benefits and drawbacks. Experimentalresults on various problems shows a speedup ofup to a full order of magnitude over previous approaches.

Sentiment Classification (SC) is about assigning a positive, negative or neutral label to a piece of text based on its overall opinion. This paper describes our in-progress work on extracting the meaning of words for SC. In particular, we investigate the utility of sense-level polarity information for SC. We first show that methods based on common classification features are not robust and their performance varies widely across different domains. We then show that sense-level polarity information features can significantly improve the performance of SC. We use datasets in different domains to study the robustness of the designated features. Our preliminary results show that the most common sense of the words result in the most robust results across different domains. In addition our observation shows that the sense-level polarity information is useful for producing a set of high-quality seed words which can be used for further improvement of SC task.

Homophily is the tendency of individuals in a social system to link to others who are similar to them and understanding homophily can help us build better user models for personalization and recommender systems. Many studies have verified homophily along demographic dimensions, such as age, location, occupation, etc., not only in real-world social networks but also online. However, there is limited research showing that homophily also exists when similarity is judged by topics of expertise or interests. We demonstrate the existence of topical homophily on Twitter using a novel source of evidence provided by Twitter lists. In this paper, we use LDA to extract topics from Twitter lists (a collection of user accounts created by some user that others can follow) and measure similarity between listed users based on the learned topics. We show that topically similar users are more likely to be linked via a follow relationship than less similar users.

Reading groups domain is a new domain for group recommenders. In this paper we propose a web based group recommender system which is called BoRGo: Book Recommender for Reading Groups, for reading groups domain. BoRGo uses a new information filtering technique which uses the difference between positive and negative feedbacks about a feature of a user profile and also presents an interface for after recommendation processes like achieving a consensus on the reading list.

Natural language programming has proven to be a very challenging task. We present a novel idea which suggests using crowdsourcing to do natural language programming. Our approach asks non-expert workers to provide input/output examples for a task defined in natural language form. We then use a Programming by Example system to induce the intended program from the input/output examples. Our early results are promising, encouraging further research in this area.

Crowdsourcing services are often used to collect a large amount of labeled data for machine learning. Although they provide us an easy way to get labels at very low cost in a short period, they have serious limitations. One of them is the variable quality of the crowd-generated data. There have been many attempts to increase the reliability of crowd-generated data and the quality of classifiers obtained from such data. However, in these problem settings, relatively few researchers have tried using expert-generated data to achieve further improvements. In this paper, we extend three models that deal with the problem of learning from crowds to utilize ground truths: a latent class model, a personal classifier model, and a data-dependent error model. We evaluate the proposed methods against two baseline methods on a real data set to demonstrate the effectiveness of combining crowd-generated data and expert-generated data.