Multi-agent path finding has been proven to be a PSPACE-hard problem. Generating such a centralised multi-agent plan can be avoided, by allowing agents to plan their paths separately. However, this results in an increased number of collisions and agents must re- plan frequently. In this paper we present a framework for multi-agent path planning, which allows agents to plan independently and solve conflicts locally when they occur. The framework is a generalisation of the CQ-learning algorithm which learns sparse interactions between agents in a multi-agent reinforcement learning setting

Web personalization involves automated content analysis of text, and modern technologies of semantic analysis of text rely on a number of scales. Among them is the abstractness of meaning, which is not captured by more traditional measures of sentiment, such as valence, arousal and dominance. The present work introduces a physics-inspired approach to constructing the abstractness scale based on databases of hypernym-hyponym relations, e.g., WordNet 3.0. The idea is to define an energy as a function of word coordinates that are distributed in one dimension, and then to find a global minimum of this energy function by relocating words in this dimension. The result is a one-dimensional distribution that assigns "abstractness" values to words. While positions of individual words on this scale are subject to noise, the entire distribution globally defines the universal semantic dimension associated with the notion of hypernym-hyponym relations, called here "abstractness".

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.

The importance of URLs in the representation of a document cannot be overstated. Shorthand mnemonics such as ``wiki'' or ``blog'' are often embedded in a URL to convey its functional purpose or genre. Other mnemonics have evolved from use (e.g., a Wordpress particle is strongly suggestive of blogs). Can we leverage from this predictive power to induce the genre of a document from the representation of a URL? This paper presents a methodology for webpage genre classification from URLs which, to our knowledge, has not been previously attempted. Experiments using machine learning techniques to evaluate this claim show promising results and a novel algorithm for character n-gram decomposition is provided. Such a capability could be useful to improve personalized search results, disambiguate content, efficiently crawl the Web in search of relevant documents, and construct behavioral profiles from clickstream data without parsing the entire document.

A natural, common way of doing this is by crowdsourcing this stage as well, and specifically Human computation is a fast-growing field that seeks to harness letting people vote over different proposals that were the relative strengths of humans to solve problems that submitted by their peers. For example, in EteRNA thousands are difficult for computers to solve alone. The field has recently of designs are submitted each month, but only a small number been gaining traction within the AI community, as k of them can be synthesized in the lab (as of late 2011, increasingly more deep connections between AI and human k 8). To single out k designs to be synthesized, players computation are uncovered (Dai, Mausam, and Weld 2010; vote by reporting their k favorite designs, each of which is Shahaf and Horvitz 2010).

Websites that encourage consumers to research, rate, and review products online have become an increasingly important factor in purchase decisions. This increased importance has been accompanied by a growth in deceptive opinion spam - fraudulent reviews written with the intent to sound authentic and mislead consumers. In this study, we pool deceptive reviews solicited through crowdsourcing with actual reviews obtained from product review websites. We then explore several human- and machine-based assessment methods to spot deceptive opinion spam in our pooled review set. We find that the combination of human-based assessment methods with easily-obtained statistical information generated from the review text outperforms detection methods using human assessors alone.

Design tasks are notoriously difficult, because success is defined by the perception of the target audience, whose feedback is usually not available during design stages. Commonly, design is performed by professionals who have specific domain knowledge (i.e., an intuitive understanding of the implicit requirements of the task) and do not need the feedback of the perception of the viewers during the process. In this paper, we present a novel design methodology for creating minimal sketches of objects that uses an iterative optimization scheme. We define minimality for a sketch via the minimal number of straight line segments required for correct recognition by 75% of naiive viewers. Crowd-sourcing techniques allow us to directly include the perception of the audience in the design process. By joining designers and crowds, we are able to create a human computation system that can efficiently optimize sketches without requiring high levels of domain knowledge (i.e., design skills) from any worker.

In this paper, we develop some new Captchas belonging to genre – "CAPTCHAs with a purpose". These CAPTCHAs apart from having its applications serve some useful purpose. reCAPTCHA is one such Captcha developed at Carnegie Mellon University. It helps to digitize books. Another such Captcha is Asirra developed at Microsoft which provides homes for homeless animals. In this paper, we present Time based, Sentence based, Human Emotion based CAPTCHAs which have range of other useful purpose such as measuring reaction time of people, promoting news, general knowledge facts, jokes among people while engaging in routine activities such as checking email. Also, one can be used for conducting online polls on a very large scale. We also showcase a New Game with a Purpose called "Identical Emotions" which helps to assign tags describing emotions depicted by the images, to varied images. It can also be used to serve Emotion Based CAPTCHA. We also present a new scheme which renders attack on CAPTCHAs useless and make old CAPTCHAs reusable and help in using CAPTCHAs which might serve some practical purpose which otherwise might be vulnerable to use. This system also enables to use different "CAPTCHAs with a purpose" in conjunction with each other. At present most websites deploy only a single algorithm reCAPTCHA whose practical purpose is to digitize books, thus is limited to one domain. This system can thus, broaden the application domain of CAPTCHAs.

With the advent of the increasingly participatory Internet and the growing power of the crowd, “Serious Games” have proven to be a fertile approach for gathering task-specific natural language data at very low cost. In this paper we outline a game we call Doodling, based on the sketch-and-convey metaphor used in the popular board game Pictionary(R), with the goal of generating useful natural language data. We explore whether such a paradigm can be successfully extended for conveying more complex syntactic and semantic constructs than the words or short phrases typically used in the board game. Through a series of user experiments, we show that this is indeed the case, and that valuable parallel language data may be produced as a byproduct. In addition, we explore extensions to this paradigm along two axes – going online (vs. face-to-face) and going cross-lingual. The results in each of the sets of experiments confirm the potential of Doodling game to generate data in large quantities and across languages, and thus provide a new means of developing data sets and technologies for resource-poor languages.

A general consensus on representation of emotions and feelings in cognitive architectures is currently missing; yet artificial emotional intelligence is vital for the integration of future robots into the human society. This work introduces one possible approach to representation and processing of emotional mental states and attitudes, that allows for implementation of control of agent behavior by emotions as well as for recognition of emotional motivations in another agent's behavior. One particular advantage of this approach is that it allows for representation and processing of complex/social emotional attitudes, like shame, jealousy, resentment, or humor. The proposed validation of the approach is based on simulation of the emergence of emotional relationships in a small group of agents in a virtual environment.