We propose computer-assisted techniques for helping with pedagogy in Algebra. In particular, given a proof problem p (of the form “Left-hand-side-term = Right-hand-side-term”), we show how to automatically generate problems that are similar to p. We believe that such a tool can be used by teachers in making examinations where they need to test students on problems similar to what they taught in class, and by students in generating practice problems tailored to their specific needs. Our first insight is that we can generalize p syntactically to a query Q that implicitly represents a set of problems [[Q]] (which includes p). Our second insight is that we can explore the space of problems [[Q]] automatically, use classical results from polynomial identity testing to generate only those problems in [[Q]] that are correct, and then use pruning techniques to generate only unique and interesting problems. Our third insight is that with a small amount of manual tuning on the query Q, the user can interactively guide the computer to generate problems of interest to her. We present the technical details of the above mentioned steps, and also describe a tool where these steps have been implemented. We also present an empirical evaluation on a wide variety of problems from various sub-fields of algebra including polynomials, trigonometry, calculus, determinants etc. Our tool is able to generate a rich corpus of similar problems from each given problem; while some of these similar problems were already present in the textbook, several were new!

Contextual knowledge is essential in answering questions given specific observations. While recent approaches to building commonsense knowledge basesvia text mining and/or crowdsourcing are successful,contextual knowledge is largely missing. To addressthis gap, this paper presents SocialExplain, a novel approach to acquiring contextual commonsense knowledge from explanations of social content. The acquisition process is broken into two cognitively simple tasks:to identify contextual clues from the given social content, and to explain the content with the clues. An experiment was conducted to show that multiple piecesof contextual commonsense knowledge can be identi-fied from a small number of tweets. Online users verified that 92.45% of the acquired sentences are good,and 95.92% are new sentences compared with existingcrowd-sourced commonsense knowledge bases.

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.

Then we add some world knowledge, in the form of commonsense statements, to help in the text understanding. The result combines this knowledge to form a multidimensional space where concepts, people, groups, and projects are all represented as vectors. From that space we retrieve information relevant to lab visitors--dynamically creating their presence in the vector space by creating a vector from the projects they have chosen as favorites. We then use the vector space to determine the relevance of objects in the space to each other--determining which projects are similar, which projects would be good fits for a lab visitor, and which projects fit which lab themes. Additionally, we have designed a user interface that makes this system easy and social to interact with. The following subections discuss our approach to interface design, our methods for extracting semantic information from the text base, and for assessing similarity of user interests with that knowledge.

We present several new contributions in sampling-based coverage path planning, the task of finding feasible paths that give 100% sensor coverage of complex structures in obstaclefilled and visually occluded environments. First, we establish a framework for analyzing the probabilistic completeness of a sampling-based coverage algorithm, and derive results on the completeness and convergence of existing algorithms. Second, we introduce a new algorithm for the iterative improvement of a feasible coverage path; this relies on a samplingbased subroutine that makes asymptotically optimal local improvements to a feasible coverage path based on a strong generalization of the RRT* algorithm. We then apply the algorithm to the real-world task of autonomous in-water ship hull inspection. We use our improvement algorithm in conjunction with redundant roadmap coverage planning algorithm to produce paths that cover complex 3D environments with unprecedented efficiency.

From this excellent collection of papers, three for presentation at ICAPS 2012, the were selected for special recognition. ICAPS continues Nguyen, Vien Tran, Tran Cao Son and Enrico the traditional high standards of AIPS and ECP Pontelli were selected for Best Student Paper as an archival forum for new research in the Award. In addition to the oral presentation of these e 45 papers included in this volume, consisting papers, the technical program of this year's of 37 long papers and 8 short papers, are ICAPS conference includes invited talks by those selected for plenary presentation at three distinguished speakers: Robert O. Ambrose ICAPS 2012 from a total of 132 submissions. Topics under various constraints and assumptions, included real-time planning, planning in mixed to empirical evaluation of planning and discrete-continuous domains, planning for systems scheduling techniques in practical applications. Papers in the subareas of optimal planning, probabilistic were encouraged from a range of neighboring and non-deterministic planning, planning disciplines, including model-based and scheduling for transportation, robot path reasoning, hybrid systems, run-time verification, planning, and new developments in heuristics control and robotics.

The Association for the Advancement of Artificial Intelligence was pleased to present the 2011 Fall Symposium Series, held Friday through Sunday, November 4–6, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the seven symposia are as follows: (1) Advances in Cognitive Systems; (2) Building Representations of Common Ground with Intelligent Agents; (3) Complex Adaptive Systems: Energy, Information and Intelligence; (4) Multiagent Coordination under Uncertainty; (5) Open Government Knowledge: AI Opportunities and Challenges; (6) Question Generation; and (7) Robot-Human Teamwork in Dynamic Adverse Environment. The highlights of each symposium are presented in this report.

The AAAI-11 workshop program was held Sunday and Monday, August 7–18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages; Analyzing Microtext; Applied Adversarial Reasoning and Risk Modeling; Artificial Intelligence and Smarter Living: The Conquest of Complexity; AI for Data Center Management and Cloud Computing; Automated Action Planning for Autonomous Mobile Robots; Computational Models of Natural Argument; Generalized Planning; Human Computation; Human-Robot Interaction in Elder Care; Interactive Decision Theory and Game Theory; Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action and Language; Lifelong Learning; Plan, Activity, and Intent Recognition; and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events.

The AAAI-11 workshop program was held Sunday and Monday, August 7–18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages; Analyzing Microtext; Applied Adversarial Reasoning and Risk Modeling; Artificial Intelligence and Smarter Living: The Conquest of Complexity; AI for Data Center Management and Cloud Computing; Automated Action Planning for Autonomous Mobile Robots; Computational Models of Natural Argument; Generalized Planning; Human Computation; Human-Robot Interaction in Elder Care; Interactive Decision Theory and Game Theory; Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action and Language; Lifelong Learning; Plan, Activity, and Intent Recognition; and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events.

The Association for the Advancement of Artificial Intelligence was pleased to present the 2011 Fall Symposium Series, held Friday through Sunday, November 4–6, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the seven symposia are as follows: (1) Advances in Cognitive Systems; (2) Building Representations of Common Ground with Intelligent Agents; (3) Complex Adaptive Systems: Energy, Information and Intelligence; (4) Multiagent Coordination under Uncertainty; (5) Open Government Knowledge: AI Opportunities and Challenges; (6) Question Generation; and (7) Robot-Human Teamwork in Dynamic Adverse Environment. The highlights of each symposium are presented in this report.