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ClaimEval: Integrated and Flexible Framework for Claim Evaluation Using Credibility of Sources

Mehdi Samadi, Partha Talukdar, Manuela Veloso, Manuel Blum
Conference Papers Association for the Advancement of Artificial Intelligence (AAAI) 2016

Abstract

The World Wide Web (WWW) has become a rapidly grow- ing platform consisting of numerous sources which provide supporting or contradictory information about claims (e.g., “Chicken meat is healthy”). In order to decide whether a claim is true or false, one needs to analyze content of dif- ferent sources of information on the Web, measure credibility of information sources, and aggregate all these information. This is a tedious process and the Web search engines address only part of the overall problem, viz., producing only a list of relevant sources. In this paper, we present ClaimEval, a novel and integrated approach which given a set of claims to vali- date, extracts a set of pro and con arguments from the Web in- formation sources, and jointly estimates credibility of sources and correctness of claims. ClaimEval uses Probabilistic Soft Logic (PSL), resulting in a flexible and principled framework which makes it easy to state and incorporate different forms of prior-knowledge. Through extensive experiments on real- world datasets, we demonstrate ClaimEval’s capability in de- termining validity of a set of claims, resulting in improved accuracy compared to state-of-the-art baselines.

Learning Task Knowledge from Dialog and Web Access

Vittorio Perera, Robin Soetens, Thomas Kollar, Mehdi Samadi, Yichao Sun, Daniele Nardi, René van de Molengraft, Manuela M. Veloso
Journal Paper Journal of Robotics, Volume 4, Number 2, 2015, Pages 223-252

Abstract

We present KnoWDiaL, an approach for Learning and using task-relevant Knowledge from human-robot Dialog and access to the Web. KnoWDiaL assumes that there is an autonomous agent that performs tasks, as requested by humans through speech. The agent needs to “understand” the request, (i.e., to fully ground the task until it can proceed to plan for and execute it). KnoWDiaL contributes such understanding by using and updating a Knowledge Base, by dialoguing with the user, and by accessing the web. We believe that KnoWDiaL, as we present it, can be applied to general autonomous agents. However, we focus on our work with our autonomous collaborative robot, CoBot, which executes service tasks in a building, moving around and transporting objects between locations. Hence, the knowledge acquired and accessed consists of groundings of language to robot actions, and building locations, persons, and objects. KnoWDiaL handles the interpretation of voice commands, is robust regarding speech recognition errors, and is able to learn commands involving referring expressions in an open domain, (i.e., without requiring a lexicon). We present in detail the multiple components of KnoWDiaL, namely a frame-semantic parser, a probabilistic grounding model, a web-based predicate evaluator, a dialog manager, and the weighted predicate-based Knowledge Base. We illustrate the knowledge access and updates from the dialog and Web access, through detailed and complete examples. We further evaluate the correctness of the predicate instances learned into the Knowledge Base, and show the increase in dialog efficiency as a function of the number of interactions. We have extensively and successfully used KnoWDiaL in CoBot dialoguing and accessing the Web, and extract a few corresponding example sequences from captured videos.

Never-Ending Learning

Tom M. Mitchell, William W. Cohen, Estevam R. Hruschka Jr., Partha Pratim Talukdar, Justin Betteridge, Andrew Carlson, Bhavana Dalvi Mishra, Matthew Gardner, Bryan Kisiel, Jayant Krishnamurthy, Ni Lao, Kathryn Mazaitis, Thahir Mohamed, Ndapandula Nakashole, Emmanouil Antonios Platanios, Alan Ritter, Mehdi Samadi , Burr Settles, Richard C. Wang, Derry Tanti Wijaya, Abhinav Gupta, Xinlei Chen, Abulhair Saparov, Malcolm Greaves, Joel Welling
Conference Papers Association for the Advancement of Artificial Intelligence(AAAI) 2015, Pages 2302-2310

Abstract

Whereas people learn many different types of knowledge from diverse experiences over many years, most current machine learning systems acquire just a single function or data model from just a single data set. We propose a never-ending learning paradigm for machine learning, to better reflect the more ambitious and encompassing type of learning performed by humans. As a case study, we describe the Never-Ending Language Learner (NELL), which achieves some of the desired properties of a never-ending learner, and we discuss lessons learned. NELL has been learning to read the web 24 hours/day since January 2010, and so far has acquired a knowledge base with over 80 million confidence-weighted beliefs (e.g., servedWith(tea, biscuits)). NELL has also learned millions of features and parameters that enable it to read these beliefs from the web. Additionally, it has learned to reason over these beliefs to infer new beliefs, and is able to extend its ontology by synthesizing new relational predicates. NELL can be tracked online at http://rtw.ml.cmu.edu, and followed on Twitter at @CMUNELL.

AskWorld: Budget-Sensitive Query Evaluation for Knowledge-on-Demand

Mehdi Samadi, Partha Pratim Talukdar, Manuela M. Veloso, Tom M. Mitchell
Conference Papers International Join Conference on Artificial Intelligence (IJCAI) 2015, Pages 837-843

Abstract

Recently, several Web-scale knowledge harvesting systems have been built, each of which is competent at extracting information from certain types of data (e.g., unstructured text, structured tables on the web, etc.). In order to determine the response to a new query posed to such systems (e.g., is sugar a healthy food?), it is useful to integrate opinions from multiple systems. If a response is desired within a specific time budget (e.g., in less than 2 seconds), then maybe only a subset of these resources can be queried. In this paper, we address the problem of knowledge integration for on-demand time-budgeted query answering. We propose a new method, AskWorld, which learns a policy that chooses which queries to send to which resources, by accommodating varying budget constraints that are available only at query (test) time. Through extensive experiments on real world datasets, we demonstrate AskWorld's capability in selecting most informative resources to query within test-time constraints, resulting in improved performance compared to competitive baselines.

OpenEval: Web Information Query Evaluation

Mehdi SamadiManuela Veloso, Manuel Blum
Conference Papers Association for the Advancement of Artificial Intelligence (AAAI) 2013

Abstract

In this paper, we investigate information validation tasks that are initiated as queries from either automated agents or humans. We introduce OpenEval, a new online information validation technique, which uses information on the web to automatically evaluate the truth of queries that are stated as multi-argument predicate instances (e.g., DrugHasSideEffect(Aspirin,GI Bleeding)). OpenEval gets a small number of instances of a predicate as seed positive examples and automatically learns how to evaluate the truth of a new predicate instance by querying the web and processing the retrieved unstructured web pages. We show that OpenEval is able to respond to the queries within a limited amount of time while also achieving high F1 score. In addition, we show that the accuracy of responses provided by OpenEval is increased as more time is given for evaluation. We have extensively tested our model and shown empirical results that illustrate the effectiveness of our approach compared to related techniques.

CMUML System for KBP 2013 Slot Filling

Bryan Kisiel, Justin Betteridge, Matt Gardner, Jayant Krishnamurthy, Ndapa Nakashole, Mehdi Samadi , Partha Pratim Talukdar, Derry Tanti Wijaya, Tom M. Mitchell
Conference Papers TAC 2013

Abstract

In this paper, we present an overview of the CMUML system for KBP 2013 English Slot Filling (SF) task. The system used a combination of distant supervision, stacked generalization and CRF-based structured prediction. Recently available anchor text data was also used for better entity matching. The system takes a modular approach so that independently developed semantic annotators can be effectively integrated without needing target ontology-specific retraining. While precision can of course be improved, the system turned out to be particularly conservative in its predictions resulting in lower recall. In addition to the main submission, we also made publicly available1 automatically tagged semantic categories of about 13 million noun phrases extracted from the KBP 2013 source corpus.

Using the Web to Interactively Learn to Find Objects

Mehdi Samadi, Thomas Kollar, Manuela M. Veloso
Conference Papers Association for the Advancement of Artificial Intelligence(AAAI) 2013

Abstract

In order for robots to intelligently perform tasks with humans, they must be able to access a broad set of background knowledge about the environments in which they operate. Unlike other approaches, which tend to manually define the knowledge of the robot, our approach enables robots to actively query the World Wide Web (WWW) to learn background knowledge about the physical environment. We show that our approach is able to search the Web to infer the probability that an object, such as a "coffee,'' can be found in a location, such as a "kitchen.'' Our approach, called ObjectEval, is able to dynamically instantiate a utility function using this probability, enabling robots to find arbitrary objects in indoor environments. Our experimental results show that the interactive version of ObjectEval visits 28% fewer locations than the version trained offline and 71% fewer locations than a baseline approach which uses no background knowledge.

Enabling robots to find and fetch objects by querying the web

Thomas Kollar, Mehdi Samadi, Manuela M. Veloso
Conference Papers Association for the Advancement of Artificial Intelligence (AAAI) 2013

Abstract

This paper describes an algorithm that enables a mobile robot to find an arbitrary object and take it to a destination location. Previous approaches have been able to search for a fixed set of objects. In contrast, our approach is able to dynamically construct a cost function to find any object by querying the web. The performance of our approach has been evaluated in a realistic simulator, and has been demonstrated on a companion robot, which can successfully execute plans such as finding a “coffee” and taking it to a destination location like, “Gates-Hillman Center, Room 7002.”

CoBots: Collaborative robots servicing multi-floor buildings

Manuela M. Veloso, Joydeep Biswas, Brian Coltin, Stephanie Rosenthal, Thomas Kollar, Çetin Meriçli, Mehdi Samadi, Susana Brandão, Rodrigo Ventura
Conference Papers International Conference on Intelligent Robots and Systems (IROS) 2012

Abstract

In this video we briefly illustrate the progress and contributions made with our mobile, indoor, service robots CoBots (Collaborative Robots), since their creation in 2009. Many researchers, present authors included, aim for autonomous mobile robots that robustly perform service tasks for humans in our indoor environments. The efforts towards this goal have been numerous and successful, and we build upon them. However, there are clearly many research challenges remaining until we can experience intelligent mobile robots that are fully functional and capable in our human environments.

Degrees of Separation in Social Networks

Reza Bakhshandeh, Mehdi Samadi, Zohreh Azimifar, Jonathan Schaeffer:
Conference Papers International Symposium on Combinatorial Search (SOCS) 2011

Abstract

Social networks play an increasingly important role in today's society. Special characteristics of these networks make them challenging domains for the search community. In particular, social networks of users can be viewed as search graphs of nodes, where the cost of obtaining information about a node can be very high. This paper addresses the search problem of identifying the degree of separation between two users. New search techniques are introduced to provide optimal or near-optimal solutions. The experiments are performed using Twitter, and they show an improvement of several orders of magnitude over greedy approaches. Our optimal algorithm finds an average degree of separation of 3.43 between two random Twitter users, requiring an average of only 67 requests for information over the Internet to Twitter. A near-optimal solution of length 3.88 can be found by making an average of 13.3 requests.

Extending the Applicability of Pattern and Endgame Databases

Mehdi Samadi, Fatemeh Torabi Asr, Jonathan Schaeffer, Zohreh Azimifar
Journal Paper IEEE Transactions on Computational Intelligence and AI in Games, Volume 1, Issue 1, April 2009, Pages: 28-38

Abstract

Social networks play an increasingly important role in today's society. Special characteristics of these networks make them challenging domains for the search community. In particular, social networks of users can be viewed as search graphs of nodes, where the cost of obtaining information about a node can be very high. This paper addresses the search problem of identifying the degree of separation between two users. New search techniques are introduced to provide optimal or near-optimal solutions. The experiments are performed using Twitter, and they show an improvement of several orders of magnitude over greedy approaches. Our optimal algorithm finds an average degree of separation of 3.43 between two random Twitter users, requiring an average of only 67 requests for information over the Internet to Twitter. A near-optimal solution of length 3.88 can be found by making an average of 13.3 requests.

Learning from Multiple Heuristics

Mehdi Samadi, Ariel Felner, Jonathan Schaeffer
Conference Papers Association for the Advancement of Artificial Intelligence (AAAI) 2008

Abstract

Heuristic functions for single-agent search applications estimate the cost of the optimal solution. When multiple heuristics exist, taking their maximum is an effective way to combine them. A new technique is introduced for combining multiple heuristic values. Inspired by the evaluation functions used in two-player games, the different heuristics in a single-agent application are treated as features of the problem domain. An ANN is used to combine these features into a single heuristic value. This idea has been implemented for the sliding-tile puzzle and the 4-peg Towers of Hanoi, two classic single-agent search domains. Experimental results show that this technique can lead to a large reduction in the search effort at a small cost in the quality of the solution obtained.

Compressing Pattern Databases with Learning

Mehdi Samadi, Maryam Siabani, Ariel Felner, Robert Holte
Conference Papers European Conference on Artificial Intelligence (ECAI) 2008

Abstract

A pattern database (PDB) is a heuristic function implemented as a lookup table. It stores the lengths of optimal solutions for instances of subproblems. Most previous PDBs had a distinct entry in the table for each subproblem instance. In this paper we apply learning techniques to compress PDBs by using neural networks and decision trees thereby reducing the amount of memory needed. Experiments on the sliding tile puzzles and the TopSpin puzzle show that our compressed PDBs significantly outperforms both uncompressed PDBs as well as previous compressing methods. Our full compressing system reduced the size of memory needed by a factor of up to 63 at a cost of no more than a factor of 2 in the search effort.

Using abstraction in Two-Player Games

Mehdi Samadi, Jonathan Schaeffer, Fatemeh Torabi Asr, Majid Samar, Zohreh Azimifar
Conference Papers European Conference on Artificial Intelligence (ECAI) 2008

Abstract

For most high-performance two-player game programs, a significant amount of time is devoted to developing the evaluation function. An important issue in this regard is how to take advantage of a large memory. For some two-player games, endgame databases have been an effective way of reducing search effort and introducing accurate values into the search. For some one-player games (puzzles), pattern databases have been effective at improving the quality of the heuristic values used in a search.

This paper presents a new approach to using endgame and pattern databases to assist in constructing an evaluation function for two-player games. Via abstraction, single-agent pattern databases are applied to two-player games. Positions in endgame databases are viewed as an abstraction of more complicated positions; database lookups are used as evaluation function features. These ideas are illustrated using Chinese checkers and chess. For each domain, even small databases can be used to produce strong game play. This research has relevance to the recent interest in building general game-playing programs. For two-player applications where pattern and/or endgame databases can be built, abstraction can be used to automatically construct an evaluation function.

Learning: An Effective Approach in Endgame Chess Board Evaluation

Mehdi Samadi, Zohreh Azimifar, Mansoor Zolghadri Jahromi
Conference Papers International Conference on Machine Learning and Applications (ICMLA) 2007

Abstract

Classical chess engines exhaustively explore moving possibilities from a chess board position to decide what the next best move to play is. The main component of a chess engine is board evaluation function. In this article we present a new method to solve chess endgames optimally without using brute-force algorithms or endgame tables. We propose to use artificial neural network to obtain better evaluation function for endgame positions. This method is specifically applied to three classical endgames: king-bishop-bishop-king, king-rook-king, and king-queen-king. The empirical results show that the proposed learning strategy is effective in wining against an opponent who offers its best survival defense using Nalimov database of best endgame moves.