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Mehdi Samadi

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11 papers
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11

AAAI Conference 2016 Conference Paper

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

  • Mehdi Samadi
  • Partha Talukdar
  • Manuela Veloso
  • Manuel Blum

The World Wide Web (WWW) has become a rapidly growing 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 different 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 validate, extracts a set of pro and con arguments from the Web information 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 realworld datasets, we demonstrate ClaimEval’s capability in determining validity of a set of claims, resulting in improved accuracy compared to state-of-the-art baselines.

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IJCAI Conference 2015 Conference Paper

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

  • Mehdi Samadi
  • Partha Talukdar
  • Manuela Veloso
  • Tom Mitchell

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.

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AAAI Conference 2015 Conference Paper

Never-Ending Learning

  • Tom Mitchell
  • William Cohen
  • Estevam Hruschka
  • Partha Talukdar
  • Justin Betteridge
  • Andrew Carlson
  • Bhavana Dalvi Mishra
  • Matthew Gardner

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)), while learning continually to improve its reading competence over time. NELL has also learned to reason over its knowledge base to infer new beliefs from old ones, and is now beginning 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.

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AAAI Conference 2013 Conference Paper

OpenEval: Web Information Query Evaluation

  • Mehdi Samadi
  • Manuela Veloso
  • Manuel Blum

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 multiargument 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.

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IROS Conference 2012 Conference Paper

CoBots: Collaborative robots servicing multi-floor buildings

  • Manuela Veloso
  • Joydeep Biswas
  • Brian Coltin
  • Stephanie Rosenthal
  • Thomas Kollar
  • Çetin Meriçli
  • Mehdi Samadi
  • Susana Brandão

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.

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AAMAS Conference 2012 Conference Paper

Enabling Robots to Find and Fetch Objects by Querying the Web

  • Thomas Kollar
  • Mehdi Samadi
  • Manuela Veloso

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. ”

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AAAI Conference 2012 Conference Paper

Using the Web to Interactively Learn to Find Objects

  • Mehdi Samadi
  • Thomas Kollar
  • Manuela Veloso

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.

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ECAI Conference 2008 Conference Paper

Compressing Pattern Databases with Learning

  • Mehdi Samadi
  • Maryam Siabani
  • Ariel Felner
  • Robert C. Holte

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.

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AAAI Conference 2008 Conference Paper

Learning from Multiple Heuristics

  • Mehdi Samadi

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 singleagent 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.

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ECAI Conference 2008 Conference Paper

Using abstraction in Two-Player Games

  • Mehdi Samadi
  • Jonathan Schaeffer 0001
  • Fatemeh Torabi Asr
  • Majid Samar
  • Zohreh Azimifar

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.

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