29
Jun
Posted by cognitivecomputing in Language. Tagged: natural language. Leave a Comment
Textual Entailment Recognition is a semantic inference task that is required in many natural language processing (NLP) applications. In this paper, we present our system for the third PASCAL recognizing textual entailment (RTE-3) challenge. The system is built on a machine learning framework with the following features derived by state-of-the-art NLP techniques: lexical semantic similarity (LSS), named entities (NE), dependent content word pairs (DEP), average distance (DIST), negation (NG), task (TK), and text length (LEN).
On the RTE-3 test dataset, our system achieves the accuracy of 0.64 and 0.6488 for the two official submissions, respectively. Experimental results show that LSS and NE are the most effective features. Further analyses indicate that a baseline dummy system can achieve accuracy 0.545 on the RTE-3 test dataset, which makes RTE-3 relatively easier than RTE-2 and RTE-1. In addition, we demonstrate with examples that the current Average Precision measure and its evaluation process need to be changed.
Read the paper:
Machine Learning Based Semantic Inference: Experiments and Observations at RTE-3
by Baoli Li, Joseph Irwin, Ernie Garcia, Ashwin Ram
Association for Computational Linguistics (ACL) Challenge Workshop on Textual Entailment and Paraphrase (WTEP-07), Prague, Czech Republic, June 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-12.pdf
27
Jun
Posted by cognitivecomputing in Health & Wellness, Language, Learning, Web / Web 2.0. Tagged: healthcare, natural language, semantic memory. Leave a Comment
NLM’s Unified Medical Language System (UMLS) is a very large ontology of biomedical and health data. In order to be used effectively for knowledge processing, it needs to be customized to a specific domain. In this paper, we present techniques to automatically discover domain-specific concepts, discover relationships between these concepts, build a context map from these relationships, link these domain concepts with the best-matching concept identifiers in UMLS using our context map and UMLS concept trees, and finally assign categories to the discovered relationships. This specific domain ontology of terms and relationships using evidential information can serve as a basis for applications in analysis, reasoning and discovery of new relationships. We have automatically built an ontology for the Nuclear Cardiology domain as a testbed for our techniques.
Read the paper:
Domain Ontology Construction from Biomedical Text
by Saurav Sahay, Baoli Li, Ernie Garcia, Eugene Agichtein, Ashwin Ram
International Conference on Artificial Intelligence (ICAI-07), Las Vegas, NV, June 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-10.pdf
8
Jun
Posted by cognitivecomputing in Agents, Game AI, Language. Tagged: believable agents, games, interactive drama, natural language, virtual worlds. Leave a Comment
Natural Language Generation for personality rich characters represents one of the important directions for believable agents research. The typical approach to interactive NLG is to hand-author textual responses to different situations. In this paper we address NLG for interactive games. Specifically, we present a novel template-based system that provides two distinct advantages over existing systems. First, our system not only works for dialogue, but enables a character’s personality and emotional state to influence the feel of the utterance. Second, our templates are resuable across characters, thus decreasing the burden on the game author. We briefly describe our system and present results of a preliminary evaluation study.
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Emotionally Driven Natural Language Generation for Personality Rich Characters in Interactive Games
by Christina Strong, Kinshuk Mishra, Manish Mehta, Alistair Jones, Ashwin Ram
Third Conference on Artificial Intelligence for Interactive Digital Entertainment (AIIDE-07), Stanford, CA, June 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-09.pdf
8
Jun
Posted by cognitivecomputing in Game AI, Language. Tagged: games, interactive drama, natural language. Leave a Comment
A growing research community is working towards employing drama management components in story-based games that guide the story towards specific narrative arcs depending on a particular player’s playing patterns. Intuitively, player modeling should be a key component for Drama Manager (DM) based approaches to succeed with human players.
In this paper, we report a particular implementation of the DM component connected to an interactive story game, Anchorhead, while specifically focusing on the player modeling component. We analyze results from our evaluation study and show that similarity in the trace of DM decisions in previous games can be used to predict interestingness of game events for the current player. Results from our current analysis indicate that the average time spent in performing player actions provides a strong distinction between players with varying degrees of gaming experience, thereby helping the DM to adapt its strategy based on this information.
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Evaluating Player Modeling for a Drama Manager Based Interactive Fiction
by Manu Sharma, Manish Mehta, Santi Ontañón, Ashwin Ram
Third Conference on Artificial Intelligence for Interactive Digital Entertainment (AIIDE-07), Workshop on Player Satisfaction, Stanford, CA, June 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-08.pdf
9
May
Posted by cognitivecomputing in Game AI, Language. Tagged: case-based reasoning, games, interactive drama, planning. Leave a Comment
There is a growing interest in producing story based game experiences that do not follow fixed scripts pre-defined by the author, but change the experience based on actions performed by the player during his interaction. In order to achieve this objective, previous approaches have employed a drama management component that produces a narratively pleasing arc based on an author specified aesthetic value of a story, ignoring a player’s personal preference for that story path. Furthermore, previous approaches have used a simulated player model to assess their approach, ignoring real human players interacting with the story-based game.
This paper presents an approach that uses a case-based player preference modeling component that predicts an interestingness value for a particular plot point within the story. These interestingness values are based on real human players’ interactions with the story. We also present a drama manager that uses a search process (based on the expectimax algorithm) and combines the author specified aesthetic values with the player model.
Read the paper:
Towards Player Preference Modeling for Drama Management in Interactive Stories
by Manu Sharma, Santi Ontañón, Christina Strong, Manish Mehta, Ashwin Ram
20th International FLAIRS Conference on Artificial Intelligence (FLAIRS-07), Key West, FL, May 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-03.pdf
8
May
Posted by cognitivecomputing in Agents, Game AI, Learning, Talks. Tagged: believable agents, case-based reasoning, games, interactive drama, rts games. 1 Comment
Computer games are an increasingly popular application for Artificial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial games. Although games are typically associated with entertainment, there are many “serious” applications of gaming, including military, corporate, and advertising applications. There are also so-called “humane” gaming applications for medical training, educational games, and games that reflect social consciousness or advocate for a cause. Game AI is the effort of going beyond scripted interactions, however complex, into the arena of truly interactive systems that are responsive, adaptive, and intelligent. Such systems learn about the player(s) during game play, adapt their own behaviors beyond the pre-programmed set provided by the game author, and interactively develop and provide a richer experience to the player(s).
The long-term goal of our research is to develop artificial intelligence techniques that can have a significant impact in the game industry. We present a list of challenges and research opportunities in developing techniques that can be used by computer game developers. We discuss three Case Based Reasoning (CBR) approaches to achieve adaptability in games: automatic behavior adaptation for believable characters; drama management and user modeling for interactive stories; and strategic behavior planning for real-time strategy games.
Read the paper:
Artificial Intelligence for Adaptive Computer Games
by Ashwin Ram, Santi Ontañón, Manish Mehta
Invited talk at the 20th International FLAIRS Conference on Artificial Intelligence (FLAIRS-07), Special Track on Case-Based Reasoning, Key West, FL, May 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-04.pdf
8
May
Posted by cognitivecomputing in Health & Wellness, Language, Learning. Tagged: healthcare, natural language. Leave a Comment
We propose a semi-supervised method to extract rule sentences from medical abstracts. Medical rules are sentences that give interesting and non-trivial relationship between medical entities. Mining such medical rules is important since the rules thus extracted can be used as inputs to an expert system or in many more other ways. The technique we suggest is based on paraphrasing a set of seed sentences and populating a pattern dictionary of paraphrases of rules. We match the patterns against the new abstract and rank the sentences.
Read the paper:
Detecting Medical Rule Sentences with Semi-Automatically Derived Patterns: A Pilot Study
by Shreekanth Karvaje, Bharat Ravisekar, Baoli Li, Ernie Garcia, Ashwin Ram
International Symposium on Bioinformatics Research and Applications ( ISBRA-07), Atlanta, GA, May 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-07.pdf
1
May
Posted by cognitivecomputing in Agents, Learning. Tagged: case-based reasoning, multistrategy learning. Leave a Comment
We present an argumentation framework for learning agents (AMAL) designed for two purposes: (1) for joint deliberation, and (2) for learning from communication. The AMAL framework is completely based on learning from examples: the argument preference relation, the argument generation policy, and the counterargument generation policy are case-based techniques.
For joint deliberation, learning agents share their experience by forming a committee to decide upon some joint decision. We experimentally show that the argumentation among committees of agents improves both the individual and joint performance. For learning from communication, an agent engages into arguing with other agents in order to contrast its individual hypotheses and receive counterexamples; the argumentation process improves their learning
scope and individual performance.
Read the paper:
Learning and Joint Deliberation through Argumentation in Multi-Agent Systems
by Santi Ontañón and Enric Plaza
in Autonomous Agents and Multi-Agent Systems (AAMAS 2007), pp. 971-978
www.cc.gatech.edu/faculty/ashwin/papers/er-07-19.pdf
11
Jan
Posted by cognitivecomputing in Game AI, Learning. Tagged: case-based reasoning, games, planning, real-time cbr, rts games. 2 Comments
The goal of transfer learning is to use the knowledge acquired in a set of source tasks to improve performance in a related but previously unseen target task. In this paper, we present a multi-layered architecture named CAse-Based Reinforcement Learner (CARL). It uses a novel combination of Case-Based Reasoning (CBR) and Reinforcement Learning (RL) to achieve transfer while playing against the Game AI across a variety of scenarios in MadRTS, a commercial Real-Time Strategy game. Our experiments demonstrate that CARL not only performs well on individual tasks but also exhibits significant performance gains when allowed to transfer knowledge from previous tasks.
Read the paper:
Transfer Learning in Real-Time Strategy Games Using Hybrid CBR/RL
by Manu Sharma, Michael Holmes, Juan Santamaria, Arya Irani, Charles Isbell, Ashwin Ram
International Joint Conference on Artificial Intelligence (IJCAI-07), Hyderabad, India, January 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-01.pdf
8
Jan
Posted by cognitivecomputing in Agents, Game AI, Learning. Tagged: believable agents, games, interactive drama, meta-reasoning, planning, virtual worlds. 1 Comment
Typically, autonomous believable agents are implemented using static, hand-authored reactive behaviors or scripts. This hand-authoring allows designers to craft expressive behavior for characters, but can lead to excessive authorial burden, as well as result in characters that are brittle to changing world dynamics.
In this paper we present an approach for the runtime adaptation of reactive behaviors for autonomous believable characters. Extending transformational planning, our system allows autonomous characters to monitor and reason about their behavior execution, and to use this reasoning to dynamically rewrite their behaviors. In our evaluation, we transplant two characters in a sample tag game from the original world they were written for into a different one, resulting in behavior that violates the author intended personality. The reasoning layer successfully adapts the character’s behaviors so as to bring its long-term behavior back into agreement with its personality.
Towards Runtime Behavior Adaptation for Embodied Characters
by Peng Zang, Manish Mehta, Michael Mateas, Ashwin Ram
International Joint Conference on Artificial Intelligence (IJCAI-07), Hyderabad, India, January 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-02.pdf
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