SIKS Course in Learning, Reasoning and Information Retrieval 21 - 24 May 2007 at Vucht

Author: Alia

see also: Michiel's summary

Attendees:  Alia, Michiel, Anna Tordai

Monday

Learning aspect in analysis and modeling of cognitive processes by Cartholijn Jonker (TUD)

In this course, Catholijn gives an interesting insight on her work on how to mimic animal (and human) learning behavior for modeling agents learning style. She discusses intelligent behavior, pattern behaviors, different types of behavior, and shared extended mind.

Additional notes: 

Different types of learning mechanism (which can be modeled for agents):

Note: * requires some knowledge

What is the goal of understanding and mimic the learning mechanism: mind - extended mind - shared extended mind (as a collective)

Relation to my research: is there a way to model the learning behavior of people searching through the internet e.g. using parts of the "Information Foraging Theory" model from Peter Pirolli (see also his chi07 workshop paper: Towards a Tool for Predicting User Exploration ). If we can model and predict this behavior correctly then this should be the first step before trying to make adaptive content and interface.

Reinforcement Learning for Intelligent Agents by Martjin van Otterlo (UT)

Martijn talks about how to model learning for agents based on Optimizing Control Theory, Operation research and planning research. Main idea: reward (and punishment) play for reinforcement learning.

Keywords (from the slides): Markov decision process, Bellman functions, Decision-theoretic planning (DTP), Reinforcement Learning (RL), Policy evaluation, Policy iteration, value iteration, exploration vs. exploitation, monte-carlo sampling, temporal-difference learning, Q-learning,

The more state spaces the environment, the more difficult it is to apply the exploration strategy.

Argumentation by Gerard Vreeswijk (UU)

Gerard gave an introduction to modeling argumentation. Reminds me of the Toulmin model a lot. One thing that sticks to my mind is that you can model an agent to pick a good argumentation strategy so that it can win a negotiation. (very much like the real life)

Relation to my research: Not sure. But it is nice to hear the lecture.

Tuesday

Qualitative Reasoning 1 & 2 by Bert Bredeweg (UvA)

Bert discusses his work on qualitative reasoning applied in two domains: physics law and ecology. Even though qualitative reasoning will never replace numerical methods which is a vital tool for scientist there are several advantage of applying qualitative reasoning: 1.before applying numerical methods to get a feel of the result (more like quick and dirty prototyping), 2. as a tool for learning, 3. qualitative reasoning can be applied when the exact values (e.g. consonant) are unknown. 4. Bert also claims that qualitative reasoning can be applied as an argumentative tool.

I think the optimal solutions would be some kind of hybrid system e.g. to apply qualitative reasoning on top of numerical methods. So, scientist can still get the approximate value which they need but can do some kind of reasoning on top of it.

Implications to my research: can I use the GARP3 tool to model and predict user searching behavior?

Note: no slides available, copyright issues.

Meta-heuristic search for combinatorial Optimization by Dirk Thierens (UU)

No further comment. 

Probabilitstic Reasoning by Peter Lucas (RUN)

Relation to my research: Very interesting for predicition, need to check in detail on how to use probabilistic reasoning to predict information seeking task mode.

Automated Reasoning by Stefan Schloback (VU)

Good introductory discussion about description logic. He talked about his research on automatically simplifying long logic equation enough so it can be solved in fewer combination trials.

Relation to my research: Not very related.

Wednesday

Formal models for Information Retrieval 1 and 2 by Djoerd Hiemstra (UT)

Nice overview about most well known IR model. Djoerd mentioned that there is a summer school on information retrieval every year (ESSIR).

Note: most well known IR model: boolean model, doc similarity, vector space model, probabilistic retrieval, language models, google pagerank.

Keyword: Relevant feedback algorithm by Rocchio.

Relevance to my research: Can I customize the probability ranking according to the different types of users information seeking needs?

Language models and Multimedia Information Retrieval by Arjen de Vries (CWI)

Arjen talks about his phd thesis and current work with Thijs. Good references at the end of the slides.
Relation to my thesis: learn the user evaluation methods from IR and see where it can be translated for SW.

Language models: applications (topic detection, cross language) by Wessel Kraai (TNO).

Good intro on language models. Note to self: need to read again about the theories.

Keyword: topical relevance ranking

Thursday

The information market by Bas van Gils (RUN)

No further comment. His presentation is about his phd thesis, a framework on information retrieval brokering.

Question answering by Maarten de Rijke (UvA) and David Ahn

Introduction on question and answering retrieval. Interesting work, but as David pointed out there is still a lot of noise in solving the QA problem in the real WWW.

Query modification 1 and 2 by Theo van der Weide (RUN)

The content is the same as Djoerd talk, but more on the mathematics.