laws of bridge game

http://www.acbl.org/acbl-content/wp-content/uploads/2014/01/Laws-of-Duplicate-Bridge.pdf

top computer bridge players

http://web.mit.edu/mitdlbc/www/articles/Bridge_Playing_Software_Review.pdf

Strategic thinking

Any strategy gives a player a power, a certain control over the outcomes of the game.

bounded rationality

too much information sometimes overloads cognitive activities and leads to poor choice in decision making


Ideally we want to be rational and well-informed decision makers ,

Rational : logical(clear and soundly reasoned) decisions
               objective

well informed decision making : we have good idea of our choices and their consequences.


but in real world scenarios like bridge game playing we encounter bounded rationality and imperfect infeormation

Higher level cognition for Bidding in Bridge :)

What makes an agent intelligent to make perfect bid ?

Adaptive laerning of oppnent's moves to tighten or loosen our tactics

http://www.sciencedirect.com/science/article/pii/S0167642307000548


here is good reference on this matter

jaakko Hintikka and seaul kripke

http://www.tark.org/proceedings/tark_mar19_86/p63-hintikka.pdf

in this paper it was told that "Knowledge eliminates uncertainty"

whether it could be for agents or humans searching for certainty in their world :D

#Epistemic Model logic
#uncertainty in World
#Incompete domain knowlegde
#multi agent systems
#games and strategies

whats understood can be explained :D

a sign of intelligence :D

Computer brigde and human analysis

meta cognitive support



here is an interesting article about current computer bridge games
http://will-bridge.us/bridge/bridge-artificial-intelligence.htm

General game playing in AI

as we started out with Knowledge representation and reasoning our approach to realize a general game player or an intelligent meta gamer would be totally knowledge based where as other approach would be knowledge free which is out of my league.

A study of Game AI
Features of Knowledge based GGP.

Event calculus for games

there are games with have uncertainity issues
like frame problem i.e, agent is not aware of whole domain


which deal will lot of random ness..
like shuffling the cards
rolling the dice etc
 

and there will be series of dynamic events which are totally occured by other agents or by nature

so how to program problem solving intelligence for such game domains

report#7 Soar Cognitive Architecture our focus

can intelligence be programmed in soar?
how reasoning is done? forward searching,matching,retrival
what representations it can support? Production rules
is there any programming apis?
is learning possible in soar?
can we incorporate dynamic worlds
can we incorporate meta reasoning
can we simulate epistemic reasoning
can we simulate multi agent scenario
can we address classical frame problem ...using temporal dynamic probabilistic epistemic logic reasoning



Allen Newell, in his book, Unified Theories of Cognition, urges the AI and cognitive science communities to endeavor to develop unifying theories for cognition.

Cognitive behaviors of concern are:

• problem solving, decision making, and routine action
• memory, learning, and skill
• perception and motor behavior
• language
• motivation and emotion
• imagining, dreaming, daydreaming, etc.  

Newell has proposed Soar as a candidate unified theory of cognition. Soar is a collection of mutually exclusive mechanisms that combine to produce a system that has been shown to be applicable to a wide array of AI (eg: planning, control, learning) and cognitive modeling (eg:power law, reaction times) tasks.

Contact bridge : a card game

aims:
 to be implemented using a cognitive architecture: SOAR
 a MultiAgent system
Interactive system
beginners can learn from explanations that agent provides for its actions.
Meta cognitive agents to give tough competition to experts.(Expert mode).



too much uncertainity
how to deal with probability

how to predict who have what and who knows what


how to build you strategies to know what others poses and break others move

Report#6 meta cognition

Report#5 a study on cognitive architectures

reprt#4 Multi agent systems

Reprt #3 Intelligent tutoring systems

requirements and features and estimated capabilities of such cognitive and interactive teaching assistants

Report#2 knowledge representation and reasoning symbollic AI

reference

AFCAI dk

KRR brachman

Readings in KRR

conceptual graphs -jf sowa



different formalisms for representing and organizing the acquired knowledge

and reasoning techniques to work on such knowledge


here we have to give some strong representation technique and reasoning technique

Report #1: Conitive models for problem solving

problem solving models ref:Sandra marshal's schemas in problem solving

symbolic
subsymbolic
connectionist
fuzzy connectionist
hybrid



Performing models
Learning models
hybrid models


concepts acuisition


ref:tom mitchell

Explanation based learning

reinforcement learning

artificial neural networks
prof:yegnanarayana .

Bayesian networks



Game theory :)