Fuzzy Neural Agents for Online NBA Scouting Mourad Atlas and Yanqing Zhang

Outline Introduction Fuzzy Neural Web Intelligence Fuzzy Neural Web Agent for NBA Scouting Conclusion and Future Work

Introduction Due to the enormous amount of information on the Web, extracting useful and relevant information for the user is challenging. Using regular searching tools such as Yahoo or Google leads to retrieving irrelevant information and also miss important ones. Hence, there is a great demand for new tools to free the Internet user from the tedious task of selecting, acquiring, processing, or sending the appropriate information. Such new tools are called “Intelligent Software Agents”. We developed a service agent that can assist a human NBA scouting agent in following the performance of the players he/she is interested in by exploiting the Web potentialities.

How Human Scouting Agent Works He makes a profile sheet document of the player he has interest in. To rate the player, he includes in the document the following information: -Background (name, school, class, position played, etc) -Characteristics (leadership, game knowledge, physical and mental ability, etc) -Game skills (overall shooting, shot selection, decision making, free throw, three point shooting, shot blocking, alertness to the ball, aggressiveness, etc) -Personal skills (maturity, attitude, coachability, feel for the game, agility, strength, endurance, poise, etc).

How Human Scouting Agent Works(cont.) Difficulty for Scouting Agent - Making an opinion about a player game skills -Takes a lot of time - Watch a lot of games - Lots of traveling -Detachment from family Solution: Statistical analysis Short Coming: Not effective if statistics are not manipulated appropriately.

How The Artificial Scouting Agent Works First, the human scouting agents (clients) need to register themselves by providing their names, email addresses, list of players interested in (up to 10 players), and the best time to be reached When the games of the day are over, the agent will visit the relevant Websites using some provided URLs, get the content of the appropriate HTML pages, and parse them to extract the statistics of the players in question.

How The Artificial Scouting Agent Works (cont.) Once the players’ numbers of the day are available, the agent will judge the performance of each player using a fuzzy evaluator. Finally, the agent will dispatch the result by emailing its clients at their chosen time.

How The Artificial Scouting Agent Works (cont.) Also, the agent will be capable of doing prediction using a neuro-fuzzy predictor. The prediction will only be done on demand because it takes a long processing time. To request it, the clients just need the contact our agent by email. The clients are allowed to request any player statistics through email as well.

How The Artificial Scouting Agent Works (cont.) Services provided by the agent Last game statistics of a player Current season statistics of a player Next game prediction of a player statistics Next season prediction of a player statistics Game and season performance evaluation of a player

Computational Intelligence fuzzy computing (FC) neural computing (NC), evolutionary computing (EC), probabilistic computing (PC), granular computing (GrC) rough computing (RC). …

Web Technology a hybrid technology including computer networks, the Internet, wireless networks, databases, search engines, client-server, programming languages, Web-based software, security, agents, e-business systems, and other relevant techniques.

Fuzzy Neural Web Intelligence Uncertainty on the Web (FLINT 2001 at BISC at UC Berkeley http://www-bisc.cs.berkeley.edu/) FNWI = Fuzzy +Neural + WT (2002) FNWI is a hybrid technology of Fuzzy Logic, Neural Networks and Web Technology (WT) on wired and wireless networks.

Fuzzy Neural NBA Scouting Agent

Fuzzy Neural Online Learning The FNNKD consists of 5 layers. The functions of fuzzy neurons in different layers are described layer as follows: Layer 1: Input Layer Oi = xi Layer 2: Compensation and Linear Combination Layer OCk and OLk Layer 3: Normal Fuzzy Reasoning Layer Ok= OCk *OLk Layer 4: Summation Layer OSC = OCk , OSR = Ok Layer 5: Output Layer Oout = OSR / OSC

Fuzzy Neural Learning Suppose: Given n-dimensional input data vectors xp (i.e., xp = (x1p, x2p,……, xnp)) and one-dimensional output data vector yp for p=1, 2 ,..., N. The energy function is defined by For simplicity, let E and denote and , respectively.

Fuzzy Neural Learning (Cont.) Step 1: Begin. Step 2: Heuristic-Knowledge-Based Initialization of parameters. Sort for p = 1, 2, …, N in non-decreasing order, and change the order of corresponding and . Partition sorted xp and yp for p = 1, 2, …, N into m data segments, then calculate the heuristic initial values of parameters. Step 3: Adjust parameters and discover m fuzzy rules Step 4: Train FNNKD. After training all parameter for certain times, if Error ≤ MaxError then goto Step 5, otherwise, goto Step 3. Step 5: End.

Summary FNWI is a useful research area Fuzzy Neural Web Agents can be used only only in Sports but also in many e-Business applications

Future Work Expert System based on human knowledge New Dynamic Learning Mehtods HWI=AI+BI+CI+WT (Hybrid Web Intelligence= AI+Biological Intelligence and Computational Intelligence+WT) in A Newly Published Book: “Computational Web Intelligence for Intelligent Web Applications”, World Scientific, 2004.)