We show that without using any domain knowledge, we can predict the final performance of a team of voting agents, at any step towards solving a complex problem. This demo allows users to interact with our system, and observe its predictions, while playing 9x9 Go

Nagarajan, Vaishnavh

Soriano Marcolino, Leandro

Tambe, Milind

Lancaster E-Prints

Every Team Deserves a Second Chance:An Interactive 9x9 Go Experience(Demonstration)Leandro Soriano Marcolino1, Vaishnavh Nagarajan2, Milind Tambe11 University of Southern California, Los Angeles, CA, 90089, USA{sorianom, tambe}@usc.edu2 Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, Indiavaish@cse.iitm.ac.inABSTRACTWe show that without using any domain knowledge, we can predictthe final performance of a team of voting agents, at any step towardssolving a complex problem. This demo allows users to interact withour system, and observe its predictions, while playing 9x9 Go.Categories and Subject DescriptorsI.2.1 [Artificial Intelligence]: Applications and Expert SystemsKeywordsTeamwork; Single and multiagent learning; Social choice1. INTRODUCTIONIt is well known that aggregating the opinions of different agentscan lead to a great performance when solving complex problems.For example, voting has been extensively used to improve the per-formance in machine learning, crowdsourcing, and even board games.Besides, it is an aggregation technique that does not depend on anydomain, being very suited for wide applicability. However, a teamof voting agents will not always be successful in problem-solving.It is fundamental, therefore, to be able to quickly assess the per-formance of teams, so that a system operator can take actions torecover the situation in time.Current multi-agent systems works focus on identifying faultybehavior, or verifying correctness [2, 1]. Such approaches are ableto identify if a system is not correct, but provide no help if a correctsystem of agents is failing to solve a complex problem. Other worksfocus on team analysis, but require domain knowledge [5, 6].In this AAMAS conference, we present a novel method to pre-dict the final performance (success or failure) of a team of vot-ing agents, without using any domain knowledge [3]. Hence, ourmethod can be easily applied in a great variety of scenarios. More-over, our approach can be quickly applied online at any step ofthe problem-solving process, allowing a system operator to identifywhen the performance of a team presents issues. This can be fun-damental in many applications. For example, consider a complexproblem being solved in a cluster of computers. It is undesirableto allocate more resources than necessary, but if we notice that aAppears in: Proceedings of the 14th InternationalConference on Autonomous Agents and MultiagentSystems (AAMAS 2015), Bordini, Elkind, Weiss, Yolum(eds.), May 4–8, 2015, Istanbul, Turkey.Copyright c© 2015, International Foundation for Autonomous Agentsand Multiagent Systems (www.ifaamas.org). All rights reserved.team is failing in problem solving, we can increase the allocationof resources. Or consider a team playing together a game against anopponent (such as board games, or poker). Different teams mightplay better against different opponents. Hence, if we notice that ateam is having issues, we could dynamically change it. Under timeconstraints, however, such prediction must be done quickly.Our approach is based on a prediction model derived from agraphical representation of the problem-solving process, where thefinal outcome is modeled as a random variable that is influenced bythe subsets of agents that agreed together over the actions taken ateach step towards solving the problem. Hence, our representationhas no dependency on the domain.In this demo, the user will be able to interact with our system,while playing 9x9 Go games against a team of agents. The agentsvote together at each turn of the game, and our prediction systemruns in real time to show the team’s probability of victory. Hence,the user can compare her own estimations with the ones given byour system, and will learn about our work while being entertainedby playing a game. Our demo also allows a team to play against anartificial opponent, so that users that do not want to interact with thesystem can still learn about it and observe its predictions. A videoshowing our demo is at http://youtu.be/O3uDQCK9tNs.2. PREDICTION METHODWe briefly present here our prediction methodology. We referthe reader to [3] for a more complete description, and for a theoret-ical explanation of why the method works. We consider scenarioswhere agents vote at every step of a complex problem, in order totake common decisions towards problem-solving. Hence, let T bea set of agents ti, A a set of actions aj and S a set of world statessk. The agents vote for an action at each world state, and the teamtakes the action decided by plurality voting. The team obtains afinal reward r upon completing all world states. We assume twopossible final rewards: “success” (1) or “failure” (0). We define theprediction problem as follows: without using any knowledge of thedomain, identify the final reward that will be received by a team.This prediction must be executable at any world state, allowing anoperator to take remedy procedures in time.The main idea of our algorithm is to learn a prediction function,given the frequencies of agreements of all possible agent subsetsover the chosen actions. Let P(T) = {T1,T2, . . .} be the powerset of the set of agents, ai the action chosen in world state sj andHj ⊆ T the subset of agents that agreed on ai in that world state.Consider the feature vector ~x = (x1, x2, . . .) computed at worldstate sj , where each dimension (feature) has a one-to-one mappingwith P(T). We define xi as the proportion of times that the chosenaction was agreed upon by the subset of agents Ti. That is, xi =∑|Sj|k=1I(Hk=Ti)|Sj| , where I is the indicator function and Sj ⊆ S isthe set of world states from s1 to the current world state sj .Hence, given a set X˜, where for each feature vector ~xt ∈ X˜we have the associated reward rt, we can estimate a function, fˆ ,that returns an estimated reward rˆ (0 ≤ rˆ ≤ 1) given an input ~x.We classify estimated rewards above 0.5 as “success”, and below0.5 as “failure”. In order to learn the classification model, we usethe features at the final world state, but the learned function can beevaluated at any stage. We use classification by logistic regression,which models fˆ as fˆ(~x) = 11+e−(α+~βT~x).3. DEMONSTRATIONIn this demo we show the predictions given by our methodol-ogy in real time, in 9x9 Go games. Go is a turn-based board gamebetween two players. At each turn, the players place a stone inan empty intersection of the board. If a group of stones is sur-rounded by the opponent’s stones, they are removed. The stonesthat surround an area form a territory, whose value is counted bythe number of empty intersections inside. The final score is thetotal amount of territory minus the number of captured stones. Adetailed description of the rules can be found in [4]. Go is a verypopular game, especially in Asian cultures, and it is currently oneof the greatest challenges for Artificial Intelligence.Our system has four different software: Fuego 1.1, GnuGo 3.8,Pachi 9.01, MoGo 4, and two variants of Fuego, in a total of 6 dif-ferent agents. These are all publicly available Go software. Theagents are represented by “cartoon” characters, and the user playsa game in a graphical interface, by clicking in the position whereshe wants to play. When it is our system’s turn, the program dis-plays the votes of all agents in the board, by showing the respectivecharacter in the position where the agent voted for (Figure 1(a)).However, our demo goes beyond playing a game against a teamof agents. Our main objective is to demonstrate our team perfor-mance prediction methodology. In a separate screen (Figure 1(b)),we show in real time our estimated probability of the team winningthe game. Such predictions are shown in a graph, allowing the userto observe how the probability dynamically changes at each turn.This allows the user to compare our predictions with her own, be-sides demonstrating that our method can be executed in real time.Moreover, we also show the frequency that each subset of agentsagreed on the final chosen action. We display the subsets in a clearmanner by showing the corresponding characters close together(for additional clarity, we do not display subsets whose frequencyis 0 or very close to 0 in comparison to the other subsets). Thisemphasizes the fact that we use only this information to make aprediction, and that our method is completely domain independent.Moreover, it allows the user to make her own predictions based onthe displayed frequencies. Finally, in the end of the game, the sys-tem scores the result, and the user is able to verify our predictions.Since in our work we compare the quality of our predictions fordifferent kinds of teams [3], in this demo we allow two differentkinds: “diverse”, composed of one copy of each agent; and “uni-form”, composed of multiple copies of Fuego (with different ran-dom seeds). The demo also allows a team to play against an ar-tificial agent (while still showing our predictions). This feature isuseful to continually display our demo while no user is interactingwith it, in order to attract users to play an actual game and learnabout our methodology. We will also have pamphlets to teach thebasic rules of Go for users that are unfamiliar with the game.Acknowledgments: This research was supported by MURI grantW911NF-11-1-0332, and by IUSSTF.(a) User plays 9x9 Go games against a team ofvoting agents. Characters display the positionswhere each agent voted for.(b) Our predictions are displayed in real time foreach turn of the game. We also show the fre-quency that each subset of agents picks the finalchosen action by the team.Figure 1: In our demo a user plays Go against a team of agents,while observing our real time predictions about the final outcome.REFERENCES[1] T. T. Doan, Y. Yao, N. Alechina, and B. Logan. Verifyingheterogeneous multi-agent programs. In AAMAS, 2014.[2] E. Khalastchi, M. Kalech, and L. Rokach. A hybrid approachfor fault detection in autonomous physical agents. In AAMAS,2014.[3] V. Nagarajan, L. S. Marcolino, and M. Tambe. Every teamdeserves a second chance: Identifying when things go wrong.In AAMAS, 2015.[4] Pandanet. Introduction to Go. http://www.pandanet.co.jp/English/introduction_of_go/.[5] T. Raines, M. Tambe, and S. Marsella. Automated assistantsto aid humans in understanding team behaviors. In AGENTS,2000.[6] F. Ramos and H. Ayanegui. Discovering tactical behaviorpatterns supported by topological structures in soccer-agentdomains. In AAMAS, 2008.

Every team deserves a second chance : An Interactive 9x9 Go Experience (Demonstration)

http://eprints.lancs.ac.uk/81465/1/aamas15Demo.pdf

Every team deserves a second chance:An Interactive 9x9 Go Experience (Demonstration)

Abstract

Similar works

Full text

Available Versions

Lancaster E-Prints