acl acl2011 acl2011-207 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: S.R.K Branavan ; David Silver ; Regina Barzilay
Abstract: This paper presents a novel approach for leveraging automatically extracted textual knowledge to improve the performance of control applications such as games. Our ultimate goal is to enrich a stochastic player with highlevel guidance expressed in text. Our model jointly learns to identify text that is relevant to a given game state in addition to learning game strategies guided by the selected text. Our method operates in the Monte-Carlo search framework, and learns both text analysis and game strategies based only on environment feedback. We apply our approach to the complex strategy game Civilization II using the official game manual as the text guide. Our results show that a linguistically-informed game-playing agent significantly outperforms its language-unaware counterpart, yielding a 27% absolute improvement and winning over 78% of games when playing against the built- . in AI of Civilization II. 1
Reference: text
sentIndex sentText sentNum sentScore
1 Our model jointly learns to identify text that is relevant to a given game state in addition to learning game strategies guided by the selected text. [sent-7, score-1.84]
2 Our method operates in the Monte-Carlo search framework, and learns both text analysis and game strategies based only on environment feedback. [sent-8, score-0.907]
3 We apply our approach to the complex strategy game Civilization II using the official game manual as the text guide. [sent-9, score-1.739]
4 This is an excerpt from the user manual of the game Civilization II. [sent-29, score-0.85]
5 2 This text describes game locations where the action “build-city” can be effectively applied. [sent-30, score-1.015]
6 A stochastic player that does not have access to this text would have to gain this knowledge the hard way: it would repeatedly attempt this action in a myriad of states, thereby learning the characterization of promising state-action pairs based on the observed game outcomes. [sent-31, score-1.14]
7 An algorithm with access to the text, however, could learn correlations between words in the text and game attributes e. [sent-33, score-0.878]
8 , the word “river” and places with rivers in the game thus leveraging strategies described in text to better select actions. [sent-35, score-0.861]
9 Since the game’s state space is extremely large, and the states that will be encountered during game play cannot be known a priori, it is impractical to manually annotate the information that would be relevant to those states. [sent-38, score-1.009]
10 Instead, we propose to learn text analysis based on a feedback signal inherent to the control application, such as game score. [sent-39, score-0.857]
11 Ac s2s0o1ci1a Atiosnso fcoirat Cioonm foprut Caotimonpaulta Lti nognuails Lti cnsg,u piasgteics 268–277, Our general setup consists of a game in a stochastic environment, where the goal of the player is to maximize a given utility function R(s) at state s. [sent-44, score-1.182]
12 An obvious way to enrich the model with textual information is to augment the action-value function with word features in addition to state and action features. [sent-48, score-0.401]
13 We test our method on the strategy game Civilization II, a notoriously challenging game with an immense action space. [sent-57, score-1.859]
14 3 As a source of knowledge for guiding our model, we use the official game manual. [sent-58, score-0.844]
15 In contrast, game manuals provide high-level advice but do not directly describe the correct actions for every potential game state. [sent-74, score-1.83]
16 The area of language analysis situated in a game domain has been studied in the past (Eisenstein et al. [sent-78, score-0.844]
17 Our goal is more open ended, in that we aim to learn winning game strategies. [sent-82, score-0.865]
18 (2009) rely on a different source of supervision game traces collected a priori. [sent-84, score-0.838]
19 For complex games, like the one considered in this paper, collecting such game traces is prohibitively expensive. [sent-85, score-0.838]
20 Game Representation The game is defined by a large Markov Decision Process hS, A, T, Ri . [sent-94, score-0.818]
21 Specifically, a state encodes attribu∈tes S So fa nthde a game world, iscuaclhly as available resources and city locations. [sent-97, score-0.925]
22 At each step of the game, a player executes an action a which causes the current state s to change to a new state s0 according to the transition function T(s0|s, a). [sent-98, score-0.594]
23 While this function is not known a priori, the|s program encoding the game can be viewed as a black box from which transitions can be sampled. [sent-99, score-0.894]
24 Finally, a given utility function R(s) ∈ R captures the likelaih goiovden o uft winning ttiohen game ∈fro Rm c satpattuer s (e. [sent-100, score-0.978]
25 This selection is based on the results of multiple roll-outs which measure the outcome of a sequence of actions in a simulated game e. [sent-104, score-0.993]
26 On game completion at time τ, we measure the final utility R(sτ). [sent-108, score-0.878]
27 5 The actual game action is then selected as the one corresponding to the roll-out with the best final utility. [sent-109, score-1.016]
28 The success of Monte-Carlo search is based on its ability to make a fast, local estimate of the ac5In general, roll-outs are run till game completion. [sent-111, score-0.818]
29 270 procedure PlayGame () Initialize game state to fixed starting state s1 ← s0 for t = 1. [sent-113, score-1.052]
30 States and actions are evaluated by an action-value function Q(s, a), which is an estimate of the expected outcome of action a in state s. [sent-121, score-0.473]
31 One successful approach is to model the action-value function as a linear combination of state and action attributes (Silver et al. [sent-127, score-0.398]
32 an action is selected uniformly at random; otherwise the action is selected greedily to maximize the current action-value function, arg maxa Q(s, a) . [sent-138, score-0.415]
33 1 Model Structure To inform action selection with the advice provided in game manuals, we modify the action-value function Q(s, a) to take into account words of the doc- ument in addition to state and action information. [sent-141, score-1.377]
34 A fixed, real-valued feature function x(s, a, d) transforms the game state s, action a, and strategy document d into the input vector The first hidden layer contains two disjoint sets of units and corresponding to linguistic analyzes of the strategy document. [sent-144, score-1.474]
35 The units of the second hidden layer f~(s, a, d, yi, zi) are a set of fixed real valued feature functions on s, a, d and the active units yi and zi of and z respectively. [sent-147, score-0.38]
36 The input layer represents the current state s, candidate action a, and document d. [sent-153, score-0.467]
37 Given this activation function, the second layer effectively models sentence relevance and predicate labeling decisions via log-linear distributions, the details of which are described below. [sent-158, score-0.482]
38 The third feature layer neural network is deterministically computed given the active units yi and zj of the softmax layers, and the values of the input layer. [sent-159, score-0.402]
39 Modeling Sentence Relevance Given a strategy document d, we wish to identify a sentence yi that is most relevant to the current game state st and action at. [sent-163, score-1.348]
40 272 Here ej is the predicate label of the jth word being labeled, and e1:j−1 is the partial predicate labeling constructed so far for sentence yi. [sent-175, score-0.356]
41 In the second layer of the neural network, the units z represent a predicate labeling ei of every sentence yi ∈ d. [sent-176, score-0.512]
42 Given the sentence selected as relevant and its predicate labeling, the output layer of the network can now explicitly learn the correlations between textual information, and game states and actions – for example, between the word “grassland” in Figure 1, and the action of building a city. [sent-180, score-1.57]
43 This allows our method to leverage the automatically extracted textual information to improve game play. [sent-181, score-0.881]
44 2 Parameter Estimation Learning in our method is performed in an online fashion: at each game state st, the algorithm performs a simulated game roll-out, observes the outcome of the game, and updates the parameters and of the action-value function Q(st, at, d). [sent-183, score-1.91]
45 These three steps are repeated a fixed number of times at each actual game state. [sent-184, score-0.869]
46 The information from these roll-outs is used to select the actual game action. [sent-185, score-0.818]
47 The algorithm re-learns Q(st, at, d) for every new game state st. [sent-186, score-0.925]
48 Specifically, we adjust the parameters by stochastic gradient descent, to minimize the mean-squared error between the actionvalue Q(s, a) and the final utility R(sτ) for each observed game state s and action a. [sent-189, score-1.249]
49 We use the official manual 6 of the game as the source of textual strategy advice for the language aware algorithms. [sent-195, score-1.027]
50 Civilization IIis a multi-player game set on a gridbased map of the world. [sent-196, score-0.818]
51 In our experiments, we consider a two-player game of Civilization II on a grid of 1000 squares, where we play against the built-in AI player. [sent-203, score-0.837]
52 – Game States and Actions We define the game state of Civilization IIto be the map of the world, the attributes of each map tile, and the attributes of each player’s cities and units. [sent-204, score-1.069]
53 The space of possible actions for a given city or unit is known given the current game state. [sent-206, score-0.975]
54 The actions of a player’s cities and units combine to form the action space of that player. [sent-207, score-0.373]
55 This results in a very large action space for the game i. [sent-209, score-0.996]
56 To effectively deal with this large action space, we assume that given the state, the actions of a single unit are independent of the actions of all other units of the same player. [sent-212, score-0.506]
57 kwdaeuhsrc,ladtxnpc,omraeluwnitcadhlef,r tc) and features computed using the game manual and these attributes (box below). [sent-220, score-0.91]
58 In the typical application of the algorithm, the final game outcome is used as the utility function (Tesauro and Galperin, 1996). [sent-222, score-0.96]
59 Given the complexity ofCivilization II, running simulation roll-outs until game completion is impractical. [sent-223, score-0.853]
60 The game, however, provides each player with a game score, which is a noisy indication of how well they are currently playing. [sent-224, score-0.927]
61 Since we are playing a two-player game, we use the ratio of the game score of the two players as our utility function. [sent-225, score-0.926]
62 Features The sentence relevance features φ~ and the f~ action-value function features consider the attributes of the game state and action, and the words of the sentence. [sent-226, score-1.155]
63 6 Experimental Setup f~, φ~ and Datasets We use the official game manual for Civilization IIas our strategy guide. [sent-232, score-0.921]
64 7 We instrument the game to allow our method to programmatically measure the current state of the game and to execute game actions. [sent-236, score-2.58]
65 , 2006) was used to generate the dependency parse information for sentences in the game manual. [sent-238, score-0.818]
66 Across all experiments, we start the game at the same initial state and run it for 100 steps. [sent-239, score-0.925]
67 Each roll-out is run for 20 simulated game steps before halting the simulation and evaluating the outcome. [sent-241, score-0.924]
68 In this setup, a single game of 100 steps runs in approximately 1. [sent-245, score-0.849]
69 Evaluation Metrics We wish to evaluate two aspects of our method: how well it leverages textual information to improve game play, and the ac- curacy of the linguistic analysis it produces. [sent-247, score-0.881]
70 Since full games can last for multiple days, we compute the percentage of games won within the first 100 game steps as our primary evaluation. [sent-251, score-1.259]
71 To confirm that performance under this evaluation is meaningful, we also compute the percentage of full games won over 50 independent runs, where each game is run to completion. [sent-252, score-1.046]
72 4571 Table 1: Win rate of our method and several baselines within the first 100 game steps, while playing against the built-in game AI. [sent-262, score-1.706]
73 All results are averaged across 200 independent game runs. [sent-265, score-0.818]
74 This evaluation is an underestimate since it assumes that any game not won within the first 100 steps is a loss. [sent-273, score-0.895]
75 It attempts to model the action value function Q(s, a) only in terms of the attributes of the game state and action. [sent-277, score-1.216]
76 The box below shows the predicted predicate structure of three sentences, with “S” indicating state description,“A” action description and background words unmarked. [sent-285, score-0.435]
77 7% of games, showing that while identifying the text relevant to the current game state is essential, a deeper structural analysis of the extracted text provides substantial benefits. [sent-289, score-0.98]
78 One possible explanation for the improved performance of our method is that the non-linear approximation simply models game characteristics better, rather than modeling textual information. [sent-290, score-0.881]
79 We generate this text by randomly permuting the word locations of the actual game manual, thereby maintaining the document’s overall statistical properties. [sent-293, score-0.818]
80 The second baseline, latent variable, extends the linear action-value function Q(s, a) of the game only baseline with a set of latent variables i. [sent-294, score-0.871]
81 , it is a four layer neural network, where the second layer’s units are activated only based on game information. [sent-296, score-1.059]
82 impractical since the relevance decision is dependent on the game context, and is hence specific to each time step of each game instance. [sent-301, score-1.73]
83 Therefore, for the purposes of this evaluation, we modify the game manual by adding to it sentences randomly selected from the Wall Street Journal corpus (Marcus et al. [sent-302, score-0.87]
84 , 1993) sentences that are highly unlikely to be relevant to game play. [sent-303, score-0.854]
85 Given that our model only has to differentiate between the game manual text and the Wall Street Journal, this number may seem disappointing. [sent-307, score-0.85]
86 Furthermore, as can be seen from Figure 5, the sentence relevance accuracy varies widely as the game progresses, with a high average of 94. [sent-308, score-0.935]
87 In reality, this pattern of high initial accuracy followed by a lower average is not entirely surprising: the official game manual for Civilization II is written for first time players. [sent-310, score-0.876]
88 As such, it focuses on the initial portion of the game, providing little strategy advice relevant to subsequence game play. [sent-311, score-0.942]
89 8 If this is the reason for the observed sentence relevance trend, we would also expect the final layer of the neural network to emphasize game features over text features after the first 25 steps of the game. [sent-312, score-1.17]
90 Figure 6: Difference between the norms of the text features and game features of the output layer of the neural network. [sent-315, score-0.994]
91 Beyond the initial 25 steps of the game, our method relies increasingly on game features. [sent-316, score-0.849]
92 This shows that our method is able to accurately identify relevant sen- tences when the information they contain is most pertinent to game play. [sent-320, score-0.854]
93 We evaluate the accuracy of this labeling by comparing it against a gold-standard annotation of the game manual. [sent-322, score-0.874]
94 Table 3 shows the performance of our method in terms of how accurately it labels words as state, action or background, and also how accurately it differentiates between state and action words. [sent-323, score-0.463]
95 This is to be expected since the model relies heavily on textual features only during the beginning of the game (see Figure 6). [sent-325, score-0.881]
96 This random labeling results in a win rate of 44% a performance similar – to the sentence relevance model which uses no predicate information. [sent-328, score-0.36]
97 This confirms that our method is able identify a predicate structure which, while noisy, provides information relevant to game play. [sent-329, score-0.981]
98 Column “S/A/B” shows performance on the three-way labeling of words as state, action or background, while column “S/A” shows accuracy on the task of differentiating between state and action words. [sent-333, score-0.538]
99 Figure 7 shows examples of how this textual information is grounded in the game, by way of the associations learned between words and game attributes in the final layer of the full model. [sent-335, score-1.083]
100 Our model, which operates in the Monte-Carlo framework, jointly learns to identify text relevant to a given game state in addition to learning game strategies guided by the selected text. [sent-337, score-1.871]
wordName wordTfidf (topN-words)
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