Papers by Santiago Ontanon
Computational Intelligence, 2010
Some domains, such as real-time strategy (RTS) games, pose several challenges to traditional plan... more Some domains, such as real-time strategy (RTS) games, pose several challenges to traditional planning and machine learning techniques. In this article, we present a novel on-line case-based planning architecture that addresses some of these problems. Our architecture addresses issues of plan acquisition, on-line plan execution, interleaved planning and execution, and on-line plan adaptation. We also introduce the Darmok system, which implements this architecture to play WARGUS (an open source clone of the well-known RTS game WARCRAFT II). We present empirical evaluation of the performance of Darmok and show that it successfully learns to play the WARGUS game.
Studies in Fuzziness and Soft Computing, 2008
Traditional artificial intelligence techniques do not perform well in applications such as real-t... more Traditional artificial intelligence techniques do not perform well in applications such as real-time strategy games because of the extensive search spaces which need to be explored. In addition, this exploration must be carried out on-line during performance time; it cannot be precomputed. We have developed on-line casebased planning techniques that are effective in such domains. In this paper, we extend our earlier work using ideas from traditional planning to inform the real-time adaptation of plans. In our framework, when a plan is retrieved, a plan dependency graph is inferred to capture the relations between actions in the plan. The plan is then adapted in real-time using its plan dependency graph. This allows the system to create and adapt plans in an efficient and effective manner while performing the task. The approach is evaluated using WARGUS, a well-known real-time strategy game.
Case-Based Planning (CBP) is an effective technique for solving planning problems that has the po... more Case-Based Planning (CBP) is an effective technique for solving planning problems that has the potential to reduce the computational complexity of the generative planning approaches [8, 3]. However, the success of plan execution using CBP depends highly on the selection of a correct plan; especially when the case-base of plans is extensive. In this paper we introduce the concept of a situation and explain a situation assessment algorithm which improves plan retrieval for CBP. We have applied situation assessment to our previous CBP system, Darmok [11], in the domain of real-time strategy games. During Darmok's execution using situation assessment, the high-level representation of the game state i.e. situation is predicted using a decision tree based Situation-Classification model. Situation predicted is further used for the selection of relevant knowledge intensive features, which are derived from the basic representation of the game state, to compute the similarity of cases with the current problem. The feature selection performed here is knowledge based and improves the performance of similarity measurements during plan retrieval. The instantiation of the situation assessment algorithm to Darmok gave us promising results for plan retrieval within the real-time constraints.
Artificial Intelligence techniques have been successfully applied to several computer games. Howe... more Artificial Intelligence techniques have been successfully applied to several computer games. However in some kinds of computer games, like real-time strategy (RTS) games, traditional artificial intelligence techniques fail to play at a human level because of the vast search spaces that they entail. In this paper we present a real-time case based planning and execution approach designed to deal with RTS games. We propose to extract behavioral knowledge from expert demonstrations in form of individual cases. This knowledge can be reused via a case based behavior generator that proposes behaviors to achieve the specific open goals in the current plan. Specifically, we applied our technique to the WARGUS domain with promising results.
Abstract Case-based reasoning is an AI problem solving and analysis methodology that retrieves an... more Abstract Case-based reasoning is an AI problem solving and analysis methodology that retrieves and adapts previous experiences to fit new contexts. This forum is intended to gather AI researchers and practitioners with an interest in CBR to present and discuss developments in CBR theory and application.
Argumentation can be used by a group of agents to discuss about the validity of hypotheses. In th... more Argumentation can be used by a group of agents to discuss about the validity of hypotheses. In this paper we propose an argumentation-based frame-work for multiagent induction, where two agents learn separately from individual training sets, and then engage in an argumentation process in order to converge to a common hypothesis about the data. The result is a multiagent induction strategy in which the agents minimize the set of examples that they have to exchange (using argumentation) in order to converge to a shared hypothesis. The proposed strategy works for any induction algorithm which expresses the hypothesis as a set of rules. We show that the strategy converges to a hypothesis indistinguishable in training set accuracy from that learned by a centralized strategy.
Goal Driven Learning (GDL) focuses on systems that determine by themselves what has to be learnt ... more Goal Driven Learning (GDL) focuses on systems that determine by themselves what has to be learnt and how to learn it. Typically GDL systems use meta-reasoning capabilities over a base reasoner, identifying learning goals and devising strategies. In this paper we present a novel GDL technique to deal with complex AI systems where the meta- reasoning module has to analyze the reasoning trace of multiple components with potentially dif- ferent learning paradigms. Our approach works by distributing the generation of learning strategies among the different modules instead of centraliz- ing it in the meta-reasoner. We implemented our technique in the GILA system, that works in the airspace task orders domain, showing an increase in performance.
Lecture Notes in Computer Science, 2011
Believable agents have applications in a wide range of human computer interaction-related domains... more Believable agents have applications in a wide range of human computer interaction-related domains, such as education, training, arts and entertainment. Autonomous characters that behave in a believable manner have the potential to maintain human users’ suspense of disbelief and fully engage them in the experience. However, how to construct believable agents, especially in a generalizable and cost effective way, is
Creating AI for complex computer games requires a great deal of tech-nical knowledge as well as e... more Creating AI for complex computer games requires a great deal of tech-nical knowledge as well as engineering effort on the part of game developers. This paper focuses on techniques that enable end-users to create AI for games without requiring technical knowledge by using case-based reasoning techniques. AI cre-ation for computer games typically involves two steps: a) generating a first version of the AI, b) debugging and adapting it via experimentation. We will use the domain of real-time strategy games to illustrate how case-based reasoning can address both steps.
Abstract. This paper analyzes how to introduce machine learning algorithms into the process of di... more Abstract. This paper analyzes how to introduce machine learning algorithms into the process of direct volume rendering. A conceptual framework for the optical property function elicitation process is proposed and particularized for the use of attribute-value classifiers. The process is evaluated in terms of accuracy and speed using four different off-theshelf classifiers (J48, Naıve Bayes, Simple Logistic and ECOC-Adaboost). The empirical results confirm the classification of biomedical datasets as a tough problem where an opportunity ...
In this paper, we present a preliminary evaluation of a text-based and graphical version of an in... more In this paper, we present a preliminary evaluation of a text-based and graphical version of an interactive fiction game that we created to look at how the user experience varies across the different mediums and modalities.
Lecture Notes in Computer Science, 2012
We present a general framework for addressing the problem of semantic intelligibility among artif... more We present a general framework for addressing the problem of semantic intelligibility among artificial agents based on concepts integral to the case-based reasoning research program. For this purpose, we define case-based semiotics (CBS)(based on the well known notion of the semiotic triangle) as the model that defines semantic intelligibility. We show how traditional CBR notions like transformational adaptation can be used in the problem of two agents achieving mutual intelligibility over a collection of concepts (defined in CBS).
Lecture Notes in Computer Science, 2011
In this paper we focus on how to use CBR for making collective decisions in groups of agents. Mor... more In this paper we focus on how to use CBR for making collective decisions in groups of agents. Moreover, we show that using CBR allows us to dispense with standard but unrealistic assumptions taken in these kind of tasks. Typically, social choice studies voting methods but assumes complete knowledge over all possible alternatives. We present a more general scenario called open-ended deliberative agreement with rational ignorance (ODARI), and show how can CBR be used to deal with rational ignorance. We will apply this approach to the Banquet Agreement scenario, where two agents deliberate and jointly agree on a two course meal. Rational ignorance makes sense in this scenario, since it would be unreasonable for the agents to know all the alternatives. Unknown alternatives, as well as a strategy to increase chances of reaching an agreement, are problems addressed using case-based methods.
ABSTRACT We present a new approach lo learn from relational data based on re-representation of th... more ABSTRACT We present a new approach lo learn from relational data based on re-representation of the examples. This approach, called property-based re-representation is based on a new analysis of the structure of refinement graphs used in ILP and relational learning in general. This analysis allows the characterization of relational examples by a set of multi-relational patterns called properties. Using them, we perform a property-based re-representation of relational examples that facilitates the development of relational learning techniques.
This paper presents a constraint optimization approach to walling in real-time strategy (RTS) gam... more This paper presents a constraint optimization approach to walling in real-time strategy (RTS) games. Walling is a specific type of spatial reasoning, typically employed by human expert players and not currently fully exploited in RTS game AI, consisting on finding configurations of buildings to completely or partially block paths. Our approach is based on local search, and is specifically designed for the real-time nature of RTS games. We present experiments in the context of the RTS game StarCraft showing promising results.
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Papers by Santiago Ontanon