Qualitative Case-Based Reasoning for Humanoid Robot Soccer: A New Retrieval and Reuse Algorithm

Abstract

Abstract. This paper proposes a new Case-Based Reasoning (CBR) approach, named Q-CBR, that uses a Qualitative Spatial Reasoning theory to model, retrieve and reuse cases by means of spatial relations. A qualitative distance and orientation calculus (EOPRA) is used to model cases using qualitative relations between the objects in a case. A new retrieval algorithm is proposed that uses the Conceptual Neighborhood Diagram to compute the similarity measure between a new problem and the cases in the case base. A reuse algorithm is also introduced that selects the most similar case and shares it with other agents, based on their qualitative position. The proposed approach was evaluated on simulation and on real humanoid robots. Preliminary results suggest that the proposed approach is faster than using a quantitative model and other similarity measure such as the Euclidean distance. As a result of running Q-CBR, the robots obtained a higher average number of goals than those obtained when running a metric CBR approach.

Qualitative case-based reasoning and learning

“…Due to the large variety of distinct QSR formalisms, these methods find applications in a number of domains, such as robot navigation and self-localization [57], geographic information systems [20], cognitive linguistics [66,53] among others [13,70]. The present paper builds upon our previous work where an extension of the OPRA, the Elevated Point Relation Algebra EOPRA [42], was applied to represent the agents in the robot-soccer domain [27,28]. EOPRA assigns an intrinsic orientation to objects and defines a qualitative distance based on an elevated point in the domain, that could be defined as the height of the observer [42].…”

What's Hot in Case-Based Reasoning

“…Research on CBR appears to be gaining momentum because of several reasons: new applications, for example, in knowledge discovery and interactive robotics (Fitzgerald et al 2015;Floyd et al 2008;Homem et al 2016), a closer alliance with research on analogy (Coman & Kapetanakis 2016) and creativity (Kendall-Morwick 2015), and a renewed interest in conversational systems (Aha et al 2001) (Gu & Aamodt, 2005), and trust and explanation (Floyd & Aha 2016, Goel & Murdock 1996. Thus, CBR research recently has led to several reviews (de Mantaras et al 2005), books such as (Lopez 2013) and (Richter & Weber 2013), as well as several tools for building CBR systems such as IUCBR (Bogaerts & Leake 2005), jCOLIBRI (Diaz-Agudo et al 2007) and myCBR (Stahl & Roth-Berghofer 2008).…”

“…• Novel approaches to similarity and retrieval (Homem et al 2016). Similarity and retrieval are among the core constructs in CBR.…”

Similar Situations Identification for the Game of Soccer

“…One of possible approaches to design a virtual team is Case-Based Reasoning (CBR) [2]. It served as a basis for several works related to RoboCup competitions [3][4][5], where virtual AI teams demonstrated the ability to behave coordinated and effectively. Does that efficient in comparison with humans play?…”