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Meaning in Artificial Agents: The Symbol Grounding Problem Revisited Dairon Rodríguez, Jorge Hermosillo & Bruno Lara Minds and Machines Journal for Artificial Intelligence, Philosophy and Cognitive Science ISSN 0924-6495 Minds & Machines DOI 10.1007/s11023-011-9263-x 1 23 Your article is protected by copyright and all rights are held exclusively by Springer Science+Business Media B.V.. This e-offprint is for personal use only and shall not be selfarchived in electronic repositories. If you wish to self-archive your work, please use the accepted author’s version for posting to your own website or your institution’s repository. You may further deposit the accepted author’s version on a funder’s repository at a funder’s request, provided it is not made publicly available until 12 months after publication. 1 23 Author's personal copy Minds & Machines DOI 10.1007/s11023-011-9263-x Meaning in Artificial Agents: The Symbol Grounding Problem Revisited Dairon Rodrı́guez • Jorge Hermosillo • Bruno Lara Received: 19 April 2011 / Accepted: 23 November 2011 Ó Springer Science+Business Media B.V. 2011 Abstract The Chinese room argument has presented a persistent headache in the search for Artificial Intelligence. Since it first appeared in the literature, various interpretations have been made, attempting to understand the problems posed by this thought experiment. Throughout all this time, some researchers in the Artificial Intelligence community have seen Symbol Grounding as proposed by Harnad as a solution to the Chinese room argument. The main thesis in this paper is that although related, these two issues present different problems in the framework presented by Harnad himself. The work presented here attempts to shed some light on the relationship between John Searle’s intentionality notion and Harnad’s Symbol Grounding Problem. Keywords Chinese room argument Symbol grounding problem Introduction Since its conception in the fifties, Artificial Intelligence as a scientific field has tried to emulate the behaviour of the human brain, on the assumption that computers and brains are information processing machines. A traditional and widely accepted view of cognition explains behaviour as a product of a direct, unidirectional line of information processing. Sensory inputs create a representation and according to this a motor action is performed; actions are D. Rodrı́guez J. Hermosillo B. Lara (&) Facultad de Ciencias, UAEM, Cuernavaca, Mexico e-mail: [email protected] D. Rodrı́guez e-mail: [email protected] J. Hermosillo e-mail: [email protected] 123 Author's personal copy D. Rodrı́guez et al. regarded as reactions, responses to stimuli. Most of the observed behaviour is considered a consequence of an innate stimulus-response mechanism that is available to the individual (Witkowski 2002). Known as the information processing metaphor or computationalism, this framework thinks of the perception processes as modules that receive, modify and then pass the information available from the environment to modules in charge of motor control. During the first decades of research, the goals of Artificial Intelligence, seemed very clear: to achieve artificial systems capable of handling the right sort of representations by means of the right set of rules. The cognitive sciences found themselves following these same assumptions on the functioning of the brain and the mind. All these ideas were the principles of cognitivism, asserting that the central functions of the mind -of thinking- can be accounted for in terms of the manipulation of symbols according to explicit rules. Cognitivism has, in turn, three elements of note: representation, formalism, and rule-based transformation (Anderson 2003). In Artificial Intelligence, the main areas of research could be framed in what is now known as GOFAI (Good Old Fashion Artificial Inteligence) which loosely follows the ideas of cognitivism. In the last three decades, a big direction change affected the cognitive sciences, including Artificial Inteligence. A cornerstone in the the aforementioned turnabout was John Searle’s seminal paper (Searle 1980), where he severly criticized the computationalist approach to cognition. Several authors in the cognitve sciences comunity attempted to give Searle a reply to his argument, which states that computer programs are not enough to allot artificial systems with minds. However, this thought experiment aroused some questions that still remain open. Later, Steven Harnad brought out the now highly cited Symbol grounding problem (SGP) (Harnad 1990), where he sets a fundamental issue for achieving intelligent machines. In this paper we address the following question: what are the links between the fundamental issues that Searle and Harnad rise? After a thorough review of the papers presented by Harnad throughout his career, we try to shed some light on the connection between Searle’s intentionality notion and Harnad’s SGP. We argue that Harnad never intended to address directly Searle’s Chinese room argument with his SGP and the solutions he proposed for it, as some works found in the literature have presumably assumed. This discussion has received little attention in the literature. We believe that after nearly 30 years of research, it is important to consider the connection between these two relevant contributions, as it could give a fresh direction in the search for Artificial Intelligence. In order to bring out our thesis, we analyse a sample of works taken from a very significant and thorough historical review of the SGP after fifteen years of research by the Artificial Intelligence community, presented by Taddeo and Floridi (2005). We believe that this review encompasses important strategies and investigations into the SGP and the search for Artificial Intelligence. However, our analysis is carried out from a different perspective than Taddeo and Floridi as they do not attempt to draw a direct connection between the two authors we are concerned with. In the next Section we present a brief outline of both the Chinese Room Argument and the SGP. We continue with a presentation of our main thesis by reviewing Harnad’s work. We then present some misinterpretations we have noticed in the 123 Author's personal copy Meaning in Artificial Agents literature regarding Searle’s and Harnad’s relevant contributions, since we believe it is important to highlight the problems and consequences of making a wrong connection between them. Finally, in the last Section, we present our conclusions. The Chinese Room Argument In his 1980 seminal paper (Searle 1980), Searle tries to answer one of the main questions the Artificial Inteligence community has been pondering throughout its history: Can a machine think? It is of vital importance to mention the context and prevailing theories when this question was posed. As discussed in the previous section, research in the Cognitive Sciences was highly influenced by computationalism and the affirmation that brains were similar to computers in that they were only information processing machines. Conversely, for people working in Artificial Intelligence, a computer was seen as a brain waiting for the right program to become an intelligent machine. The origin of this idea is in Turing’s (1950) paper where he states that a computer can be considered intelligent if it is indistinguishable in its verbal behavior from a human being (Turing 1950). It follows from this argument that it would be possible to have an intelligent machine; the only missing element is the right program, a program designed to hold a conversation in a human manner. However, Searle argued against that possibility by means of the Chinese room argument. Searle’s argument centers on a thought experiment in which he himself, who knows only English, sits in a room following a set of syntactic rules written in English for manipulating strings of Chinese characters. Following these rules he is able to answer a few questions asked in Chinese so that for those outside the room it appears as if Searle understands Chinese. However, he only pays attention to formal features of manipulated symbols, overlooking the meaning that could be associated with them. Therefore, the aforementioned set of rules enables Searle to pass the Turing Test even though he does not understand a single word of Chinese. The argument is intended to show that while suitably programmed computers may appear to converse in natural language, they are not capable of grasping the semantic contents of their own symbolic structures. Searle argues that the thought experiment underscores the fact that computers merely use syntactic rules to manipulate symbol strings, but have no understanding. Searle goes on to say: The point of the argument is this: if the man in the room does not understand Chinese on the basis of implementing the appropriate program for understanding Chinese then neither does any other digital computer solely on that basis, because no computer has anything the man in the room does not have (Searle 1980). Symbol Grounding as a Response to the Chinese Room In Harnad (1990), Harnad posed the following problem. An artificial agent, even though capable of handling symbols syntactically, nevertheless does not have the 123 Author's personal copy D. Rodrı́guez et al. means to link those symbols to their referents. This problem was framed in the context of the prevailing theories in the cognitive sciences at the time, namely the symbolic model of the mind, which holds that ‘‘the mind is a symbol system and cognition is symbol manipulation’’ (Harnad 1999). Strictly speaking, the SGP can be seen as the problem of endowing Artificial Agents with the necessary means to autonomously create internal representations that link their manipulated symbols to their corresponding referents in the external world. These representations should arise through the agents own sensory motor capabilities, by grouping in general categories the invariant features of perceived data. These categorical representations have as their main function to act as concepts, allowing agents to pick out the referents for manipulated symbols. Until now, we have addressed Searle’s Chinese room argument as well as Harnad’s main thesis regarding the SGP. Nevertheless, we still have to discuss how the two are related to each other. In this respect, most of the specialized literature has regarded Harnad’s proposal to solve the SGP as the first attempt to coherently answer the questions that the Chinese Room thought experiment raised at the time. Our point here is that, contrary to what some authors presume, Harnad has never intended to show that an Autonomous Agent, by grounding its symbols, grasps their meaning. This is the main debate we engage and which is central to our discussion in this section. Let us be clear about our point. It would appear that the generalized assumption, that manipulation of grounded symbols entails understanding, supports a broader theoretical view that considers the SGP to be equivalent to the problem of endowing Autonomous Agents with meaningful thoughts about the world. This view is drawn on the general assumption that once an Autonomous Agent has grounded all its manipulated symbols it will be able to understand what its symbolic structures stand for, just as we know what our thoughts and beliefs are about. However, in clear opposition to this last statement, Harnad himself aimed at showing that our meaningful mental states could not be reduced to holding categorical representations. Harnad’s point is that categorical representations are not enough for a subject to make its internal states bearers of meaning, since meaning can only be achieved when we know what those mental states are about. In what follows, we will introduce a thought experiment made by Harnad himself. According to this experiment, solving the SGP does not necessarily imply solving the lack of thoughts in Artificial Agents. We then turn our attention to Harnad’s particular conception of meaningful mental states, which postulates an additional element to the mere categorical representations as a condition to enable the knowledge of what our thoughts are about. As it is well known, Harnad agrees with Searle in that symbols and their manipulation by Artificial Agents is not enough to produce understanding of any kind; however, few authors have realized that Hanard seems to go beyond the traditional criticism to computationalism when he asserts in Harnad (2003) that even if an Artificial Agent was able to ground all the symbols it manipulates, this would not imply that the general project of Artificial Inteligence is actually possible. To express this another way, solving the SGP does not imply that Artificial Agents possess thoughts or beliefs. 123 Author's personal copy Meaning in Artificial Agents To demonstrate this last thesis, Harnad developed his own version of the Chinese room thought experiment (Harnad 1992): let us imagine a robot built up in such a way that it could, by its own means, provide representations to each of the symbols it manipulates. Furthermore, let us imagine that thanks to these representations and to its particular functional organization, the robot would have the same verbal behavior as we exhibit daily when confronted with sensory stimuli. In that case, the robot might be able to succeed in the robotic version of the Turing Test, that is, its verbal behavior in the face of sensory stimuli would be indistinguishable from that of a normal human being. Nevertheless, Harnad further mentions that even with all these capabilities ‘‘the robot would fail to have in its head what Searle has in his: It could be a Zombie’’ (Harnad 2003); that is, it would lack any kind of belief or thought that could be considered a bearer of meaning. Even if the thought experiment we have briefly described does not try to demonstrate that robots cannot think, it does contribute to the debate in that it reveals that we would not incur in any conceptual contradiction if we postulate the existence of an Artificial Agent that, while lacking the capability of thought, would be endowed with the means to pick out the referents for the symbols it manipulates. Thus, the fundamental intuition that Harnad seeks to appeal to, with his mental experiment, is that there would be no technical inconvenience if we wanted to make a robot that, while possibly being a zombie, would be able to categorize and identify objects on the basis of categorical representations that it would have acquired autonomously. The latter means that it would not be evident whatsoever, that an Artificial Agent would have meaningful internal states because it has grounded the symbols it manipulates. So, what is missing in an Artificial Agent for it to have thoughts as we do? Harnad suggests that the human brain posseses two properties that make ‘‘meaningless strings of squiggles become meaningful thoughts’’ (Harnad 2003). The first property has already been pointed out by Harnad himself, when in (1990) he wrote about the limits of computationalism: the human brain, as opposed to computational systems, has the capacity to pick out the referents for the symbols manipulated. In this line of thought, the fact that Mary correctly believes that Aristotle was the mentor of Alexander the Great is possible, among other things, thanks to her capability to direct her thoughts to Aristotle and not any other Greek philosopher such as Plato. Several authors, Harnad himself included, have tried to explain this capability with the existence of internal representations or concepts, that given their particular internal structure are capable of selecting the referent for our thoughts. According to this perspective ‘‘beliefs imply the use of concepts: one can not believe that something is a cow unless one understands what a cow is and in this same way, possess the concept cow’’ (Honderich 1995). Following this line of reasoining, it follows that our thoughts are made out of categorical representations, e.g. concepts that are used to determine or select their respective referents. It is then, in a similar manner, that the symbol grounding as proposed by Harnad would be useful for an Artificial Agent to pick out the referents for the symbols it manipulates. What all this implies is that grounding is at least a necessary condition for thoughts because it is what explains the particular directionality of our mental 123 Author's personal copy D. Rodrı́guez et al. states. However, as discussed above, grounding by itself appears insufficient for meaningful mental states. With respect to the second property that allows us to have thougths, Harnad postulates that phenomenological consciousness and understanding are somehow intimately tied. Harnad points to the fact that our mental states possess a phenomenological or experiential component: ‘‘[we] all know what it FEELS like to mean X, to understand X, to be thinking about X’’ (Harnad 1992). However, for Harnad this fact, far from being a simple curiosity, is tightly bond with the property, characteristically exhibited by some of our internal states, to be about facts or particular entities. This aboutness property has been called intentionality (Brentano 1874). As an example, Harnad affirms that the difference between understanding a certain sentence written in english and not understanding a sentence because it is written in Chinese resides in a difference in phenomenological content: in the first case we feel the sentence to be about something in particular, in the second case such sensation of aboutness is missing (Harnad 1992). This difference suggests that the property to represent entities would be intrinsically present on those internal states that come accompanied by a phenomenological content of aboutness and that in a certain manner would define them as thoughts about the world. Simply stated: we are able to make our internal states bearers of meaning when we feel (become aware of) their aboutness. The argument runs contrary to the naturalist consensus followed by most cognitive scientists, and it is partially backed by an old and vigorous philosophical tradition that links the concepts of consciousness and thinking. This tradition makes its first formulation in the works of the French philosopher Rene Descartes, who made the following declaration: ‘‘I take the word thought to cover everything that we are aware of as happening within us, and it counts as thought because we are aware of it’’ (Descartes 2010). A natural conclusion to our discussion of the second property exposed by Harnad is that there can not be meaningful thoughts that are not at the same time conscious mental states. This would impose an important restriction on the entities to which we can justifiably allot mental states, as only entities to which we concede consciousness could have them. Evidently, this would mean that for the case of the position defended by Harnad and that we have tried to summarize, the general project of Artificial Intelligence, namely trying to endow Artificial Agents with thoughts, could only be completed when we manage to solve not only the SGP, given that ‘‘the problem of intentionality is not the SGP; nor is grounding symbols the solution to the problem of intentionality’’ (Harnad 2003), but on top of that, creating artificial consciousness.1 1 Before this possibility, Harnad himself is particularly sceptic: ‘‘the problem of discovering the causal mechanism for successfully picking out the referent of a category name can in principle be solved by cognitive science. But the problem of explaining how consciousness can play an independent role in doing so is probably insoluble’’ (Harnad 2003). 123 Author's personal copy Meaning in Artificial Agents Some Representative Misinterpretations We have pointed out that in the specialized literature there is a trend towards considering that Harnad’s proposal is a method for Artificial Agents to give meaning to the symbols it manipulates. It is important to realize that this assumption has had a deep impact in the terms in which some authors express themselves about their proposed solutions to the SGP. As we shall see in this section, the origin of this common assumption can be found in different sources. An early notable example can be found in Davidsson (1993), where Davidsson states, referring to the SGP, that: the problem of concern is that the interpretations (of symbols) are made by the mind of an external interpreter rather than being intrinsic to the symbol manipulating system. The system itself has no idea of what the symbols stand for. In this way, Davidsson reformulates the SGP in such a way that subtly turns it in the problem posed by Searle. It follows then, for Davidsson, that the mechanisms suggested by Harnad for Artificial Agents to autonomously acquire internal representations are at the same time tools or means for endowing the internal states of Artificial Agents with meaning: Harnad suggests in the same article ([Har90]) a solution to this problem [SGP]. According to him, the meaning of the system’s symbols should be grounded in its ability to identify and manipulate the objects that they are interpretable as standing for (Davidsson 1993). Echoing this interpretations of Harnad’s work, Davidsson states as one of his main goals to solve the SGP by developing an agent that must by itself be able to make sense of the symbols used to represent its knowledge about its environment and of its problem solving capabilities (cf. paper VI). Thus, as pointed out earlier, it must be able to interpret and reason about these symbols (Davidsson 1996). Davidsson hopes to accomplish this goal through the vision system and its use of epistemological representations that are parts of the same structure as the corresponding symbols, which permits grounding, or the connection between symbols (designators) and their referents (objects in the world), to be carried out (Davidsson 1993). However, as can be derived from the previous section, this conclusion is not obvious and therefore requires further justification. Besides, it is important to point out that an interpretation such as Davidsson’s about SGP is implausible in the light of what Harnad himself argued later, since his main concern consisted simply in making manipulated symbols to be: ‘‘grounded in something other than just meaningless symbols’’ (Harnad 1999), where grounded means the ability to pick out 123 Author's personal copy D. Rodrı́guez et al. referents for manipulated symbols and not the ability to make sense of symbols as Davidsson suggested. Another author following a similar interpretation of the SGP is Mayo (2003). According to him: In response to Searle’s well-know Chinese room argument against Strong AI (and more generally computationalism), Harnad proposed that if the symbols manipulated by a robot were sufficiently grounded in the real world, then the robot could be said to literally understand. With no doubt, this quote represents a clear enough instance of the misconception concerning SGP that Mayo shares with different authors. Naturally, the answer of these authors to the Chinese room argument consists in proposing a mechanism to endow Artificial Agents with categorical representations for the symbols that these are capable of handling. In this respect Mayo that holds that: the symbols, by virtue of their groundedness, can be manipulated intrinsically without any distinct and artificial rules of composition being defined. This could serve as the starting point for a definition of understanding (Mayo 2003). Mayo continues to say: ‘‘By elaborating on the notion of symbol groundedness in three ways, I will show that Searle’s CRA is considerably weakened.’’ A similar position is held by Rosenstein and Cohen (1998) when they affirm that: To make the leap from percepts to symbolic thought and language, the agent requires a way of transforming uninterpreted sensor information into meaningful categories. That is, the agent must solve the bottom-up version of the SGP The solution outlined below was inspired by the method of delays, a nonlinear dynamics tool for producing spatial representations of time-based data. More recently, we note that even a leading author like Luc Steels is entangled with some of the misinterpretations we have pointed out. However, there is nothing wrong in Steels’s interpretation of the SGP but problems arise when we consider Steels’s particular reading of Searle’s Chinese room argument: Language requires the capacity to link symbols (words, sentences) through the intermediary of internal representations to the physical world, a process known as symbol grounding. One of the biggest debates in the cognitive sciences concerns the question of how human brains are able to do this. Do we need a material explanation or a system explanation? John Searle’s well known Chinese Room thought experiment, which continues to generate a vast polemic literature of arguments and counter-arguments, has argued that autonomously establishing internal representations of the world (called ’’intentionality’’ in philosophical parlance) is based on special properties of human neural tissue and that consequently an artificial system, such as an autonomous physical robot, can never achieve this (Steels 2006). 123 Author's personal copy Meaning in Artificial Agents One clear difference with Davidsson is that Steels frames the problem posed by Searle in terms of the SGP. But even when the starting point is different, Steels’s conclusion is shared with Davidsson in that both believe that the SGP and the problem posed by Searle can both be solved just by means of some kind of internal categories or concepts: However, as I argued elsewhere, there is a further possibility in which the brain (or an artificial system) might be able to construct and entertain m[eaningful]-representations, namely by internalizing the process of creating m[eaningful]-representations. Rather than producing the representation in terms of external physical symbols (sounds, gestures, lines on a piece of paper) an internal image is created and re-entered and processed as if it was perceived externally. The inner voice that we each hear while thinking is a manifestation of this phenomenon (Steels 2008). Now, before rejecting once and for all Steels’s last thesis, it is important to realize its attractiveness. According to a certain old fashion conception about meaning, concepts, or what Steels calls internal images, are a necessary condition to obtain intentional mental states, since they allow us to choose the referents of our beliefs and thoughts (Russell 1905). Concepts would also be a necessary condition to the organization of our perceptive experience through categorization, in that knowing that there is a cat before me, for instance, necessarily implies knowing what a cat is; that is, it is necessary to have acquired the concept of a cat. Those beings whose cognitive systems are capable of associating concepts to the symbols they manipulate are at the same time being able to organize their perceptions and to generate a stable overt behavior as a response to those stimuli. Nevertheless, it does not follow from the latter, as Steels seems to presume, that an agent capable of organizing its sensory inflow, because it manipulates symbols with categorical representations, possess thoughts and beliefs, that is, it could be a zombie just as it follows from the mental experiment presented by Harnad. Conclusion We regard meaningful states of mind, thoughts, as the internal states of an Agent (real or artificial) which are bearers of meaning. Following Harnad’s insights, the meaning that thoughts have for its possessor derive from the awareness the subject has of what they represent or stand for, more than of attaching concepts to symbols. That is, instead of considering understanding as a SGP, we regard it as a property of the rather more intricate notion of thinking; more specifically, the problem of symbol grounding concerns the internal construction of a mapping between sensory input and symbols being manipulated, so as to give grounding to those symbols. However, this internal mapping or representation does not follow from an understanding (or knowledge) of what the symbols refer to or stand for, at least no more than what, for example, a translation of an English expression into Chinese would intend in a non-native context. 123 Author's personal copy D. Rodrı́guez et al. Therefore, contrary to what several authors have misconstrued throughout the last three decades, the SGP, which is clearly concerned with providing internally manipulated symbols with concepts, does not refer to intrinsically meaningful internal states of mind. To summarize, we have argued that the position defended by Harnad, which concerns the general problem of supplying thoughts to Artificial Agents, can only be addressed when, first, the Symbol Grounding Problem is solved, thereby giving concepts to the manipulated symbols, and second, when artificial consciousness is achieved, thereby giving intentionality to those manipulated symbols. References Anderson, D. 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