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Autonomous Agents as Embodied AI
Introduction
Q1) "Is it necessary for an intelligent system to
possess a body...?" Further, "concrete proposals on how to proceed" with Embodied AI research are encouraged. The intent here is to speak to each of these questions, with relatively lengthy discussions of Q2 and Q4, and brief responses to the others. And, a concrete proposal will be made on how to proceed. Much of what follows will also apply to Artificial Life research. Here are my short answers to the above questions, offered as appetizers for the main courses below. A1) Software systems with no body in the usual physical sense can be intelligent. But, they must be embodied in the situated sense of being autonomous agents structurally coupled with their environment. A2) An embodied architecture must have at least the primary elements of an autonomous agent, sensors, actions, drives, and an action selection mechanism. Intelligent systems typically must have much more. A3) These systems are driven by built-in or evolved-in drives and the goals generated from them. A4) We pursue a science of embodied systems by developing theories of how mechanisms of mind can work, making predictions from the theories, designing autonomous agent architectures that supposedly embody these theories, implementing these agents in hardware or software, experimenting with the agents to check our predictions, modifying our theories and architectures, and looping ad infinitum. A5) Real objects exist, as objects, only in the "minds" of autonomous agents. Their meanings are grounded in the agent's perceptions, both external and internal. A6) An ontology for knowledge about autonomous agents will include sensors, actions, drives, action selection mechanisms, and perhaps representations, goals and subgoals, beliefs, desires, intentions, emotions, attitudes, moods, memories, concepts, workspaces, plans, schedules, various mechanisms for generating some of the above, etc. This list does not even begin to be exhaustive. A7) Each autonomous agent uses it own ontology which is typically partly built-in or evolved-in and partly constructed by the agent. My concrete proposal on how to proceed includes an expanded form of the cycle outlined in A4 augmented by Sloman's notion of exploration of design space and niche space (1995). Now for the main courses. The Action Selection Paradigm of Mind
Other competing paradigms of mind include the connectionist paradigm (Smolensky 1988, Varela et al 1991, Horgan and Tiensen 1996) and the enactive paradigm (Maturana 1975, Maturana and Varela 1980, Varela et al 1991). The structural coupling invoked in A1 above derives from the enactive paradigm. The connectionist paradigm offers a brain metaphor of mind rather than a computer metaphor. The action selection paradigm of mind (Franklin 1995), on which this essay is based, sprang from observation and analysis of various embedded AI systems. Its major tenets follow: AS1) The overriding task of mind is to produce the next action. AS2) Actions are selected in the service of drives built in by evolution or design. AS3) Mind operates on sensations to create information for its own use. AS4) Mind re-creates prior information (memories) to help produce actions. AS5) Minds tend to be embodied as collections of relatively independent modules, with little communication between them. AS6) Minds tend to be enabled by a multitude of disparate mechanisms. AS7) Mind is most usefully thought of as arising from the control structures of autonomous agents. Thus, there are many types of minds with vastly different abilities. These tenets will guide much of the discussion below as, for example, A5 above will derive from AS3. As applied to human minds, AS4 and AS5 can be more definitely asserted. An action produces a change of state in an environment (Luck and D'Inverno 1995). But not every such change is produced by an action, for example the motion of a planet. We say that the action of a hammer on a nail changes the environment, but the hammer is an instrument not an actor. The action is produced by the carpenter. Similarly, it's the driver who acts, not the automobile. In a more complex situation it's the user that acts, not the program that produces payroll checks. In an AI setting the user acts with the expert system as instrument. On the other hand, a thermostat acts to maintain a temperature range. Since actions, in the sense meant here, are produced only by autonomous agents (see below), AS1 leads us to think of minds as emerging from the architectures and mechanisms of autonomous agents. Thus it seems plausible to seek answers to "foundational questions concern[ing] the nature of human thinking and intelligence" by studying the architectures, mechanisms, and behavior of autonomous agents, even artificial agents such as autonomous robots and software agents. Autonomous Agents
An autonomous agent is a system situated within and a part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda and so as to effect what it senses in the future (Franklin and Graesser 1997). And what sorts of entities satisfy this definition? The following figure illustrates the beginnings of a natural kinds taxonomy for autonomous agents (Franklin and Graesser 1997).
An environment for a human will include some range of what we call the real world. For most of us, it will not include subatomic particles or stars within a distant galaxy. The environment for a thermostat, a particularly simple robotic agent, can be described by a single state variable, the temperature. Artificial life agents "live" in an artificial environment often depicted on a monitor (e.g. Ackley and Littman 1992). Such environments often include obstacles, food, other agents, predators, etc. Sumpy, a task-specific software agent, "lives" in a UNIX file system (Song, Franklin and Negatu 1996). Julia, an entertainment agent, "lives" in a MUD on the internet (Mauldin 1994). Viruses inhabit DOS, Windows, MacOS, and even Microsoft Word. An autonomous agent must be such with respect to some environment. Such environments can be described in various ways, perhaps even as dynamical systems (Franklin and Graesser 1997). Keep in mind that autonomous agents are, themselves, part of their environments. Human and animal sensors need no recounting here. Robotic sensors include video cameras, rangefinders, bumpers or antennae with tactile and sometimes chemical receptors (Brooks 1990, Beer 1990). Artificial life agents use artificial sensors, some modeled after real sensors, other not. Sumpy senses by issuing UNIX commands such as pwd or ls. Virtual Mattie, a software clerical agent (Franklin et al, forthcoming) senses only incoming email messages. Julia senses messages posted on the MUD by other users, both human and entertainment agents. Sensor return portions of the environmental state to the agent. Senses can be active or passive. Though all the senses mentioned above were external, internal senses, proprioception, also is part of many agents' design. Some might consider the re-creation of images from memory (see AS4 above) to be internal sensing. Again, there's no need to discuss actions of human, animal or even robots. Sumpy's actions consist of wandering from directory to directory, compressing some files, backing up others, and putting himself to sleep when usage of the system is heavy. Virtual Mattie, among other things, corresponds with seminar organizers in English via email, sends out seminar announcements and keeps a mailing list updated. Julia wanders about the MUD conversing with occupants. Again, actions can be external or internal, such as producing plans, schedules, or announcements. Every autonomous agent come with a built-in set of primitive actions. Other actions, usually sequences of primitive actions, can also be built in or can be learned. The definition of an autonomous agent requires that it pursue its own agenda. Where does this agenda come from? Every autonomous agent must be provided with built-in (or evolved-in) sources of motivation for its actions. I refer to these sources as drives (see AS2 above). Sumpy has a drive to compress files when needed. Virtual Mattie has a drive to get seminar announcements out on time. Drives may be explicit or implicit. A thermostat's single drive is to keep the temperature within a range. This drive is hardwired into the mechanism. Sumpy's four drives are as hardwired as that of the thermostat, except that it's done in software. My statement of such a drive describes a straightforward causal mechanism within the agent. Virtual Mattie's six or so drives are explicitly represented as drives within her architecture. They still operate causally, but not in such a straightforward manner. An accounting of human drives would seem a useful endeavor. Drives give rise to goals that act to satisfy the drives. A goal describes a desired specific state of the environment (Luck and D'Inverno 1995). I picture the motivations of a complex autonomous agent as comprising a forest in the computational since. Each tree in this forest is rooted in a drive which branches to high-level goals. Goals can branch to lower level subgoals, etc. The leaf nodes in this forest comprise the agent's agenda. Now that we can recognize an autonomous agent's agenda, the question of pursuing that agenda remains. We've arrived at action selection (see AS1 above). Each agent must come equipped with some mechanism for choosing among its possible actions in pursuit of some goal on its agenda. These mechanisms vary greatly. Sumpy is named after it subsumption architecture (Brooks 1990a). One of its layers uses fuzzy logic (Yager and Filev 1994). Some internet information seeking agents use classical AI, say planning (Etzioni and Weld 1994). Virtual Mattie selects her actions via a considerably augmented form of Maes' behavior net (1990). This topic will be discussed in more detail below. Finally, an autonomous agent must act so as to effect its possible future sensing. This requires that the agent not only be in and a part of an environment, but that it be structurally coupled to that environment (Maturana 1975, Maturana and Varela 1980, Varela et al 1991). (See also A1 above.) Structural coupling, as applied here, means that the agent's architecture and mechanisms must mesh with its environment so that it senses portions relevant to its needs and can act so as to meet those needs. Having unpacked the definition of autonomous agent, we
can think of it as specifying the appropriate objects of study of Embodied
AI. |
