COMP150 Behavior-Based Robotics

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For class use only, do not distribute COMP150 Behavior-Based Robotics http://www.cs.tufts.

1 For class use only, do not distribute COMP150 Behavior-Based Robotics

2 Brief summary from last week Started with idea of intelligent behavior and attempts to make the notion sufficiently precise that we can launch a successful research program into developing (behavior-based) systems that exhibit intelligent behavior Want to be able to define and implement robotic control architectures (behavior-based or other) for a set of tasks and number of different environments The ultimate goal is to be able to make general statements about the workings (i.e., the functional organization) and trade-offs (i.e., the difference in performance) among different control mechanisms Next steps: clarify what is different in the behavior-based approach compared to classical approaches to robotics, and determine relationship of behavior-based robotics to situated embodied cognitive science

3 What is Cognitive Science? First, have to define what cognitive science means Careful: there are probably as many different answers as researchers in the field! So I won't attempt to give you a clear-cut definition, but rather a few intuitions that will later crystallize as we study different models Taken literally: science of cognition (from Latin: pp. of cognoscere meaning to become acquainted with, to know ) From The Mind s New Science-A History of the Cognitive Revolution? (Basic Books, New York, 1985) by Howard Gardner: Cognitive science is a contemporary, empirically based effort to answer long-standing epistemological questions?

4 What is Cognitive Science? Cognitive science as empirical approach to epistemology (that is, the theory dealing with knowledge, what it is and how it is possible) What is knowledge? implicit vs. explicit positive vs. negative declarative vs. procedural innate vs. acquired direct vs. indirect Other definitions stress more the notion of "information" and view cognitive systems as "information processing systems"--what does information mean? (Shannon, Dretske, Barwise, etc.)

5 What is Cognitive Science? Other variant: interdisciplinary effort to study and understand cognition-- but what is cognition? Other version: interdisciplinary effort to study and understand cognitive systems--what are cognitive systems? Yet another one: interdisciplinary effort to study intelligent systems (again, what are intelligent systems?) Examples of cognitive systems: only (?) biological systems (not all animals) but what about Deep Blue? The situated embodied angle : cannot understand cognition (whatever that is) in isolation of the body that is controlled by the cognitive system and the environment in which the body is situated behavior-based robotics takes a very similar stance!

6 The Historical Roots of Cognitive Science No general consensus about the exact day of birth 2 major (founding) events: the Hixon Symposium on cerebral mechanism in behavior (1948) the symposium on information at MIT (1956) The Hixon symposium was an interdisciplinary meeting of researchers interested in cybernetics and the theory of information (e.g., John von Neumann, Warren McCulloch, and Norbert Wiener) Combine ideas from mathematics, the new field of "computer science", cybernetics, and neurobiology (e.g., discussions of the "neuronal model" and its information-theoretic properties)

7 The Symposium on Information at MIT (1956) Among the participants Noam Chomsky, Claude Shannon und George Miller Chomsky's colorless green ideas sleep furiously (as opposed to ideas furiously green colorless sleep ) making the case for the syntactic, grammatical, rule-based structure of language Shannon's syntactic notion of information (information content of a signal is the inverse of its probability of its expectation) Miller: the magic number 7 in memory tasks; he writes about the second day of the meeting that cognitive science burst from the womb of cybernetics and became a recognizable, interdisciplinary adventure in its own right (Miller, A Very Personal History, Cambridge, MA: MIT Center for Cognitive Science, 1979, S. 9).

8 The Symposium on Information at MIT (1956) Furthermore, Miller wrote that he left the symposium with the conviction that human experimental psychology, theoretical linguistics, and the computer simulation of cognitive processes were all pieces from a larger whole, and that the future would see a progressive elaboration and coordination of their shared concerns (Miller, 1979, S. 9) Note how the upcoming (digital) computers and the idea that computers could process information (maybe not unlike the brain) were crucially contributing to the establishment of cognitive science as a field that could study cognitive architectures and processes (possibly and largely independent of the physical underpinnings)

9 The Birth of Artificial Intelligence (1956) Marvin Minsky and John McCarthy's workshop in the summer of 1956 Herbert Simon and Allen Newell present Logic Theorist, the first prototype of an artificial intelligent system the term artificial intelligence is coined by McCarthy Marvin Minsky states in the proposal to the 1956 Dartmouth conference: an intelligent machine would tend to build up within itself an abstract model of the environment in which it is placed. If it were given a problem it could first explore solutions within the internal abstract model of the environment and then attempt external experiments. (McCarthy, 1956)

10 The Classical View (of cognition) From today's perspective clear: humans are able to process (complex) information (i.e., receive, store, retrieve, transform, etc. information) Note that it was not possible to say that in the 40ies and beginning 50ies in psychology (as behaviorism dominated the field in the US, cognitive psychology was regarded unscientific since it had used introspection as a method to obtain knowledge about the mind) Computer helped: computers also process information (just like other biological systems), but furthermore we know how they do it! (e.g., computational states = inner states of the system; almost abhorred by behaviorists)

11 The Classical View (of cognition) To compute a function on a computer (i.e., to let it exhibit a certain behavior), it suffices to specify the program description of the function (i.e., the algorithm) => specify various computational states All these ideas (computer as information processing system, syntactic specification of programs that give rise to a causal chain of actions inside of the physical system computer ) suggest a daring analogy: Maybe cognition (understood as the sum of all information processing cognitive processes) are nothing but a complex computational process? Put differently: maybe cognition is simply computation? (but note that clearly not all computations are cognitive)

12 The Computer Metaphor The mind is to the brain as the program is to the hardware (e.g. Searle, Johson-Laird, Dennett, Phylyshyn, etc.) The program based on it is sometimes called computationalism" or "the computational claim on mind ( cognition is computation and can be understood as such ) Computationalists view cognitive processes as computational manipulation of representations (i.e., syntactic transformations of discrete entities, so-called symbol tokens, within the cognitive system, which might stand in (i.e., represent) for something within or outside the system Note that in digital computers there are discrete units (e.g. bits), which are manipulated and transformed during a computational process which is syntactically specified by a program In philosophy of mind: Turing machine-functionalism

13 The Computer Metaphor The computationalist claim: cognitive processes can be described syntactically without paying attention to their semantics Cp to Haugeland's famous dictum that Semantics will take care of itself if only the syntax is right Example: the Physical Symbol System Hypothesis (PSSH), Newell and Simon (1980): Cognition can be viewed and understood as a process that manipulates (physical) symbol tokens, which receive a semantic interpretation (physical because these processes are implemented in a physical medium)

14 The Modularity Hypothesis Example: the Modularity Hypothesis (Fodor (1983) is one of the strongest proponents of the modularity hypothesis ) 3 level model: transducer, input/output system (modular), and higher cognitive functions (non-modular) Note that some systems can indeed be clearly identified in the brain and distinguished from others Modules are: functionally specific (their computational organization depends on the respective stimulus) cognitive impenetrable (e.g., one has no conscious control over how one perceives things) fast (since they work autonomous) informationally closed (do not need any information of other systems to do their job) flat output (no complex representations)

15 Summary of the Classical Approach The classical approach to cognition shaped research in artificial intelligence an robotics: the PSSH and the modularity hypotheses (among others) were completely in line with the way AI researcher wrote programs that made computers exhibit intelligent behavior For example, the PSSH made digital computers sufficient for cognition (and also situated embodied cognition, in way foreshadowing Harnad's symbol grounding problem in the 90ies) The modularity hypothesis implied that many cognitive faculties (e.g., various aspects of perception) could be tackled independently from other parts of the cognitive system (and from the body, for that matter) In robotics, the sense-think-act cycle became the main paradigm (prototypical example is Shakey

16 The Classical Approach in Robotics The sense-plan-act cycle became the main paradigm in robotics (prototypical: Shakey, 1968)

17 The Time of Change... Early to Mid-Eighties we see criticism of the computational model (note philosophers had been criticizing it for quite some time, e.g., the Gödel- Lucas argument or Searle's "Chinese Room") Important factor: the upcoming connectionists (e.g., Rumelhart & McClelland's PDP, 1988) Also, AI did not reach expectations ( AI winter ) Common tenor of critics: the symbolic, top-down model might be insufficient Maybe implementation details matter, maybe the body matters! Maybe natural intelligent systems are not computational! So, first look at the neuronal level to figure out what these neurons do, then start contemplating about what "architecture" they implement

18 The Time of Change... Connectionists: researchers who study the properties of neural networks (with respect to cognitive functions) Main convictions: symbols cannot be assumed they need to be viewed as "emergent" upon the properties of neural/connectionist networks use biologically plausible models (such as neural networks) The rise of the neurosciences (as indicated by the "neuro"-prefix): neuro-linguistics neuro-informatics cognitive neuro-psychology/physiology

19 The Time of Change... Beginning of the 90ies: even more radical approaches against the classical model Dynamicists (=adherents of the theory of dynamical systems) criticize not only the classical level of description and/or its various methods of implementation also doubt that representation is the central notion Critical component left out by computationalism: time! Good source: It's about Time, byrobert Port and Tim van Gelder, MIT Press, 1995 Claim: the mathematical formalism of (discrete or continuous) dynamical systems is the appropriate way of characterizing physical systems (like bodies with brains) that change over time

20 The Time of Change... Similar development in AI: embodied artificial intelligence Advanced mainly by roboticists (Rodney Brooks, Hans Moravec, Rolf Pfeiffer, and others) New fields: behavior-based and evolutionary robotics (usually tied with neural networks) to study biologically inspired control mechanisms (and to make things work!) Main credo of all of them: cannot study mind without body! Claim: many cognitive processes do not have to be implemented directly, but will result as a byproduct from the interaction of the agent with its environment! Important: physical (not simulated) agents as physics matters (i.e., the physical properties of the body) => Overlap between dynamicists and embodied AI people

21 Pfeiffer's Embodied Cognitive Science Pfeiffer's notion for cognitive science that deals with complete agents (i.e., control systems implemented in a body ) Two kinds of agents : real (in the sense of "physical") simulated (in the sense of "virtual") What is the difference? (is it essential?) Can we learn from simulated agents/robots or should real agents be preferred? Important notion in this context: model (i.e., whether we want to model a particular target system)

22 Pfeiffer's Embodied Cognitive Science Basic notions (for Pfeiffer's notion of embodied cognitive science ): Self-sufficiency Autonomy Situatedness Embodiment Adaptivitiy Ecological Niche Note that several definition of embodied cognitive science and have been proposed in the literature that do not include all the above concepts

23 Pfeiffer's Embodied Cognitive Science Self-sufficiency multiple tasks and behaviors trade-offs and deficits circadian cycles behavior control Autonomy Situatedness Embodiment Adaptivitiy Ecological Niche

24 Pfeiffer's Embodied Cognitive Science Self-sufficiency Autonomy degrees of autonomy dependence on the environment vs. dependence on other agents self-sufficiency increases autonomy Situatedness Embodiment Adaptivitiy Ecological Niche

25 Pfeiffer's Embodied Cognitive Science Self-sufficiency Autonomy Situatedness Definition: "An agent is situated if it acquires information about its environment only through its sensors in interaction with the environment" agents can acquire their own history more autonomous evolutionary techniques Embodiment Adaptivitiy Ecological Niche

26 Pfeiffer's Embodied Cognitive Science Self-sufficiency Autonomy Situatedness Embodiment agents have a body, they are physical agents (merely simulated agents can never be embodied!) embodied agents are forced to interact with their environment agents are subject to physical forces, etc. possibility to utilize physics for control (e.g., gaits, etc.) Adaptivitiy Ecological Niche

27 Pfeiffer's Embodied Cognitive Science Self-sufficiency Autonomy Situatedness Embodiment Adaptivitiy the ability to adjust oneself to the environment adaptivity and intelligence are directly related different kinds: evolutionary adaptation physiological adaptation sensory adaptation adaptation by learning

28 Pfeiffer's Embodied Cognitive Science Ecological Niche: Definition: "The range of each environmental variable (such as temperature, humidity, food items, etc.) within which a species can exist and reproduce" no universal animal! (some contrast this to "universal computation") need to characterize the niche w.r.t. a particular agent: only properties that are behaviorally relevant matter static vs. dynamic environment deterministic vs. non-deterministic the sensors and effectors (i.e., how the system can interact with the environment) the objects in the environment and their properties

29 Back to Behaviors... Again from Mataric' article: Behavior-based robotics controllers consist of a collection of behaviors that achieve and/or maintain goals.. For example, "avoid-obstacles" maintains the goal of preventing collisions; "go-home" achieves the goal of reaching some home destination. Behaviors are implemented as control laws (sometimes similar to those used in control theory), either in software or hardware,, as a processing element or a procedure. Each behavior can take inputs from the robot's sensors (e.g., camera, ultrasound, infrared, tactile) and/or from other behaviors in the system, and send outputs to the robot"s effectors (e.g., wheels, grippers, arm, speech) and/or to other behaviors.. Thus, a behavior-based controller is a structured network of interacting behaviors.

30 Back to Behaviors... Motivation: compare to Mataric' characterization of behaviors to "finite- state machines (FSMs)" (e.g., the 2nd to last sentence in the above quote) Note that many behavior-based architectures are actually direct variants/implementations of FSMs (e.g., subsumption,situated automata, and others) Behavior-based design: implement control components that achieve "behaviors" and arrange them in a control system so that the transitions between behaviors occur according to specification This effectively means designing an architecture Many different architectures out there, can be characterized by architecture schemata (i.e., architectures are instances of architecture schemata that are e less detailed)

31 Quick Excursion on Architecture In general, architectures specify an arrangement of components (or functional units) and how they are connected Consequently, architectures define what is commonly called "virtual machine" in CS (e.g., a non-physical, "software" machine with interacting "software" parts) Will talk much more about what these components can be later (for now, think of a "planning mechanism", for example) Main way to "carve up" architecture space: reactive vs. deliberative architectures Or better: deliberative vs. non-deliberative as "reactive" has different meanings to different people

32 Quick Excursion on Architecture Different meanings of reactive : reactive as "stateless" reactive as "(tight) sensor-motor coupling" (no intermediary processing) reactive as "simple" reactive as "non-representational" reactive as "fast, timely response" Hence, be very careful when you read the term "reactive", it may not mean what you think it means! For us, in the context of behavior-based robotics reactive does not mean stateless or non-representational, but usually has the connotation of the other three meanings

33 Quick Excursion on Architecture One (strong) characterization of deliberative architectures:they have components to perform "what-if" reasoning Hence, they need to be able to entertain representations of non-existent, yet possible states (among other things) Other characterization of deliberative include properties such as representational, planning, reasoning, knowledge-based, etc. Often, distinction between explicit and implicit representations are made (e.g., explicit vs implicit goals, explicit vs implicit reasoning, explicit vs implicit planning, etc.) Main distinction to keep in mind: architectural mechanisms (that which all instances of the architecture share) vs apriori or acquired knowledge (i.e., that which different instances of the same architecture differ on)

34 Quick Excursion on Architecture Other dimensions of architectural variation (Fig in the Arkin book is only partially useful): Arrangement of components e.g., components arranged in a "hierarchy" (e.g., where components on higher levels dominate components on lower levels)? Flow of control e.g., sequential activation of components (cp. the "Omega" model by Albus, 1981) Self-modifying vs. non-selfmodifying architectures e.g., architectures with learning mechanisms

35 Quick Excursion on Architecture Functional dependence on external factors vs. independent functioning e.g., a chess program vs. controller of a machine (think of the "thermostat" controlling the blinker) Functions of components vs. emergent functions e.g., "wall-following" implemented in a special component vs. emergent from the functioning of other components Behavior arbitration e.g., centralized arbiter that decides which "behavior to execute" Will take a look at several behavior-based architectures in this course and also talk about various extensions

Introduction to cognitive science Session 1: Introduction

Introduction to cognitive science Session 1: Introduction Martin Takáč Centre for cognitive science DAI FMFI Comenius University in Bratislava Príprava štúdia matematiky a informatiky na FMFI UK v anglickom