Innate Knowledge - an overview | ScienceDirect Topics

4 SCIENTIFIC PRESUPPOSITIONS OF AI
Some of the premises of logical AI are scientific in the sense that they are subject to scientific verification or refutation. This may also be true of some of the premises listed above as philosophical.
innate knowledge The human brain has important innate knowledge, e.g. that the world includes three dimensional objects that usually persist even when not observed. This knowledge was learned by evolution. The existence of innate knowledge was not settled by philosophical analysis of the concept, but is being learned by psychological experiment and theorizing. Acquiring such knowledge by learning from sense data will be quite hard but possible.
Indeed it is worthwhile to build as much knowledge as possible into our robots. The CYC project of Douglas Lenat is an attempt to put a large amount of common sense knowledge into a database.
Identifying human innate knowledge has been the subject of recent psychological research. See [Spelke, 1994] and the discussion in [Pinker, 1997] and the references Pinker gives. In particular, babies and dogs know innately that there are permanent objects and look for them when they go out of sight. We'd better build that into our robots, as well as other innate knowledge psychologists identify. Evolution went to a lot of trouble to acquire knowledge that we needn't require robots to learn from experience. Maybe the childhood preference for natural kind concepts is something robots should have built in.
middle out Humans deal with middle-sized objects and develop our knowledge up and down from the middle. Formal theories of the world must also start from the middle where our experience informs us. Efforts to start from the most basic concepts, e.g. to make a basic ontology, are unlikely to succeed as well as starting in the middle. The ontology must be compatible with the fact that the basic entities in one's initial ontology are not the basic entities in the world. More basic entities, e.g. electrons and quarks, are known less well than the middle entities.
logic level Allen Newell, who did not use logical AI, nevertheless proposed [Newell, 1993] that there was a level of analysis of human rationality that he called the logic level at which humans could be regarded as doing what they thought would achieve their goals. Many of the systems the Carnegie-Mellon group built, e.g. SOAR, were first designed at the logic level.
universality of intelligence Achieving goals in the world requires that an agent with limited knowledge, computational ability and ability to observe use certain methods. This is independent of whether the agent is human, Martian, or machine. For example, playing chess-like games effectively requires something like alpha-beta pruning.
universal expressiveness of logic This is a proposition analogous to the Turing thesis that Turing machines are computationally universal—anything that can be computed by any machine can be computed by a Turing machine. The expressiveness thesis is that anything that can be expressed, can be expressed in first order logic with a suitable collection of functions and predicates.
Some elaboration of the idea is required before it will be as clear as the Turing thesis. First order logic isn't the best way of expressing all that can be expressed any more than Turing machines are the best way of expressing computations. However, with set theory, as axiomatized in first order logic, whatever can be expressed in stronger systems can apparently also be expressed in first order logic.
Gödel's completeness theorem tells us that every sentence p true in all models of a set a of sentences can be deduced. However, nonmonotonic reasoning is needed and used by humans to get consequences true in simple models. Very likely, reflection principles are also needed.
We expect these philosophical and scientific presuppositions to become more important as AI begins to tackle human-level intelligence.
5.4.4.2.1 Routine response experiment
The first part of the experiment was conducted to validate the capability to execute a task using a routine response and the ability to maintain the task context after a reactive response is invoked.
Experimental steps.
1.
ISAC actively monitors the environment.
2.
Barney doll is placed within the field of view causing ISAC to recognize it, i.e., the situation, and to decide to play with the doll according to its innate knowledge.
3.
When someone claps their hands, ISAC detects the location of the sound using the sound localization algorithm described in Appendix 4.
4.
ISAC stops executing the task and saccades toward the source of the sound.
5.
Because the task context is still active in the WM, ISAC goes back to the task after the reactive response is completed.
5.4.4.2.2 Task-switching experiment
The second part of the experiment was conducted to validate the functionality of FRA to switch tasks when a new situation is recognized when an event occurs.
Experimental steps.
1.
ISAC continues the task from the above experiment.
2.
Someone enters the room and approaches ISAC.
3.
When a motion is detected at the door, using the motion detection method described in Appendix 4, ISAC stops executing the current task, fixates on the detected motion, and tracks the motion with the cameras.
4.
When the motion enters the workspace, ISAC recalls a similar learned experience, thus executes the handshake behavior instead of going back to the previous task.
This experiment shows a setup of ISAC cognitive architecture to perform simple cognitive control during task execution. In this setup, we focus on FRA, which was used to execute a task using first-order responses. To help in providing simple cognitive control, CEA was used to generate a task internally and make a decision for task switching. In this section, we would like to evaluate the FRA-based operation of the system using the following criteria:
1.
The ability to switch back and forth between reactive and routine responses.
2.
The ability to use routine responses to execute tasks.
3.
The ability to switch tasks based on situational change.
In this experiment, the system has the ability to switch between reactive and routine responses seamlessly without losing its attention from the task. Reactive responses caused the system to immediately suspend the current task and attend to the particular percept that triggers the response. Reactive responses provided by FRA serve as a nontask-oriented mechanism which helps the system to become aware of other events that happen in the environment that may require attention; therefore, the system should respond within a short period of time after an event happens. A short delay is expected, however, because of the complexity of the detection algorithms and the propagation delay time in communication between agents. In the experiment, a set of clapping sounds was present at various angles where 0° was directly in front of ISAC. Table 1 summarizes the amount of time that the system took to respond after the clapping sounds were heard and the amount of time the system takes to resume the previous action after the reaction responses were completed.
Table 1. Response and resuming times to loud noise reaction
Trial
Angles (degrees)
Response Time (ms)
Resuming Time (ms)
1
64.07
102
153
2
−38.65
906
143
3
18
105
135
4
−49
594
141
5
−2.65
716
139
Both parts of the experiment in this section show that FRA can be used to execute simple tasks successfully using knowledge about the task in LTM. Two tasks used in this experiment were to play with Barney doll and to handshake the person. Note that in this experiment, the tasks were not given by a person but instead were internally generated by CEA. The task information was passed between CEA and FRA using a shared memory slot. This method allows both agents to communicate very fast. FRA executed a task as soon as CEA posted the task to the memory slot.
The task-switching experiment demonstrates the capability of the system to switch tasks based on situational change. The decision to switch tasks comes from CEA based on the situation. In this experiment, the system did not go back to the previous task because the robot detected that a person was moving toward it and entering the workspace. Due to a strong association between the event and a task in the learned experience, CEA decided to switch to a handshake with the person instead of going back to the previous task.
The performance evaluation in this section is performed based on partial results from the experiment. The final performance evaluation will be completed before the workshop.
7.6 Discussion
Biomorphic robots can solve problems without the construction of explicit, symbolic world models. It is not necessary for such a robot to have a concept of, e.g., obstacles in order to avoid them. Instead, they can use evaluations of their inner state to exploit adaptively sensory signals to enhance their performance according to the internal measure. Using neural networks with continuous inputs avoids the problem of discretizing the state space. Moreover, networks based on a simple Hebbian learning rule are well suited for a hardware implementation, which is another aspect of our work [8].
Physical systems always have to handle noisy data, which makes symbolic information processing very difficult. Moreover, not only the environment might change but also the system itself. Therefore, mechanisms are needed that take the relevance of sensory cues into account. Sensors that become faulty through time can then be tuned out continuously. Autonomous agents should be as flexible as possible. Fixed sense-act mechanisms would require too much knowledge of the system's designer for each environment and each sensor class to be sufficiently effective in the end.
On the other hand, it is not necessary and not even useful to learn every kind of behavior. It seems obvious to directly implement innate knowledge which does not have to be changed for a lifetime. Biological systems show a broad variety of viable combinations of learning and innate knowledge. Insects, for example, only have a chance to survive when using many innate behavior patterns. Their shortness of life and their hostile environment do not allow many trials for learning. Nevertheless, insects are also able to adapt their behavior to an “abnormal” world.
Biological and technical systems often use “innate” knowledge for protective actions because experimental actions can be deadly in dangerous situations. Using a priorily given reflex for these situations is very effective. By this, the flexibility of actions is constrained minimally but the chance to survive is increased considerably.
Reflexes can be used to assign appropriate actions to dangerous situations, mechanisms of conditioning render anticipation of these actions possible and strengthen actions that enhance the internal state.
Operant conditioning or, more generally, reinforcement learning techniques rely on an evaluation function that assesses the momentary internal state of the system. This is the minimum of knowledge an agent must have if it is to enhance its performance within a lifetime.
Conditioning models are not limited to simple tasks such as obstacle avoidance as shown in Section 7.5.2: nonimplemented behaviors such as path following can emerge and, by providing the integration of self-motion, map building should also be possible. Further investigations will concentrate on the topics of place learning and the use of multilayer conditioning models for robot control. It should always be remembered, however, that even “higher” abilities which surely can be found in numerous animals are bound to the maintenance of the individual inner equilibrium. Thus, to make a biomorphic robot build a map requires keeping a certain equilibrium such that map building (or, better, perfect orientation) emerges.
Little work has been done so far to understand the conditions for stability and convergence of attentional conditioning models. But carefully studying stabilities and oscillations in learning processes of animals and robots will certainly shed more light on the constraints and possibilities of such fascinating systems.
Acknowledgments: Part of this work was supported by the DFG (Deutsche Forschungsgemeinschaft) and the Freie Hansestadt Bremen through the Graduiertenkolleg Raumorientierung und Handlungsorganisation autonomer Systeme. The authors further thank Prof. Michael Stadler for lively discussions on topics of this chapter. We also thank A. Giesert for improving the English, C. Herwig for helpful comments, and L. Geipel for providing the photograph.
Introduction: The Nature of Phonological Knowledge
I will assume, in what follows, that phonology as a discipline has, as its object of inquiry, a form of knowledge. On that assumption, the central questions that arise in the philosophy of phonology are: what is its nature? Is it entirely mind-internal? Is it intersubjective in nature? Is it, somehow, both of those? How do we come to possess such knowledge? If phonological knowledge is mind-internal, is there innate phonological knowledge, present in the mind at birth? What form would such innate knowledge take? Can such putatively innate phonological knowledge be coherently subsumed under a Chomskyan version of innate linguistic knowledge? If there is innate phonological knowledge, how much of phonological knowledge is acquired, rather than innate? Why? Is phonological knowledge distinct in kind from syntactic, morphological and semantic knowledge? What assumptions might lead us to think that it cannot be distinct? Or, if it is distinct, how and why does it differ from other kinds of linguistic knowledge? These issues are complex; they are connected to issues in (among other things) child development, neurophysiology, and issues in phonological theorising concerning (1) the phonetics/phonology distinction, (2) the interpretation of the notion ‘the linguistic sign’, and the notion ‘groundedness’, (3) the acquisition of phonological knowledge and the factors involved in it, including social interaction, (4) the status of social conventions, unconscious knowledge and implicit learning, (5) competence, performance and usage-based approaches to phonological knowledge, and (6) internalist and externalist conceptions of phonological knowledge. While I will endeavour to deal with each of these in turn, we will see that they are inter-connected in complex ways; discussion of any of these topics requires discussion of the others.
11.1 Introduction
It would be impossible to review the whole field of artificial intelligence (AI) in a few short pages—from its beginnings, the field in toto may be regarded as bio-inspired in attempting to emulate human reasoning, said humans being biological animals albeit with enhanced cognitive faculties. The AI program has evolved and has been peppered with paradigms and paradigm shifts, faster than any other academic field I can think of. Furthermore, these paradigm shifts have not yielded any single dominant paradigm, merely a multiplicity of paradigms embattled with each other. Every decade or so, a new paradigm joins the flock to declare its divine right as the foundation of cognition. Yet, a multiplicity of cognitive processes is probably likely—human cognition has evolved piecemeal involving many interlocking faculties in conflict with the illusion of the unity of consciousness. Marvin Minsky’s 1988 book Society of Mind was an early statement of this notion. His K-lines theory was essentially connectionist with linked nodes, each node represented by a simple frame. Cognitive dichotomies abound: inaccessible, unconscious, and procedural processes versus accessible, conscious, and declarative processes that are suited to connectionist and symbolic approaches, respectively (Holyoak and Spellman, 1993). Further complications occur such as in language acquisition involving an innate universal grammar combined with learning of the parameters of that grammar associated with specific languages. More generally, humans and other animals are limited to learning knowledge that conforms to specific structural regularities based on some naive physics—innate knowledge has been acquired through evolutionary processes and it shapes learned knowledge acquired through experience.
I shall focus on two dominant paradigms—symbolic processing and connectionism—that emerged early in the field and have been engaged in a spirited conflict as to which represents the key to human cognition, and ergo, the importation of intelligence into the machine. Connectionist models are adept at pattern matching, while symbolic systems encapsulate logic. Connectionist links serve to transmit activation signals between simple units rather than implementing structured relationships between symbols. Hence, the neurons of a typical neural network obviously cannot readily transmit structured symbolic information (Feldman and Ballard, 1982). Yet, symbols occur throughout biological systems—the genetic code is symbolic (Cariani, 2001). A symbol is an arbitrary designation that refers to an object, event, or relation in the outside world. It is a unitary concept but can be formed into composite symbol structures such as linguistic expressions. However, the early neural networks of McCulloch–Pitts neurons comprising binary maximum-of-sum threshold units implemented logic gates of AND, OR, and NOT with the appropriate weights and thresholds, indicating an early synergy between connectionism and symbolicism. A more recent variation on this theme is the adaptive connectionist expert system (ACES) built on a neural logic network that performs both pattern recognition and logical inferencing using past examples within a unified network architecture (Low et al., 1991). The neural logical network is a finite directed graph in which input nodes feed into output nodes with three logical values (TRUE, FALSE, and UNKNOWN). Each link is characterized by a number pair (x,y) representing both excitatory and inhibitory excitations rather than a single weight value with (1,0) for TRUE, (0,1) for FALSE, and (0,0) for UNKNOWN. The net excitatory and inhibitory weights for each link are summed independently. Logic gates may be implemented readily; for example, A → B may be implemented by two input nodes A and B feeding into a single output node with weights (− 1/2, − 2) and (2, ½). Initially, input nodes are assigned known values, while output nodes are assigned UNKNOWN values. The weights then implement logical inferencing to determine the values of the output nodes. Another approach is the neural tree network that comprises neuronal nodes connected in a decision tree architecture similar to ID3 (Sankar and Mammone, 1993). It thus combined neural networks with decision trees in a hybrid format. These constitute what might be regarded as integrated approaches to symbolic connectionism.
The physics of the outside world is determined by causal relations between objects and events (Hatfield, 1990). The connection between the representation and the referents is arbitrary—hence, the multitude of possible representations and their processes. The only constraint is that the representation and its evolution over time should match the represented real-world processes. In good old-fashioned AI (GOFAI), it is first-order logic that is employed to model the real-world states. In connectionism, it is learning from past correlations. The attraction of symbolic rule-based systems rooted in first-order logic is transparency of reasoning. Symbols are discrete, categorical representations of the external world. Symbols are defined in terms of their syntax and the operations on those symbols. Furthermore, symbols are specific representations that permit binding to a value (filler) that may be variable or constant. Furthermore, the same variable may occur in several places in a formula or sequence of formulae. In language, it is represented by the problem of assigning pronouns to their referred objects. Most importantly, symbolic reasoning based on logic offers the prospect of provability by virtue of its transparency.
In a connectionist system, different segments of a formula are represented independently within a network. Connectionism is based on neural networks that can be modeled by differential equations—they are based on finer grained microfeatures than symbols; that is, they are subsymbolic, intermediate between neural foundations and cognitive systems (Smolensky, 1988). Neural networks learn from example by generalizing from input–output pairings of the training data. Knowledge is represented as numerical weights. The neural network is commonly treated as a black box in which its internal mechanism is hidden from analysis. Such neural networks do not offer transparency in that knowledge and inferencing are distributed within a network as a set of microfeatures that by themselves are not readily analyzable—it is the interrelationships between these entities that are important. Subsymbolicism implies that connectionist processing involves microfeatures that are elements distributed through the network—they are not semantically interpretable into properties of the world unless constituted with other microfeatures into symbols through patterns of activation. Higher level cognition involves symbolic processing whereby cognition emerges from lower (subsymbolic) level of neurons and neural assemblies (Fodor and Pylyshyn, 1988). The neural network’s set of weights cooperate in a shared subsymbolic representation. The symbol then emerges as a collective cooperation between the distributed set of neurons. This form of representation is analogous to a hologram. Neural networks process truth values of instances to variables from which they learn a model theory (Komendantskaya, 2009). They offer the ability to learn from training examples through adaptation of their connection weights. Connectionism can determine the effect of an input on the output by tracking the error terms in each step of learning. The relative contributions of each input on output errors can thus be determined over time. The chief advantage of neural networks lies in their learning ability.
Rather than adding fuel to the fires of conflict that have been kept plenty-stoked for decades, I shall explore the narrow area of the peace-makers—symbolic connectionism (Wermter and Sun, 2000). Symbolic connectionism offers the prospect of combining the expressiveness of symbolic processing with the adaptability of connectionism. The key factor is in matching two different forms of representation of objects and events (referents) in the world. I believe that it is through these attempts to hybridize symbolic processing and neural networks that insights into cognition, both human and manufactured, may be gained. Furthermore, in a rather naive sense, the notion that the neural substrate implements symbolic processing in the brain is an obvious and natural one. In this view, symbolic connectionism implements a form of combined bottom-up (connectionist) and top-down (symbolic) processing that compensate for the disadvantages of each (Minsky, 1991). Or more positively, this reinforces the procedural strengths of symbolic processing with the adaptiveness of connectionist processing. Structured neural networks resemble semantic networks in representing knowledge as structures with relations between them. The activity level of each node/neuron constitutes the amount of evidence for the symbol. There is a natural mapping between goal-driven symbolic representations and subsymbolic neural computations (Polk et al., 2002). Attractor networks possess attractors as stable discrete states in a distributed network. Production rules are asymmetric associations between symbols that map onto excitatory or inhibitory connections between attractors representing conjunctions between states, possible actions, and subgoals. This emulates the role of the dorsolateral prefrontal cortex in problem solving which represents subgoals.
Symbolic methods assume that meaning is implicit in the relations between abstract symbols. Such abstract symbols are ungrounded in that they are not associated with perception–action events in the world. In this view, abstract symbols such as words create meaning through the conjunction of relations to other abstract symbols. This may be quantified by a vector encoding the many co-occurrences of symbol pairs in k-dimensional space (nominally k ~ 300–400). Searle’s Chinese room argument suggests that this is nonsense (Searle, 1980). Human cognition is fundamentally rooted in action-based behavior that is constrained and, indeed, dictated by the situation in the real world including the body of the agent (affordances). Furthermore, it has been shown experimentally that symbol grounding to referents in the real world must be implemented in principle (Glenberg and Robertson, 2000). Human cognition is consistent with meaning being derived from indexing arbitrary symbols (words) to real perception–action objects or events in the world (indexical hypothesis). The problem is how to discriminate between the variety of potential real-world referents in the world to which a symbol should be mapped. The only solution is to deny the utility of abstract symbols and replace them with perceptual symbols that are indexed directly to affordances. Meaning is inherent in perceptual experiences of the world and associated sets of bodily actions (affordances) applicable to the real-world situation. The relation between perceptions of the environment and actions in the environment may be modeled effectively through stimulus–response rules that may be learned neurally.
From these insights emerge symbolic connectionism. The chief advantage of symbolic connectionism is that it offers the prospect of combining a priori knowledge with new empirical knowledge.
3.1 Reward is enough for knowledge and learning
We define knowledge as information that is internal to the agent; for example, knowledge may be contained within the parameters of an agent's functions for selecting actions, predicting cumulative reward, or predicting features of future observations. Some of this knowledge may be innate (prior knowledge), while some knowledge may be acquired through learning.
An environment may demand innate knowledge. In particular, it may be necessary, in order to maximise total reward, to have knowledge that is immediately accessible in novel situations. For example, a new-born gazelle may need to run away from a lion. In this case, innate understanding of predator evasion may be necessary before there is any opportunity to learn this knowledge. Note, however, that the extent of prior knowledge is limited both in theory (by the capacity of the agent) and in practice (by the difficulty of constructing useful prior knowledge). Furthermore, unlike other abilities that we shall consider, environmental demand for innate knowledge cannot be operationalised; it is the knowledge that comes before experience, and therefore cannot be acquired from experience.
An environment may also demand learned knowledge. This will occur when future experience is uncertain, due to unknown elements, stochasticity, or complexity of the environment. This uncertainty results in a vast array of potential knowledge that the agent may need, depending on its particular realisation of future events. In rich environments, including many of the hardest and most important problems in natural and artificial intelligence, the total space of potential knowledge is far larger than the capacity of the agent. For example, consider an early-human environment. An agent may be born in either the Arctic or Africa, leading to radically different requirements for knowledge to maximise total reward: facing polar bears or lions; traversing glaciers or savannah; building with ice or with mud; etc. Particular events within the agent's life may also lead to radically different needs: choosing to hunt or to farm; facing locusts or war; going blind or going deaf; encountering friend or foe; etc. In each of these possible lives, the agent must acquire detailed and specialised knowledge. If the sum of such potential knowledge outstrips the agent's capacity, knowledge must be a function of the agent's experience and adapt to the agent's particular circumstances – thus demanding learning. In practice this learning may take many computational forms, for example making predictions, models or skills through the adaptation of parameters or the construction, curation and reuse of structure.
In summary, the environment may call for both innate and learned knowledge, and a reward-maximising agent will, whenever required, contain the former (for example, through evolution in natural agents and by design in artificial agents) and acquire the latter (through learning). In richer and longer-lived environments the balance of demand shifts increasingly toward learned knowledge.
6.1 Fuzzy logic control (FLC)
6.1.1 Operation
In FLC, a mathematical model of the system is not required. This in itself is a significant advantage because the uncertainties such as un-modelled physical quantities, non-linearity and unpredictable changes in operating point can be excellently dealt with [73,74]. However, it does require the designer to have some prior knowledge of how the output responds qualitatively to the inputs. The typical process structure of an FLC is shown in Fig. 6. The controller has three functional blocks namely fuzzification, rules inferences and defuzzification. In addition, it has a rule table in which the designed rules are stored. The process in which the FLC performs the calculation is called rule inference.
Fig. 6. Fuzzy logic control (FLC) process structure.
The inputs to a FLC-based MPPT are usually an error E and a change in error ΔE. Since dP/dV vanishes at the MPP, both inputs can be calculated as follows:
(21)E(k)=P(k)−P(k−1)V(k)−V(k−1)orE(k)=P(k)−P(k−1)I(k)−I(k−1)
(22)ΔE=E(k)−E(k−1)
Once E and ΔE are computed and converted to the linguistic variables, the FLC output can also be defined linguistically in terms of voltage (ΔV), current (ΔI) or duty cycle (ΔD). The linguistic variables for the output signal are usually assigned based on innate knowledge of the particular PV system being employed.
6.1.2 Previous work using FLC
In [75], an FLC with seven linguistic variables is used, while in [76] and [77], the membership functions are made less symmetric to give more significance to the specific linguistic variables. In an effort to overcome tracking deterioration with respect to changes in solar insolation experienced by [78], authors in [79] introduce the array power variation and duty cycle as the inputs for the FLC. Although, it performs satisfactorily in varying environmental conditions, it could not reduce the steady state error in the array power. Extending the idea of [78] and [79], authors in [80] propose a 3-input FLC based MPPT. The information derived from array current, power and duty cycle of converter are selected as the inputs to the FLC. Some improvements are seen in [81], where the authors employ the Fuzzy Cognitive Networks (FCN) to enhance the performance of [78]. The voltage, current, temperature, solar insolation and control variable (current) are used as the nodes of the FCN. The weights of the interconnection nodes are determined using a wide range of different climatic conditions. Once the FCN is trained, it can be mounted on any PV system. The proposed method results in better tracking speed, but at the expense of additional switch and a sensor.
In [82], the authors exploit the advantages of the FLC to improve the performance of the conventional HC method. The FLC is developed by translating the HC algorithm into 16 fuzzy rules. In addition, several authors utilise the idea of adaptive FLC. For instance, in [83], an adaptive FLC is proposed to facilitate the constant tuning of the membership functions and the rule based table in order to achieve optimum performance. In another work, [84], the scaling factor of both fuzzy inputs and output are automatically tuned to achieve the better dynamic performance of MPPT.
Authors in [85] propose a single input fuzzy logic controller (SI-FLC) by applying the “signed distance method” [86]. The input to SI-FLC consists of only one variable known as “distance”. This is in contrast to the conventional FLC, which requires an error and the derivative (change) of the error as its inputs. The reduction in the number of inputs simplifies the rule table to one-dimensional, allowing it to be treated as a single-input single-output controller.