Artificial Neural Sensory/Short-Term/Long-Term/Emotional Memory Integration for Autonomous AI Systems

In order for an Artificially Intelligent System (AIS) to be truly Autonomous, we must provide the system with the abilities to acquire, categorize, classify, store, and retrieve information and knowledge, and provide abilities to infer or reason about the knowledge that it has stored. This drives the need for memory types that are similar to the different memories in the human brain (Crowder and Friess, 2010, 2011): • Sensory Memory (where raw, unprocessed information from sensors is buffered and initial pre-processing is accomplished) • Short-Term Memory (called “working memory” where new information from sensory memory is stored while it is processed and “reasoned about”) • Long-Term Memory (where permanent knowledge is stored through rehearsal, encoding, and memory association occurs) • Emotional Memory (memories about experiences, events, or information that cause “stress” within the AIS) Given these differing types of memories, the need for memory integration within the AIS becomes necessary. Memory integration takes information that is available within the AIS memories (what the system has sensed, learned, and ‘knows’) and continually broadcasts it to the cognitive center of the AIS and integrates it into current short-term memory to provide integrated knowledge, or ‘world data’, to the cognitive system. Learning takes three different forms within the AIS: • Learning to strengthen knowledge (gain a better understanding of things, topics, etc. that have been learned) • Learning to acquire knowledge (understanding new information, new topics, etc., that have not been previously experienced or learned) 1 Raytheon Intelligence and Information Systems, Aurora, CO 80011 2 Relevant Counseling, Aurora, CO 80011 • Learning to construct knowledge (create a knowledge representation in our memories) create meaningful connections between knowledge Presented here is a framework and architecture for integration of AIS artificial memories, based on Dr. Mayer’s work in Constructivist Learning (Mayer, 2004), as applied to artificial neural memories. The notions of select, organize, and integrate will be discussed for each type of memory. In particular, of the major functionalities within the AIS short-term memory attention loop, the Spatio-Temporal Burst Detector will be discussed. Within short-term memory, information is ordered in terms of its spatial and temporal characteristics. For the AIS, spatial and temporal transition states are measured in terms of mean, mode, velocity, and acceleration, and are correlated between their spatial and temporal characteristics and measurements. Here we look for rapid increases in temporal or spatial characteristics that may trigger an inference or emotional response from the AIS cognitive processes. State transitions bursts are ranked according their weighting (velocity and acceleration), together with the associated temporal and spatial characteristics. Burst detection and processing of the measurements may play an important role for identifying relevant topics, concepts, and/or inferences that provide contexts, and are important in overall memory integration. I. Artificial Neural Memories Memories involve the acquisition, categorization, classification and storage of information. The purpose of memory is to provide the ability to recall information and knowledge as well as events. We base our current understanding of the world around us on what we have previously learned, and chosen to store. Everything that we do in the present relies on memories of what has happened, or what has been learned in the past, unless new memories must be created for a new experience/information. Without our memories, we would not be able to go through day-to-day living, using abstract thought and performing the most basic functions. Without memories we wouldn’t be able to drive a car, brush our teeth, or perform any of the things we do “without thinking about them.” Through our conceptual recollection of the past we are able to communicate with other people. However, the human brain does not store files in neat folders or in relational databases, but instead stores information as fragments that are used to construct memories when needed. Hence, we propose a binary information fragment encoding system for use in artificial intelligence systems that can be utilized to create a combined sensory perceptive, short-term, and long-term memory system similar to how the human brain dynamically stores and recalls (reconstructs) memories. In order to design, develop, and implement an Artificially Intelligent System (AIS) to be truly autonomous, it must be provided with dynamic memory abilities (Crowder 2010a). Memories are typically classified into three different types: Sensory, Short-Term, and Long-Term (Miller 2002, Newell 2003). Each memory type has several instantiations, dealing with different types of information. Here we will explore each type of memory system and its implications to the AIS. We begin our discussion of memory types with a look at the relationships between the three main types of memories. Figure 1 illustrates an AIS Memory Upper Ontology, similar to human memory systems, describing these relationships (Eichenbaum 2002). Figure 1 – AIS Memory Upper Ontology 1.1 Sensor Memories The Sensory Memory within the AIS memory system are those memory registers where raw, unprocessed information ingested via AIS environmental sensors are buffered to begin initial processing. The AIS sensory memory system has a large capacity to accommodate large quantities of possibly disparate and diverse information from a variety of sources (Crowder 2010). And although it has a large capacity, it has a short duration. The information that is buffered in this sensory memory must be sorted, categorized, turned into information fragments, metadata, contextual threads, and attributes (including emotional attributes) and then sent on to the working memory (Short-Term Memory) for initial cognitive processing. This cognitive processing is known as Recombinant Knowledge Assimilation (RNA), where raw information content is discovered from the information domain, decomposed and reduced, compared, contrasted, and associated into new relationship threads within a temporary working knowledge domain and subsequently normalized into pedigree within the knowledge domain for future use (Carbone 2010). Hence, based upon the information gathered in initial Sensory Memory processing, Cognitive Perceptrons, manifested as Intelligence information Software Agents (ISAs), are spawned, as in relative size swarms, to create initial “thoughts” about the data. Subsequently, hypotheses are generated by the ISAs. The thought process information, along with the ISA sensory information are then sent to a working memory region which will alert the artificial cognition processes within the AIS to begin processing. (Crowder and Friess 2010a&b;). Figure 2 illustrates the Sensory Memory Lower Ontology. Figure 2 – Sensory Memory Lower Ontology 1.2 Short-Term Artificial Memories Short-Term or “Working” memory within the AIS is where new information is transitionally stored in a Temporary Knowledge Domain (Carbone 2010) while it is being processed into new Knowledge. This follows the paradigm that information content has no value until it is thought about (Brillouin 2004). Short-Term memory is where most of the reasoning within the AIS happens. Short-Term memory (STM) provides a major functionality, called “rehearsals” that allows the AIS to continually refresh, or rehearse, the Short-Term memories while they are being processed and reasoned about, so that memories do not degrade until they can be sent on to Long-Term Memory and acted upon by the artificial consciousness processes within the AIS’s cognitive framework (Crowder and Carbone 2011a). It should be noted that Short-Term memory is much smaller in relative space needed to process information content as compared to long term memory. Short-Term memory should be perceived not necessarily as a physical location, as in the human brain, but rather as a rapid and continuous processing of information content relative to a specific AIS directive or current undertaking. One must remember that the ShortTerm memory, which includes all external and internal sensory inputs, will trigger a rehearsal if the AIS discovers a relationship to either a previously interred piece of information content in Short-Term or Long-Term memory. Figure 3 illustrates the Short-Term Memory Lower Ontology for the AIS. 1.3 Long-Term Artificial Memories Long-Term Memory (LTM), in the simplest sense, is the permanent Knowledge Domain where we assimilate our memories (Carbone 2010). If information we take in through our senses doesn’t make it to LTM, we can’t and don’t “remember” it. Information that is processed in the STM makes it to LTM through the process of rehearsal, processing, encoding, and then association with other memories. In the brain, memories are not stored in files, or in a database. Memories, in fact, are not stored as whole memories at all, but instead are stored as information fragments. The process of recall, or remembering, constructs memories from these information fragments that are stored in various regions of the brain, depending on the type of information. In order to create our AIS in a way that mimics human reasoning, we follow the process of storing information fragments and their respective encoding in different ways, depending on the type and context of the information, as discussed above. Each simple discrete fragment of objective Knowledge includes an n-dimensional set of quantum mechanics-based mathematical relationships to other fragments/objects bundled in the form of eigenvector optimized Knowledge Relativity Threads (KRT) (Carbone 2010) (Carbone and Crowder, 2011).These KRT bundles include closeness, and relative importance value among others. This importance is tightly coupled, per the math, to the AIS emotional storage as a function of desire or need, as described in Figure 4, where the LTM Lower Ontology is illustrated. Figure 3 – AIS Short-Term Memory Lower Ontology There are three main types of LTM (Crowder 2010a): • Explicit, or Declarative Memories • Implicit Memories • Emotional Memories 2. Artificial Memory Processing and Encoding 2.1 Short-Term Artificial Memory Processing In the human brain, STM corresponds to that area of memory associated with active consciousness, and is where most of the cognitive processing takes place. It is also a temporary storage and requires rehearsal to keep it fresh until it is compiled into Long-Term Memory (LTM). In the AIS, the memory system does not decay over time, however, the notion of “memory refresh” or rehearsal is still a valid concept as the Artificial Cognitive Processes work on this information. However, the notion of rehearsal means keeping track of “versions” of STM as it is being processed and evaluated by the artificial cognition algorithms. This is illustrated in Figure 5, the AIS STM Attention Loop. There are three distinct processes that are handled within the STM that determine where information is transferred after cognitive processing (Crowder 2010a). Figure 4 – Artificial Long-Term Memory Lower Ontology This processing is shown in Figure 6. The Artificial STM processing steps are: • Information Fragment Selection: this involves filtering the incoming information from the AIS Artificial Preconscious Buffers into separable information fragments and then determining which information fragments are relevant to be further processed, stored, and acted on by the cognitive processes of the AIS as a whole. Once information fragments are created from the incoming sensory information, they are analyzed and encoded with initial topical information, as well as Metadata attributes that allow the cognitive processes to organize and integrate the incoming information fragments into the AIS’s overall LTM system. The Information Fragment encoding creates a small, Information Fragment Cognitive Map that will be used for the organization and integration functions. • Information Fragment Organization: these processes within the Artificial Cognition framework create additional attributes within the Information Fragment Cognitive Map that allow it to be organized for integration into the overall AIS LTM framework. These attributes have to do with how the information will be represented in LTM and determine how these memory fragments will be used to construct new memories, or recall, memories later by as needed by the AIS, using Knowledge Relativity Thread representation to capture the context of the Information Fragment and each of it’s qualitative relationships to other fragments and/or bundles of fragments already created. • Information Fragment Integration: Once the Information Fragments within the STM have been KRT encoded, they are compared, associated, and attached to larger, Topical Cognitive Maps that represent relevant subject or topics within the AIS’s LTM system. Once these Information Fragment Cognitive Maps have been integrated, processed, and reasoned about, including emotional triggers or emotional memory information, they are sent on to both the LTM system, as well as the AIS Artificial Prefrontal Cortex to determine if actions are required. Figure 5 – Short-Term Artificial Memory Attention Loop One of the major functions within the STM Attention Loop is the Spatio-Temporal Burst Detector. Within these processes, Binary Information Fragments (BIFs) are ordered in terms of their spatial and temporal characteristics. Spatial 3 and Temporal transitions states are measured in terms of mean, mode, median, velocity, and acceleration and are correlated between their spatial and temporal characteristics and measurements. Rather than just looking at frequencies of occurrence within information, we also look for rapid increases in temporal or spatial characteristics that may trigger an inference or emotional response from the cognitive processes. It is not that an AIS system processes information content differently based upon how rapidly content is ingested, it is simply that an AIS must be able to recognize instances when information content might seem out of place within the context of a situation: e.g., a single speeding car within a crowd of hundreds of other cars. An AIS, not only optimizes its processing on the supply side of the knowledge economy, but has to recognize, infer, and avoid distraction on what focuses the demand side of its knowledge economy places upon operations and directives. State transition bursts are ranked according to their weighting (velocity and acceleration), together with the associated temporal and/or spatial characteristics, and any triggers that might have resulted from this burst processing (LaBar and Cabeza 2006). This Burst Detection and 3 Spatial in this reference can be geographically (either 2-D or 3-D), cyber-locations, or other characteristics that may be considered “spatial” references or characteristics. processing may help to identify relevant topics, concepts, or inferences that may need further processing by the Artificial Prefrontal Cortex and/or Cognitive Consciousness processes (Crowder and Friess 2011 a&b;). Figure 6 – AIS Information Fragment Encoding Once processing within the STM system has completed and all memories are encoded, mapped to topical associations, and their contexts captured, their knowledge relativity thread bundled representations are created and are sent on to the Cognitive Processing engine Memories that are deemed relevant to “remember” are integrated into the Long Term Memory system. 2.2 Long-Term Artificial Memory Processing The overall AIS High-Level memory architecture is shown in Figure 7. The one thing of note is the connection between Emotional memories and both Explicit and Implicit memories. Emotional Memory carries both Explicit and Implicit characteristics. Explicit or Declarative Memory is utilized for storage of “conscious” memories or “conscious thoughts.” Explicit memory carries those information fragments that are utilized to create what most people would “think of” when they envision a memory. Explicit memory stores things, i.e., objects, and events, things that are experienced in the person’s environment. Information fragments stored in Explicit Memory are normally stored in association with other information fragments that relate in some fashion. The more meaningful the association, the stronger the memory and the easier the memory is to construct/recall when you choose to (Yang and Raine 2009). In our AIS, Explicit Memory is divided into different regions, depending on the type or source of information. This division of regions occurs because different types of information fragments within the AIS memories are encoded and represented differently, each with its own characteristics that make it easier to construct/recall the memories later when the AIS needs the memories. In the AIS LTM, we utilize Fuzzy, Self-Organizing, Contextual Topical Maps to associate currently processed Information Fragments from the STM with memories stored in the LTM (Crowder, Scally, and Bonato 2011). Figure 7 – High-Level Artificial Memory Architecture LTM information fragments are not stored in databases or as files, but encoded and stored as a triple helix of continuously recombinant binary neural fiber threads that represent: • The Binary Information Fragment (BIF) object along with the BIF Binary Attribute Objects (BAOs). • The BIF Recombinant Knowledge Assimilation (RNA) Binary Relativity Objects. • The Binary Security Encryption Threads. Built into the RNA Binary Relativity Objects are Binary Memory Reconstruction Objects, based on the type and source of BIF, that allow memories to be constructed for recall purposes. There are several types of Binary Memory Reconstruction Objects, they are: • Spectral Eigenvectors that allow memory reconstruction using Implicit and Biographical LTM BIFs • Polynomial Eigenvectors that allow memory reconstruction using Episodic LTM BIFs • Socio-Synthetic Autonomic Nervous System Arousal State Vectors that allow memory reconstruction using Emotional LTM BIFs • Temporal Confluence and Spatial Resonance coefficients that allow memory reconstruction using Spatio-Temporal Episodic LTM BIFs • Knowledge Relativity and Contextual Gravitation coefficients that allow memory reconstruction using Semantic LTM BIFs 3. Constructivist Learning In the view of constructivist, learning is a constructive process in which the learner is building an internal illustration of knowledge, a personal interpretation of experience. This representation is continually open to modification, its structure and linkages forming the ground to which other knowledge structures are attached. Learning is an active process in which meaning is accomplished on the basis of experience. This view of knowledge does not necessarily reject the existence of the real world, and agrees that reality places constrains on the concepts that are, but contends that all we know of the world are human interpretations of our experience of the world. Conceptual growth comes from the sharing of various perspectives and the simultaneous changing of our internal representations in response to those perspectives as well as through cumulative experience (Bednar, Cunnigham, Duffy, Perry, 1995). When considering an AIS we have to ask ourselves “what is reality?” Think about humans. Each person has an experience of an event. Each person will see reality differently and uniquely. There is also world reality. This world reality may be based on fact or perception of fact. In fact, we construct our view of the world, of reality, from our memories, our experiences. For further thought let’s then consider Construct Psychology. According to “The internet Encyclopedia of Personal Construct Psychology” the Constructivist philosophy is interested more in the people’s construction of the world than they are in evaluating the extent to which such constructions are “true” in representing a presumable external reality. It makes sense to look at this in the form of legitimacies. What is true is factual legitimate and what is people’s construction of the external reality is another form of legitimacy. Later on we can consider the locus of control in relation to internal and external legitimacies or realities. You are correct if you are thinking that AIS is not human and will not have human perceptions. Artificially cognitive systems may have their own perceptions and realities, although it is important that the cognitive systems and memories have the abilities to construct correct views of the world around it if we are to rely on them. Thus, a mentor will be necessary. That mentor will need to understand the artificial cognitive system, the AIS, and be able to understand the AIS in a human way, a human reality. After all, isn’t this what makes the AIS autonomous? Constructive psychology is a meta-theory that integrates different schools of thought. According to the above cited article: Hans Vaihinger (1852-1933) asserted that people develop “workable fictions”. This is his philosophy of “As if” such as mathematical infinity or God. Alfred Korzybski’s (18791950) system of semantics focused on the role of the speaker in assigning meaning to events. Thus constructivists thought that human beings operated on the basis of symbolic or linguistic constructs that help navigate the world without contacting it in any simple or direct way. Postmodern thinkers assert that constructions are viable to the extent that they help us live our lives meaningfully and find validation in shared understandings of others. We live in a world constituted by multiple realities social realities, no one of which can claim to be “objectively” true across persons, cultures, or historical epochs. Instead, the constructions on the basis of which we live are at best provisional ways of organizing our “selves” and our activities, which could under other circumstances be constituted quite differently. According to “Adlerian Therapy as a Relational Constructivist Approach” the Adlerian perspective affirms the emphasis on the importance of humans as active agents in creatively involved in the construction of their own psychology. Martic and Sugarman’s (1997) position is “...although humans exist in a socio-cultural world of persons, a distinguishing characteristic of personhood is the possession of an individual agentic consciousness. The article goes on to say “...if there is no self-reflexive individual and situatedness is indeed inescapable, then it is a spurious notion to think we can engage in what Gergen (1999) called the “emancipator potential of discourse analysis, that is inquiry which causes us to reflect critically and creatively on our own forms of life.”” Also, Adlerian therapy accounts for both the social-embedded nature of human knowledge and the personal agency of creative and self-reflective individuals within relationships. According to “Personal Construct Psychology, Constructivesm, and Postmodern Thought” (Luis Boteela at http://www.massey.ac.nz/ -alock/virtual/Construc.htm) there are three main areas to consider. Psychological knowledge, psychological practice, and psychological research. Consider pshcyological knowledge. This article Mahoney, 1991 (p.451) “knowledge cannot be disentangled from the process of knowing, and all human knowing is based in value-generated processes”. Next consider psychological research. In postmodern terms, research is not viewed as a mapping of some objective reality, but as an interactive co-construction of the subject investigated (Kvale, 1992b) This conversational and interpretive view of psychological research requires a muti-method approach, fostering the use of hermeneutic, phenomenological, and narrative methodologies. According to the above named article Constructivist meta-theory assumes that knowledge is a hypothetical (anticipatory) construction. Thus, it departs from the traditional objective conception of knowledge as an internalized representation of reality. Epistemic values vary according to constructivist theories; all of them can be viewed as alternatives to the justification position. This leaves constructivist meta-theory facing the task of articulation an alternative set of epistemic values, taking into account that values are, by definition, subjective preferences. Two of these epistemic values correspond to a) the pragmatic value of knowledge claims (i.e. their predictive efficiency, viability, and fertility), and b.) the coherence of knowledge claims (i.e. their internal and external consistency, and unifying power.) This article also goies on to say that according to Maturana and Varely (1987) living beings are autopoietic (self-creating or self-producing) systems in the sense that they are capable of maintaining “their own organization, the organization which is developed and maintained being identical with that which performs the development and maintenance. (Andrews, 1979 p 359. The notion of autopoisis is similar to Mahoney’s (1988) concept of morphogenic nuclear structure, and is supported by Von Oester’s (1984) contention that the central nervous system operates as a closed system organized to produce a stable reality. Other constructive see the nervous system as open and effected by social influence. Lastly this article goes on to say that organisms interact by means of structural coupling, i.e. codrifting and setting up the mutual conditions for effective action. Although there is question as to whether Piaget’s schemas are actually constructivist, his theory may be useful for AIS. The schema may equate to the constraints given to AIS to start. Many articles talk about Piaget as construct theory. For the AI Constructivist Learning, the AI cognitive learning process is a building (or construction) process in which the AI’s cognitive system builds an internal illustration of knowledge, based on its experiences and personal interpretation (fuzzy inferences) of experience. The knowledge representation and knowledge relativity threads within the cognitive system’s memories are continually open to modification, and the structure and linkages formed within the AI’s short-term, long-term, and emotional memories, along with the contextual knowledge relativity threads, form the bases for which knowledge structures are created and attached to the Binary Information Fragments. Learning becomes a very active process, where meaning is accomplished through experience, combining structural knowledge (knowledge provided in the beginning) with constructivist knowledge to provide the AIS’ view of the “real world” around it. Conceptual growth within the autonomous AIS would come from collaboration among all AIS ISAs within the system, sharing their experiences and inferences... the total of which creates changing interpretations of their environment through their collective, cumulative experiences. Therefore, one of the results of the Constructivist Learning process within the AIS is to gradually change the “Locus of Control” from external (the system needed external input in order to make sense, or infer, about its environment) to internal (the system having a cumulative constructive knowledge-base of information, knowledge, context, and inferences to handle a given situation internally – meaning able to make relevant and meaningful decisions and inferences about a situation without outside knowledge or involvement. It might be possible to pose specific goals for the AIS to cause it to “Construct” knowledge about a subject or situation to aid in its learning process as the system evolves. It may be possible to provide a “real-world context” for the AIS, giving it the cognitive knowledge to understand where its “Locus of Control” should be internal vs. external, and when it can make that shift in its understanding. I believe this follows theories of human cognition and is possible through the use of the learning system we have created and the Metacognitive and Metamemory Constructs we have already developed – along with Occam and PAC learning methods. This, combined with the Cognitive Economy concepts – provides the final pieces of the AIS fully autonomous, cognitive framework required for completely autonomous environmental interaction, evolution, and control by the AIS. 3.1 Adaptation of Constructivist Learning Concepts to an AIS • Learning to strengthen knowledge (gain a better understanding of things, topics, etc. that have been learned) o Role of the learning management systems: Administering learning goals and constraints o Role of the learning algorithms: Measures of Effectiveness against goals and constraints Utilizes hypothesis testing from hypotheses generated by knowledge acquisition learning system o Function of the learning in this role: increase in stimulus-response-feedback for this strengthened knowledge within the Cognitive Conceptual Ontology o Focus: addition of behaviors/information to current memories. Addition of contextual threads to current memories. Addition of emotional memory triggers. Addition of procedural memories. • Learning to acquire knowledge (understanding new information, new topics, etc., that have not been previously experienced or learned) o Role of the learning management system: present new information/concepts to be learned from sensor information correlated with current Conceptual Ontology. o Role of the learning algorithms: receive and process information in order to form new concept(s) that must be included in Conceptual Ontology (Occam Learning algorithms) o Function of the learning in this role: Create new concepts, find fundamental concept that can be learned about this new information and generate hypotheses about concept for knowledge strengthening learning system to utilize when new information is available. o Focus: Creation of procedural memories. Creation of initial information fragments. • Learning to construct knowledge (create a knowledge representation in our memories) create meaningful connections between knowledge o Role of the learning management system: cognitive guidance and modeling. Deconstruct information into manageable information fragments, correlation (integration) into current memory fragment structure. Encoding of memory fragments, based on Recombinant kNowledge Assimilation (RNA) Threads and Information Encoding schemas. o Role of the learning algorithms: reasoning and analysis of data to determine stimulus/response to goals and constraints. Making sense of the information and constructing knowledge representations. o Functions of the learning in this role: Create meaningful information fragment representations and contextual threads that allow assimilation into long-term memories. Memory organization and integration. o Focus: Constructivist learning (active learning) utilizing a variety of cognitive processes (reasoner and analyst agents) during the learning process. Construction of emotional contexts. 4. Conclusions and Discussion Described here are memory processing and encoding methodologies to provide Artificially Intelligent Systems with memory architectures, processing, storage, and retrieval constructs similar to human memories. We believe these are necessary to provide artificial cognitive structures that can truly learn, reason, think, and communicate similar to humans. There is much work to do and our current research will provide the software processing infrastructure for the Information Software Agents necessary to create the underlying cognitive processing required for this Artificial Neural Memory System (ANMS). Below we give an example of memory reconstruction utilizing this ANMS, for reconstruction of image memories. 4.1 Implicit and Biographical Memory Recall/Reconstruction Utilizing Spectral Decomposition Mapping We create non-uniform expanding fractal decomposition of the image to be “remembered.” We utilize the right and left Eigenvectors of the Pollicott-Ruelle resonances to determine the separable Pictorial Information Fragment (PIF) objects. The resulting singular fractal functions form fractal spectral representations of the PIFs. These Binary Fractal Representations are stored as the Binary Information Fragments for the image. The reconstruction uses these PIFs to create a piece-wise linear image memory reconstruction, although the individual PIFs can be utilized in other memory and cognitive processes, such as to perform pattern matching and/or pattern discovery. The proposed high-level architecture for the ISA cognition and memory system is illustrated in Figure 8. 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