| --- |
| title: "House Oversight: Estate Documents (Nov 12) (HOUSE_OVERSIGHT_013024)" |
| source: "House Oversight: Estate Documents (Nov 12)" |
| sourceUrl: "https://www.justice.gov/epstein" |
| date: "2026-01-01" |
| category: "House Oversight" |
| eftaNumber: "HOUSE_OVERSIGHT_013024" |
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| --- |
| |
| 108 |
| 6 A Brief Overview of CogPrime |
| top-level goals will be simple things such as pleasing the teacher, learning new information |
| and skills, and protecting the robot's body. Figure 6.3 shows part of the architecture via which |
| cognitive processes interact with each other, via commonly acting on the AtomSpace knowledge |
| Comparing these diagrams to the integrative human cognitive architecture diagrams given |
| in Chapter 5, one sees the main difference is that the CogPrime diagrams commit to specific |
| structures (e.g. knowledge representations) and processes, whereas the generic integrative archi- |
| tecture diagram refers merely to types of structures and processes. For instance, the integrative |
| diagram refers generally to declarative knowledge and learning, whereas the CogPrime diagram |
| refers to PLN, as a specific system for reasoning and learning about declarative knowledge. Ta- |
| ble 6.1 articulates the key connections between the components of the CogPrime diagram and |
| those of the integrative diagram, thus indicating the general cognitive functions instantiated by |
| each of the CogPrime components. |
| 6.3 Current and Prior Applications of OpenCog |
| Before digging deeper into the theory, and elaborating some of the dynamics underlying the |
| above diagrams, we pause to briefly discuss some of the practicalities of work done with the |
| OpenCog system currently implementing parts of the CogPrime architecture. |
| OpenCog, the open-source software framework underlying the "OpenCogPrime" (currently |
| partial) implementation of the CogPrime architecture, has been used for commercial applica- |
| tions in the area of natural language processing and data mining; for instance, see [GPPG06] |
| where OpenCogPrime's PLN reasoning and RelEx language processing are combined to do |
| automated biological hypothesis generation based on information gathered from PubMed ab- |
| stracts. Most relevantly to the present work, it has also been used to control virtual agents in |
| virtual worlds [GEA08]. |
| Prototype work done during 2007-2008 involved using an OpenCog variant called the Open- |
| PetBrain to control virtual dogs in a virtual world (see Figure 6.6 for a screenshot of an |
| OpenPetBrain-controlled virtual dog). While these OpenCog virtual dogs did not display in- |
| telligence closely comparable to that of real dogs (or human children), they did demonstrate a |
| variety of interesting and relevant functionalities including: |
| • learning new behaviors based on imitation and reinforcement |
| • responding to natural language commands and questions, with appropriate actions and |
| natural language replies |
| • spontaneous exploration of their world, remembering their experiences and using them to |
| bias future learning and linguistic interaction |
| One current OpenCog initiative involves extending the virtual dog work via using OpenCog |
| to control virtual agents in a game world inspired by the game Minecraft. These agents are |
| initially specifically concerned with achieving goals in a game world via constructing structures |
| with blocks and carrying out simple English communications. Representative example tasks |
| would be: |
| • Learning to build steps or ladders to get desired objects that are high up |
| • Learning to build a shelter to protect itself from aggressors |
| HOUSE_OVERSIGHT_013024 |
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