Patent Application
20230316201
The subject matter described herein relates to computer-implemented techniques and specification for a computer-implemented Dilemma and Uncertainty Planning and Control System (DUPCS) for selectively configuring constrained resource systems in a modular team-system to produce one or more effects for increasing uncertainty and computer-implemented dilemma plans so as to degrade a competitor's ability to gauge its own performance in a shared environment or to undertake harmful actions.
Increasing amounts of information about companies and other organizations and entities are available online and via various data feeds (including proprietary data feeds). This information can characterize the operations of such companies and other organizations and entities (e.g., competitors, opposing military, etc.) including historical actions and performance as well as projected future performance.
In a first aspect, contextual data characterizing a current state of resources of an entity is received and processed. In addition, data specifying desired adversarial effects against the entity is received and processed. The contextual data and desired adversarial effects are used to generate an effect-web plan comprising a plurality of effects, actions and task plans to implement the desired adversarial effects and a dilemma topology comprising a graph specifying a plurality of dilemmas to impose when prespecified conditions are met. Available team-systems to execute the effect-web plan and dilemma topology are then determined (i.e., identified, etc.) which results in the generated effect-web plan being deployed by a selected team-system and at least one dilemma based on the dilemma topology graph being imposed. Data characterizing the multi-order impact of the deployment of the effect-web plan on the competitor and the imposition of the at least one dilemma are monitored. In some implementations, the multi-order impact of the deployment of the effect-web plan on the competitor and the imposition of the at least one dilemma can be visualized in a graphical user interface (e.g., a dashboard, etc.). One more of the generated effect-web plan or the dilemma topology can be modified and deployed based on the monitoring to increase a likelihood of an occurrence and success of the desired adversarial effects.
The resources of the entity can include one or more of: people, processes, or technology resources of the entity.
The team-systems can include an ensemble of information gatherer systems, network builder systems and performer systems.
The generated effect-web plan and dilemma topology can be deployed as part of a computer-implemented simulation.
The available team-systems can be determined using a multi-objective constraint optimization algorithm. The available team-systems can be ranked using one or more machine learning models or other techniques and the top ranked team-system can be selected (or top team-systems selected).
The contextual data can take various forms including competitor identification and state data and/or executive strategy data and/or effect instrumentation data.
The contextual data can be received from one or more of a gatherer system data feed, a network builder system, or a performer system. In addition or in the alternative, the contextual data can comprise simulation data.
In some variations, a plurality of sensors (or data feeds) can be deployed to obtain the contextual data. The sensors can take various forms including hardware-based sensors including a processor and memory and/or software-based sensors configured to obtain data and synthesize contextual data from differing data sources. The sensors can provide the monitored data for effect instrumentation.
The received contextual data can include competitor state variables. With these variations, the generation of the effect-web plan and dilemma topology can include applying game theory to specify one or more computer-implemented identified actions to evaluate the competitor state variables operating within a pre-defined range.
The effect-web plan and dilemma topology can be generated so as to influence a subset of the competitor state variables comprising end-state variables. Effects deployed by the effect-web plan can cause one or more of the end-state variables to change over time in a desired direction as quantified through an uncertainty score.
New tasks and schedules can be formulated by iteratively matching constraints applicable to the information gatherer systems, performer systems and/or network builder systems within the team-system. These formulated new tasks and schedules can be deployed by the team-system configuration in a simulated or real environment.
Control on uncertainty associated with end-state variables can be established such that at least one of the deployed effects targets the competitor state variables having the associated uncertainty in an operating range of the competitor state variables.
Systems are also provided for computer-implemented dilemma and uncertainty planning having at least one data processor and memory storing instructions to implement various aspects as provided herein. The systems can also include a plurality of information gatherer systems, a plurality of network builder systems, and/or a plurality of performer systems.
Non-transitory computer program products (i.e., physically embodied computer program products) are also described that store instructions, which when executed by one or more data processors of one or more computing systems, cause at least one data processor to perform operations herein. Similarly, computer systems are also described that may include one or more data processors and memory coupled to the one or more data processors. The memory may temporarily or permanently store instructions that cause at least one processor to perform one or more of the operations described herein. In addition, methods can be implemented by one or more data processors either within a single computing system or distributed among two or more computing systems. Such computing systems can be connected and can exchange data and/or commands or other instructions or the like via one or more connections, including but not limited to a connection over a network (e.g., the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, or the like), via a direct connection between one or more of the multiple computing systems, etc.
The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and advantages of the subject matter described herein will be apparent from the description and drawings, and from the claims.
The current subject matter is directed to advanced computer-implemented techniques and computing architectures for instituting effect-web plans by an entity A, also known as DUPCS “owner” for an entity B. Entity B, can for example, be A's business competitor or other form of adversary. The effect-web plans can result in a desired adversarial effect or result such as increasing uncertainty and confusion for the entity B to disrupt B's normal state or a decrease in gauging B's own performance in a shared marketplace. A shared marketplace, as provided herein, is an environment in which performer systems, network builder systems and information gatherer systems exchange information, goods and services in a zero-sum game for incentives such as an increase in geographical territory or increased number of consumers of information, goods and services. Examples of a shared marketplace include business markets for incentives of greater product penetration, social media for incentives such as dominant narratives, or military battlespace with incentives such as capture of adversary territory.
The current subject matter is also directed to computer-implemented dilemma topology engineering. In this context, dilemma refers to a situation in which a difficult choice has to be made between two or more alternatives, especially equally undesirable ones—situational judgements made under duress that have a higher probability of error, and thereby, a higher probability of disadvantage. In a competitive context, to impose a dilemma is to cause a situational judgement under stress with an expectation of a higher probability of competitor (or enemy) error (or a worse outcome than expected). Further, the current subject matter is directed to dilemma topology which specify a relationship between existing dilemmas and their interdependency describing the dilemma landscape/space. Related topology engineering can be implemented to ensure dilemma space is never empty across the campaign timeline. Further, dilemma shaping can be deployed which provides scientific methods to establish controls for navigating and designing dilemma topologies.
As used herein, the term “owner” (entity A) can refer to the initiator of the algorithms provided herein in relation to one or more competitors. The term “competitor state variable” (CoSV) can refer to a variable that corresponds to some aspect of an entity B's overall state or a state that pertains to a particular issue, circumstance, people, process and/or technology. The term “range of CoSV” can refer to the operating range of the variable. Range of CoSV can be either qualitative or quantitative but not both. A qualitative range is a Boolean state (true/false encoded as 1 or 0). A quantitative range can be bounded within two thresholds: [lowerbound, upperbound], encoded as a floating-point number. The term “end state variable” (EnSV) can refer to a subset of CoSV that the owner intends to influence and has the technology and the means to do so. The owner typically has incomplete information about the corresponding CoSV operating range. Uncertainty of CoSV can refer to the difference between the bounded ranges of EnSV and CoSV variables. “Game theory strategies” can refer to the owner contextualizing competitor response per a library of game theory strategies such as Tit for Tat, Tit for 2 Tats, and many others. “Competitor state” CoSV for owner's use and targeted influence can be termed as a set of EnSVs. “Effect” can refer to a set of EnSVs that the owner can influence, instrument, and monitor in a spatiotemporal manner by its actions. Effect can be qualitatively denoted by: <no effect, partially effective or fully effective> and quantitatively denoted by Effect_n_i_score, where n_i is the ith effect in a set of n effects. “Effect-Web” can refer to a set of effects with spatiotemporal characteristics. “Effect-Web state” can refer to an effect-web caused by the owner and can qualitatively result in: <no effect, partially effective or fully effective> and be quantitatively expressed as effect_web_score. “Dilemma” can refer to a set of EnSVs that can be affected concurrently with one or more effects. This subset is selected deliberately to bundle actionable EnSVs that contribute to an increased uncertainty at the competitor's end. An effect may or may not lead to dilemma if the degree to which an EnSV could be affected does not result in a response from the competitor. Each effect has a response indicator, which based on the owner's understanding of the competitor, determines if the effect has been successful, partially successful or failed, as quantified by Effect_n_i_score. Similarly, dilemma response indicators determine if a subset of EnSVs grouped as a “dilemma_i” match a quantified target UncertaintyScore. “Network builders systems” can comprise a set of systems that provide communication and control mechanisms to influence EnSVs. “Performer systems” can refer to a set of systems that produce an effect to indirectly influence EnSV ranges and uncertainty score. “Information gatherer systems” can refer to a set of systems that provide instrumentation (data feeds) to monitor EnSVs. “Action” can refer to an act that may be taken by a performer system to produce an effect. “Task” can refer to an action that is scheduled for a performer system in the future. “Team-system” can refer to a configuration of information gatherer systems, performer systems and network builder systems that collectively, as an Acknowledged SoS, act to deliver an effect.
A subset of variables in 201 is further analyzed for their domain and range values in 202 to arrive at a set of mathematical functions CoSV(x_i) that identifies relationship between domain and range values, where x denotes a competitor x and i denotes a CoSV from a set of CoSVs for competitor x. A domain of CoSV(x_i) is the set of values for which the function CoSV(x_i) is defined. For example, let there be a competitor or adversary with a radar asset that has a maximum range of detection of 100 miles and is protected by munitions that can counterattack. A radar for an adversary is a competitor asset that provides deterrence. Let CoSVx_1=range of radar detection and CoSVx_2 be the number of munitions for counterattack are the two CoSVs. Let the known maximum value of CoSVx_1 be 100 miles and the maximum value of CoSVx_2 be 70. Accordingly, function CoSVF(x,1) is defined. A function f(x) such as CoSVF(x,1) can be “to decrease the detection range of radar”. Similarly, CoSVF(x,2) can be “reduce the number of munitions”. Further, the domain of CoSVF(x,1) can take the values: (destroy, degrade), that could fulfil the goal of decreasing the detection range. These results can be the two effects that will be requiring planning. Likewise, the domain values for CoSVF(x,2) can take value: (expend at least 90% of munitions). A range of function CoSVF(x,1) is the set of values CoSV(x_i) can take for a given set of domain values. For each of the identified domain values for CoSVF(x,1), viz., the range values for the domain: “destroy” can result in the interval [0,5] miles, and the range values for the domain: “degrade” can result in the interval [0,50] miles. Similarly, the range values for “expend” domain for CoSVF(x,2) can take the values in the interval [0,7]. From this set of CoSVF(x, i) functions, a subset can be manually selected in 204 that the DUPCS has the technology and means to affect. This selection can be based on multiple criteria such as technology, geography, understanding of various competitor variables and timeframes. For the running example, of these two CoSVs, CoSVF(x,1) can be selected for further consideration. Through manual input in 203 that specifies the time horizon to impact a CoSV(x_i) and a manual selection of a subset of j CoSVs from the set of i CoSVs, a set of end-state state variable functions EnSV(x_j) for different time horizons in 205 are defined that DUPCS can affect. For the current example, let the time horizon be in the interval [1,6] hours and EnSV(x,1)=CoSVF(x,1). A function EnSVF(x,1), then, is defined for time horizon tH1=2 hours for (domain, range)=(degrade, [50,100]miles), and another function EnSVF(x,2) is defined for time horizon tH2=5 hours for (domain, range)=(destroy, [0,5] miles). The target EnSVF(x,1) functions are then further reviewed manually in 206 to identify i indirect effects in the shared marketplace that could influence the range values for EnSVF(x) functions. For this example, two effects have now been identified EnSVF(x,1): degrade by Cyber effect and EnSVF(x,2): destroy by kinetic effect. For each of the i effects identified in 206, effects are specified using effect taxonomy, fidelity, and end use aspects 207. Effect taxonomy includes effect classification such as kinetic effects or non-kinetic effects. Kinetic effects include direct fires or indirect damage by kinetic performer systems. Non-kinetic effects include effects resulting from a cyberattack or electronic attack or disruption in accessibility and communication without any kinetic means such as psychological operations. Effect fidelity can include aspects such as the set of EnSV(x_j) range values, purpose, spatial location time horizon, granularity, duration, geographical size, and number of other EnSVs affected by the effect. For the current example, EnSVFx1(degrade) results in [range=(0, 50), purpose=“disruption”, spatial location=(lat, long), time horizon=2 hr, granularity=“campaign”, duration=1 hr, geographical size=radius of 50 miles, number of other EnSVs affected=0). Effect purpose attribute can include semantic association of effect for a given purpose such as disruption, deactivation, destroying of a competitor resource, suppression, delay, illumination, and enhancement of an existing effect.
Data characterizing the n effects can be provided (from module 206) to the effect instrumentation API 106. The effect instrumentation API 106 sends a list characterizing such effects to module 208 which can identify m dilemmas in a dilemma space that can influence a subset of EnSVs. The dilemma space can include m dilemmas which can be formed from various types of dilemmas types (collection of various subsets of EnSVs) specified according to competitor B response indicators as one or more of intended dilemmas 210, imposed dilemmas 211, and experienced dilemmas 212. These three type of dilemmas 210-212 can be respectively specified using dilemma fidelity, taxonomy and end use (i.e., a set of rules specifying conditions triggering imposition of dilemmas, etc.). Intended (Perceived) dilemma 210, in this context, can be the owner's desired outcome of a dilemma (prior to its imposition by the owner entity relative to a second entity competitor B). Imposed dilemma 211 can characterize the actual imposed dilemma by the Owner entity relative to a second entity, which the owner entity can validate. The experienced dilemma 212 can characterize the second entity's actions in response to the imposed dilemma (which, in turn, can quantify the degree to which the imposition of the dilemma is successful, etc.).
The specification of all the n effects and m dilemmas is then used to calculate an uncertainty risk score in 213, which can be value derived from an aggregate of which is a function of the dilemma space, time horizon, and the EnSV space. An uncertainty score quantifies the risk associated with achieving an effect, and associated dilemmas within a given time horizon. In one variation, the uncertainty score is the ratio of identified domain range in EnSVFx with the operational maximum value of the CoSVx normalized over the time horizon. For example, as the objective of DUPCS is to degrade competitor's ability to gauge its own performance, the uncertainty score for EnSVFx1(degrade) will be higher than the EnSVFx2(destroy). Consequently, the selection of “degrade” over “destroy” effect will be preferred within the effect-web plan. The uncertainty risk score for the overall effect-web is aggregated from the constituent uncertainty scores of each of the effects and can be further normalized by the number of effects and aims to rank-prioritize and minimize the needed effects. This uncertainty score for each effect and its constituent fidelity attributes are then used in 214 to arrive at various actions (through manual input) that could be performed to produce the effects in a simulated or real environment. For example, for EnSVFx1(degrade), the action selected is cyber-attack, and the EnSVFx2(destroy), the action selected is kinetic strike. The identified set of actions are validated through game theory strategy API 102 that evaluates actions sequences per game theory, such as Tit for Tat, Tit for 2 Tat, etc. In some variations, operation 214 can specify strategies to incorporate from operation 213 and related executive control aspects. Executive control strategies can include parameters resulting from the evaluation of strategies, team-system formation recommendations and prior experience of executive task planners. For example, to achieve a degrade effect, a cyber-attack, if possible, provides a clean execution with minimum casualties. Alternatively, a destroy effect must be a kinetic activity to rule out any reboot or restart of the radar target asset. This process allows quality assurance processes to be performed (e.g., a sanity check by experienced personnel). Rankings of the strategies and/or implementation schedules (single event, recurring every day, recurring every week, etc.) can be specified.
A simulated environment can be implemented through a computer-based simulation tool that executes the effect-web plan either using discrete event simulation (e.g., Discrete Event Systems [DEVS] formalism, agent-based modeling (NetLogo, Repast, etc.), or tools like Arena, Simio, and Anylogic), continuous system simulation (e.g., mathematical differential equations) or hybrid simulation approaches (that include both discrete and continuous approaches simultaneously). A real environment that may execute an effect-web plan can comprise human-machine system-teams that refine various aspects of effect-web plan before tasks are executed by human-machine teams.
The effect-web plan in 312 is of the structure/schema, constituting, for example, a matrix with columns: time horizon, echelon level specifying authority over resources in a team-system configuration, resource availability, EnSVF(x,j) domain values, effect specifications, EnSVF(x,j) range values, team configuration and/or task orders. Other elements can be utilized depending on the desired configuration fidelity. For an executable effect-web plan, the matrix will have at least two rows indicating at least two effects within an effect-web, where each row is an instantiation of a team-system configuration and related information in other columns tasked to deliver the effect. As noted above, an effect-web can be defined as a confluence of multiple effects to influence CoSV in more than one way.
Further, feasible actions can be defined in 214 and associated tasks in 310 (i.e., actions and tasks that can be taken by a performer to produce an effect and/or dilemma). For example, these actions can include operations such as launching a drone sensor swarm for gathering new data feeds, launching a cyberattack, launching physical strikes, etc. The effect(s) and/or dilemmas can, in some variations, be implemented by a team-system (i.e., a combination of coordinated computing systems and/or computer-implemented processes) comprising a specified configuration of information gatherer systems, performer systems and network builder systems that collectively act to deliver an effect. The actions can be implemented via one or more tasks executed by a performer system.
As noted above, if the effect-web score is not consistent with the historical trend in 412, trajectory needs to be altered, then, at 413, attributes of the effect-web plan and/or dilemmas can be modified until the desired effect/uncertainty score is obtained. As part of creating effects, available network builder systems that are not constrained along with available performer systems that are not constrained can be identified or otherwise established. As part of the effect-web creation, a dominant game theory strategy can be identified that yields one or more effects within the effect-web. Based on the identified dominant strategy, a new target range for the competitor state variables can be established (e.g., by way of Game Theory Strategy API 102) over different time periods/on different time-bases.
In one example, a disk controller 748 can interface with one or more optional disk drives to the system bus 704. These disk drives can be external or internal floppy disk drives such as 760, external or internal CD-ROM, CD-R, CD-RW or DVD, or solid state drives such as 752, or external or internal hard drives 756. As indicated previously, these various disk drives 752, 756, 760 and disk controllers are optional devices. The system bus 704 can also include at least one communication port 720 to allow for communication with external devices either physically connected to the computing system or available externally through a wired or wireless network. In some cases, the at least one communication port 720 includes or otherwise comprises a network interface.
To provide for interaction with a user, the subject matter described herein can be implemented on a computing device having a display device 740 (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information obtained from the bus 704 via a display interface 714 to the user and an input device 732 such as keyboard and/or a pointing device (e.g., a mouse or a trackball) and/or a touchscreen by which the user can provide input to the computer. Other kinds of input devices 732 can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback by way of a microphone 736, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input. The input device 732 and the microphone 736 can be coupled to and convey information via the bus 704 by way of an input device interface 728. Other computing devices, such as dedicated servers, can omit one or more of the display 740 and display interface 714, the input device 732, the microphone 736, and input device interface 728.
One or more aspects or features of the subject matter described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) computer hardware, firmware, software, and/or combinations thereof. These various aspects or features can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device. The programmable system or computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
These computer programs, which can also be referred to as programs, software, software applications, applications, components, or code, include machine instructions for a programmable processor, and can be implemented in a high-level procedural language, an object-oriented programming language, a functional programming language, a logical programming language, and/or in assembly/machine language. As used herein, the term “machine-readable medium” refers to any computer program product, apparatus and/or device, such as for example magnetic discs, optical disks, memory, and Programmable Logic Devices (PLDs), used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor. The machine-readable medium can store such machine instructions non-transitorily, such as for example as would a non-transient solid-state memory or a magnetic hard drive or any equivalent storage medium. The machine-readable medium can alternatively or additionally store such machine instructions in a transient manner, such as for example as would a processor cache or other random access memory associated with one or more physical processor cores.
In the descriptions above and in the claims, phrases such as “at least one of” or “one or more of” may occur followed by a conjunctive list of elements or features. The term “and/or” may also occur in a list of two or more elements or features. Unless otherwise implicitly or explicitly contradicted by the context in which it is used, such a phrase is intended to mean any of the listed elements or features individually or any of the recited elements or features in combination with any of the other recited elements or features. For example, the phrases “at least one of A and B;” “one or more of A and B;” and “A and/or B” are each intended to mean “A alone, B alone, or A and B together.” A similar interpretation is also intended for lists including three or more items. For example, the phrases “at least one of A, B, and C;” “one or more of A, B, and C;” and “A, B, and/or C” are each intended to mean “A alone, B alone, C alone, A and B together, A and C together, B and C together, or A and B and C together.” In addition, use of the term “based on,” above and in the claims is intended to mean, “based at least in part on,” such that an unrecited feature or element is also permissible.
The subject matter described herein can be embodied in systems, apparatus, methods, and/or articles depending on the desired configuration. The implementations set forth in the foregoing description do not represent all implementations consistent with the subject matter described herein. Instead, they are merely some examples consistent with aspects related to the described subject matter. Although a few variations have been described in detail above, other modifications or additions are possible. In particular, further features and/or variations can be provided in addition to those set forth herein. For example, the implementations described above can be directed to various combinations and subcombinations of the disclosed features and/or combinations and subcombinations of several further features disclosed above. In addition, the logic flows depicted in the accompanying figures and/or described herein do not necessarily require the particular order shown, or sequential order, to achieve desirable results. Other implementations may be within the scope of the following claims.
This application is a continuation-in-part of U.S. application Ser. No. 17/713,842, filed Apr. 5, 2022. The foregoing related application, in its entirety, is hereby fully incorporated by reference.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 17713842 | Apr 2022 | US |
| Child | 17966319 | US |
