OnGoal System Framework

Updated 2 September 2025

OnGoal system is a computational framework for tracking, assessing, and maximizing goal achievement by integrating domain-specific metrics with decision-theoretic models.
It employs advanced methodologies such as data mining, fuzzy expert systems, and reinforcement learning to optimize actions and enhance decision-making.
The framework leverages multiobjective optimization and knowledge-driven models to improve system performance, resource efficiency, and goal fulfillment.

The OnGoal System broadly refers to computational frameworks and methodologies for tracking, assessing, and maximizing goal achievement across diverse domains—ranging from soccer analytics and system control to multiagent communications and dialogue management—by integrating domain knowledge, data-driven modeling, and decision-theoretic optimization. In research literature, OnGoal systems are characterized by their focus on (i) extracting objective, actionable metrics for goal fulfiLLMent, (ii) optimizing agent actions to directly improve goal-related outcomes, and (iii) managing uncertainty and complexity through advanced learning or inference mechanisms.

1. Conceptual Foundation: Goal-Oriented System Design

OnGoal systems unify principles from semantic communication, machine learning, and decision science to directly target goal satisfaction under realistic constraints. The design approach prioritizes:

Definition of application-specific goals (e.g., maximizing soccer goals, minimizing control system error).
Extraction and transformation of both raw and derived features encoding domain-relevant knowledge.
Quantitative modeling of goal attainment risks and payoffs, typically via probabilistic, fuzzy, or neural architectures.
Decision support layers that select optimal actions based on robust estimation of future utility (e.g., selection of the optimal kick direction in soccer (Oliveira et al., 2013), filtering sensor updates with predicted relevance (Kutsevol et al., 24 Feb 2025)).
Continuous adaptation to dynamic environments, opposing agent strategies, or non-stationary system conditions.

This goal-oriented paradigm is distinguished from traditional utility- or information-maximizing strategies by the direct embedding of application targets into the modeling and optimization pipeline.

2. Representative Methodological Frameworks

OnGoal research adopts several canonical frameworks to achieve high-performance, goal-centric decision support:

2.1 Data Mining and Feature Engineering

In RoboCup soccer simulations, OnGoal kick selection uses the CRISP-DM methodology (business understanding, data extraction, domain-informed feature engineering, and validation). Features derived from positional and angular variables (e.g., “Angle_Ball_Goalkeeper_Destiny”) encode the semantics of goal threats (Oliveira et al., 2013).
Multilayer Perceptron (MLP) networks, with carefully selected input variables and output transformations, serve as scoring chance predictors, yielding >78% improvement in goal rates over linear discriminant analysis (LDA) baselines.

2.2 Fuzzy Expert Systems

Objective evaluation of agents (e.g., goalkeepers) is achieved by encoding qualitative characteristics (flexibility, overhead dominance, courage) into fuzzy rule bases and membership functions (Bazmara et al., 2013). Outputs are defuzzified to yield interpretable quality scores, facilitating transparent comparisons and consistent team selection.

2.3 Game-Theoretical and Machine Learning Models

Strategic multi-agent scenarios such as 1-vs-1 shot-taking in football are analyzed through models integrating theory-based block responses, ML-driven outcome estimation, and game theory. The Expected Probability of Shot On Target (xSOT) metric mediates payoff calculations by capturing counterfactual success probabilities (Yeung et al., 2023).

2.4 Goal-Oriented Communication Protocols

In networked multiagent systems, OnGoal frameworks employ metrics such as the Grade of Effectiveness (GoE) or Goal-oriented Tensor (GoT) to balance transmission cost, semantic relevance, and actuation utility. Policy optimization leverages Markov Decision Processes (MDP/POMDP), reinforcement learning (DRL), and Nash equilibrium search for sampling and transmission (Li et al., 2023, Liu et al., 18 Mar 2025, Agheli et al., 19 Jan 2024, Raghuwanshi et al., 31 May 2024, Kutsevol et al., 24 Feb 2025).

3. Optimization Algorithms and Metrics

Optimization in OnGoal systems typically targets the joint minimization of discrepancy error, resource consumption, and improvement in goal-centric utility:

Sequential Quadratic Programming (SQP) algorithms solve composite multiobjective problems both for system performance assessment and controller tuning, ensuring robustness across operating scenarios (Befekadu, 2020).
Lagrangian-based methods and Karush-Kuhn-Tucker (KKT) conditions yield optimal transmission activation probabilities and thresholding criteria for sensor reporting in IoT (Agheli et al., 19 Jan 2024, Raghuwanshi et al., 31 May 2024).
Double-Dueling Deep Q-Networks (D3QN) are leveraged for joint transmission-control policy learning in non-orthogonal multiple access (NOMA) networks, mediating the trade-off between transmission efficiency and control fidelity (Liu et al., 18 Mar 2025).
Value of Updates (VoU) methods at the transport layer, informed by belief networks and augmentation predictors (model-based or LSTM-driven), implement packet admission control based on the expected marginal reduction in estimation error versus transmission cost (Kutsevol et al., 24 Feb 2025).

4. Knowledge-Driven and Multimodal Systems

Knowledge integration is pivotal for OnGoal systems operating in complex, high-dimensional environments:

Soccer commentary generation employs knowledge-grounded video captioning models that align entity/object detection from video with domain-specific knowledge graphs, producing fine-grained, contextually rich narrative descriptions (Qi et al., 2023).
Graph Neural Network (GNN) frameworks (GoalNet), with attention and transformer-based variants, credit non-scoring but pivotal player actions by encoding spatial-temporal interaction data and centrality into learned player embeddings (Jiang et al., 12 Mar 2025).

5. Evaluation and Empirical Results

Metrics such as Kolmogorov-Smirnov separation, AUC_ROC, control LQG cost, xSOT/xG correlation, mean squared error (MSE), and user paper-derived cognitive load indices provide the empirical backbone for OnGoal system validations.
Semantics-aware transmission and self-decision schemes in networked agents attain up to 29.52% higher Grade of Effectiveness, 67.21% fewer transmissions, and at least 92% of optimal performance compared to non-adaptive or periodic policies (Agheli et al., 19 Jan 2024).
In dialogue systems, goal tracking and visualization led to reduced user effort and time, higher engagement, and improved resilience in task fulfiLLMent (Coscia et al., 28 Aug 2025).

6. Applications and Significance

OnGoal systems are deployed across varied domains:

Robotic and simulated soccer—maximizing scoring opportunities via advanced kick selection and goalkeeper evaluation.
Industrial Internet of Things and cyber-physical systems—optimizing sensor reporting, network traffic, and control stability under stringent timing and resource constraints.
Remote multiagent control and semantic communications—joint scheduling/transmission design to improve system-wide utility and decision fidelity.
Multi-turn human-LLM dialogue—real-time goal tracking, feedback, and strategic prompting via enhanced interface design.

By embedding application goals into every layer of sensing, reasoning, and communication, the OnGoal paradigm delivers both improved performance metrics and transparent, actionable insight for complex systems.

7. Future Directions and Open Challenges

The literature highlights key areas for further research in OnGoal systems:

Data-driven refinement of feature selection, membership functions, and goal weighting for fuzzy expert systems.
Integration of self-learning and adaptive mechanisms, including feedback-driven rule refinement and reinforcement-based update.
Extension of knowledge-grounded frameworks to multimodal and real-time domains, requiring robust entity-object linking and scalable inference.
Addressing evaluation dissonance, especially in subjective or creative tasks, through user feedback loops and personalized model adaptation.
Cross-domain generalization, ensuring applicability and interpretability of OnGoal methodologies in varied operational contexts (from robotics to conversational AI).

Ongoing development of OnGoal systems remains centered on the translation of complex, multidisciplinary goals into quantifiable, optimizable actions, enabling robust, goal-attentive decision support in modern intelligent applications.