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The agent maintains probabilistic models of opponent states and hidden information, updating beliefs through Bayesian inference as new observations arrive, allowing strategic decision-making under information asymmetry typical in poker, diplomacy, and deception games.","intents":["Build an agent that plays poker or other imperfect information games competitively","Model opponent beliefs and hidden card distributions in card games","Reason about strategic deception and information hiding in multi-agent scenarios","Train agents to make optimal decisions when game state is partially observable"],"best_for":["Game AI researchers studying imperfect information games","Multi-agent RL practitioners building competitive game agents","Teams developing poker engines or hidden-information game solvers"],"limitations":["Computational complexity scales exponentially with game tree size and number of hidden variables","Belief tracking requires explicit state space enumeration or approximation, limiting scalability to very large games","No built-in support for continuous action spaces or real-time decision-making under strict latency constraints"],"requires":["Python 3.8+","Game environment with well-defined state/action/observation spaces","Computational resources for tree search or neural network inference"],"input_types":["game state representation (structured data)","observation history (sequence of game events)","action space definition (discrete or enumerated)"],"output_types":["action selection (discrete choice)","belief distribution over hidden states (probability distribution)","value estimates for game positions (numeric)"],"categories":["planning-reasoning","game-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-suspicion-agent__cap_1","uri":"capability://planning.reasoning.opponent.modeling.and.belief.inference","name":"opponent modeling and belief inference","description":"Constructs and maintains dynamic models of opponent behavior and likely hidden states through Bayesian belief updating and historical action analysis. The system tracks opponent action patterns, infers probability distributions over their possible hands/strategies, and updates these beliefs incrementally as new game information becomes available, enabling adaptive strategy selection based on opponent model predictions.","intents":["Infer what cards an opponent likely holds based on their betting patterns","Adapt agent strategy based on learned opponent tendencies and behavioral models","Estimate opponent's belief state about the agent's own hidden information","Predict opponent actions probabilistically to improve decision quality"],"best_for":["Poker AI and card game engine developers","Multi-agent learning researchers studying opponent adaptation","Game theory practitioners building strategic agents"],"limitations":["Opponent modeling accuracy depends on sufficient historical action data; sparse games require many iterations to converge","Assumes rational or learnable opponent behavior; exploitative strategies against irrational players may be suboptimal","No explicit handling of deceptive play or intentional strategy shifts by opponents"],"requires":["Python 3.8+","Game history/replay data or online game environment","Probabilistic inference library (e.g., numpy, scipy)"],"input_types":["opponent action history (sequence of moves)","game outcomes (win/loss/pot distribution)","hand strength indicators (if available)"],"output_types":["belief distribution over opponent hands (probability matrix)","opponent strategy model (action probabilities per state)","confidence scores for predictions (numeric)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-suspicion-agent__cap_2","uri":"capability://planning.reasoning.multi.agent.strategic.planning.with.deception","name":"multi-agent strategic planning with deception","description":"Enables agents to plan multi-step strategies that account for deception, bluffing, and information manipulation in competitive multi-agent settings. The planner constructs game trees that model not just opponent actions but opponent beliefs about the agent's state, allowing strategies that exploit information asymmetry through strategic information revelation or concealment. Uses recursive belief modeling to reason about nested levels of strategic thinking.","intents":["Plan a bluffing strategy in poker that accounts for opponent belief states","Design multi-turn deception tactics in diplomacy or negotiation games","Reason about when to reveal or conceal information to maximize strategic advantage","Construct strategies that exploit opponent misconceptions about the agent's hand/state"],"best_for":["Game AI researchers studying strategic deception","Multi-agent RL teams building agents for diplomacy or negotiation","Competitive game engine developers"],"limitations":["Computational cost grows exponentially with planning depth and number of agents; practical depth limited to 3-5 moves ahead","Assumes opponents follow rational decision-making; strategies may fail against unpredictable or non-strategic players","Requires explicit game model; cannot adapt to unknown game rules or hidden mechanics"],"requires":["Python 3.8+","Game environment with explicit state/action/observation model","Sufficient computational resources for tree search (GPU optional but recommended)"],"input_types":["current game state (structured representation)","opponent belief models (probability distributions)","action space (enumerated or parameterized)"],"output_types":["action sequence (plan)","value estimate for plan (numeric)","belief trajectory (how opponent beliefs evolve under plan)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-suspicion-agent__cap_3","uri":"capability://planning.reasoning.game.theoretic.solution.computation","name":"game-theoretic solution computation","description":"Computes game-theoretic solutions (Nash equilibria, exploitability metrics, best responses) for imperfect information games using algorithms like counterfactual regret minimization (CFR) or similar iterative solution methods. Produces strategy profiles that are provably optimal or near-optimal under game-theoretic assumptions, enabling agents to play unexploitable strategies or measure how exploitable current strategies are.","intents":["Compute Nash equilibrium strategy for a poker variant or imperfect information game","Measure how exploitable an agent's current strategy is against optimal play","Generate best-response strategies against known opponent strategies","Train agents toward game-theoretically sound play through iterative refinement"],"best_for":["Game theory researchers and academics","Poker engine developers seeking theoretically sound baselines","Teams building agents that must be unexploitable or near-optimal"],"limitations":["Computational cost is prohibitive for large game trees; practical only for simplified game variants or abstracted state spaces","Convergence to equilibrium can require millions of iterations; wall-clock time may be hours or days for non-trivial games","Assumes fixed game rules and rational opponents; solutions are brittle to rule changes or opponent deviations"],"requires":["Python 3.8+","Game definition with complete state/action/payoff specification","High computational resources (multi-core CPU or GPU for large games)"],"input_types":["game specification (rules, payoff matrix, information structure)","abstraction parameters (if using game abstraction)"],"output_types":["strategy profile (action probabilities per information set)","exploitability metric (numeric, e.g., expected loss against best response)","convergence history (iteration-by-iteration improvement)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-suspicion-agent__cap_4","uri":"capability://data.processing.analysis.information.set.abstraction.and.state.compression","name":"information set abstraction and state compression","description":"Reduces the computational complexity of imperfect information games by grouping similar game states into information sets and applying state abstraction techniques. Compresses the game tree by merging states that are strategically equivalent from the agent's perspective, enabling solution computation and planning in games too large for exact analysis. Uses techniques like card clustering, action abstraction, and betting round abstraction.","intents":["Reduce a large poker variant to a computationally tractable abstracted game","Group similar hands into equivalence classes for faster computation","Abstract betting actions to reduce branching factor in game tree search","Scale game-theoretic solution methods to realistic game sizes"],"best_for":["Poker AI researchers and engine developers","Game theory practitioners working with large imperfect information games","Teams needing to balance solution quality with computational tractability"],"limitations":["Abstraction introduces approximation error; solution quality degrades with coarser abstractions","Designing effective abstractions requires domain knowledge and manual tuning; no fully automated abstraction discovery","Abstracted solutions may not transfer well to the full game or to opponent strategies outside the abstraction"],"requires":["Python 3.8+","Game specification and domain knowledge for designing abstractions","Clustering or grouping algorithms (e.g., k-means, hand strength clustering)"],"input_types":["full game specification","abstraction parameters (cluster count, action grouping rules)","hand strength or similarity metrics"],"output_types":["abstracted game representation (reduced state space)","mapping between full and abstracted states (bijection)","abstracted strategy (applicable to full game via mapping)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-suspicion-agent__cap_5","uri":"capability://planning.reasoning.sequential.decision.making.under.uncertainty","name":"sequential decision-making under uncertainty","description":"Enables agents to make optimal or near-optimal decisions in sequential games where outcomes depend on hidden information and future opponent actions. Integrates belief tracking, value estimation, and action selection to handle the full pipeline of decision-making under uncertainty. Uses techniques like expectimax search, value iteration, or policy gradient methods adapted for imperfect information settings.","intents":["Decide whether to fold, call, or raise in poker given uncertain hand strength","Choose actions that maximize expected value when future states are partially observable","Balance exploration of uncertain outcomes against exploitation of known good actions","Make decisions that account for both immediate payoffs and long-term strategic position"],"best_for":["Game AI developers building decision-making agents","RL practitioners working on imperfect information environments","Teams building agents for real-time games with hidden information"],"limitations":["Value estimation accuracy depends on quality of belief models; poor beliefs lead to poor decisions","Computational cost grows with planning horizon; real-time constraints may limit lookahead depth","No explicit handling of risk preferences; agents optimize expected value, not robust or conservative strategies"],"requires":["Python 3.8+","Belief model or belief inference capability","Value function approximator (neural network, lookup table, or analytical formula)"],"input_types":["current observation (partial game state)","belief state (probability distribution over hidden states)","action space (enumerated)"],"output_types":["action selection (discrete choice)","value estimate (numeric, expected payoff)","confidence or uncertainty estimate (optional)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-suspicion-agent__cap_6","uri":"capability://planning.reasoning.multi.agent.learning.and.strategy.adaptation","name":"multi-agent learning and strategy adaptation","description":"Enables agents to learn and adapt strategies through self-play, population-based training, or interaction with other agents in imperfect information games. Implements learning algorithms (e.g., policy gradient, Q-learning variants, or game-theoretic learning) that converge toward improved strategies while handling the non-stationarity of multi-agent learning environments. Tracks learning progress and strategy evolution across training episodes.","intents":["Train an agent to play poker competitively through self-play against copies of itself","Adapt agent strategy in response to opponent strategy changes during ongoing play","Discover novel strategies through population-based training or evolutionary methods","Measure agent improvement over training time and identify convergence plateaus"],"best_for":["RL researchers studying multi-agent learning in games","Game AI teams training agents through self-play","Competitive game developers building adaptive opponents"],"limitations":["Multi-agent learning is non-stationary and unstable; convergence is not guaranteed and may require careful hyperparameter tuning","Training time can be prohibitive for complex games; may require days or weeks of computation","Agents may converge to exploitable local optima or rock-paper-scissors cycles rather than equilibrium strategies"],"requires":["Python 3.8+","Game environment with step/reset interface","RL library (e.g., PyTorch, TensorFlow) or custom learning algorithm implementation"],"input_types":["game observations (partial state)","rewards (numeric feedback)","opponent actions (for learning opponent models)"],"output_types":["learned policy (action probabilities or deterministic mapping)","value function (state value estimates)","training metrics (win rate, exploitability, learning curves)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":19,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","Game environment with well-defined state/action/observation spaces","Computational resources for tree search or neural network inference","Game history/replay data or online game environment","Probabilistic inference library (e.g., numpy, scipy)","Game environment with explicit state/action/observation model","Sufficient computational resources for tree search (GPU optional but recommended)","Game definition with complete state/action/payoff specification","High computational resources (multi-core CPU or GPU for large games)","Game specification and domain knowledge for designing abstractions"],"failure_modes":["Computational complexity scales exponentially with game tree size and number of hidden variables","Belief tracking requires explicit state space enumeration or approximation, limiting scalability to very large games","No built-in support for continuous action spaces or real-time decision-making under strict latency constraints","Opponent modeling accuracy depends on sufficient historical action data; sparse games require many iterations to converge","Assumes rational or learnable opponent behavior; exploitative strategies against irrational players may be suboptimal","No explicit handling of deceptive play or intentional strategy shifts by opponents","Computational cost grows exponentially with planning depth and number of agents; practical depth limited to 3-5 moves ahead","Assumes opponents follow rational decision-making; strategies may fail against unpredictable or non-strategic players","Requires explicit game model; cannot adapt to unknown game rules or hidden mechanics","Computational cost is prohibitive for large game trees; practical only for simplified game variants or abstracted state spaces","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.14,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.27,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-06-17T09:51:04.049Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=suspicion-agent","compare_url":"https://unfragile.ai/compare?artifact=suspicion-agent"}},"signature":"oLr4Cr59Rsc9D+Hyk6moloSrGhtbbDUTqTqHsLDzIJJflB3t46ebtQryZZOsSvphjDGBP23InxrZ+TJhzLWFAw==","signedAt":"2026-06-20T12:13:09.205Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/suspicion-agent","artifact":"https://unfragile.ai/suspicion-agent","verify":"https://unfragile.ai/api/v1/verify?slug=suspicion-agent","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}