Adventure in Interactive Research

• Adventure is a family of interactive systems characterized by exploration, sequential decision-making, narrative progression, and state-dependent actions.
• It is used as a technical paradigm to evaluate memory, planning, and language grounding within partially observable environments modeled as POMDPs.
• Applications span from text-adventure AI competitions and pedagogical UNIX command-line games to procedural mystery generation and co-creative tabletop experiences.

Searching arXiv for recent and canonical papers related to adventure games, interactive fiction, and adventure-style pedagogical systems. Adventure, in computational and media research, denotes a family of interactive forms organized around exploration, sequential decision-making, narrative progression, and state-dependent action. In the literature surveyed here, the term spans classic text adventures and interactive fiction, grounded dialogue worlds, GUI-based full-story game benchmarks, educational command-line systems that adopt a text-adventure structure, branching e-books, LLM-generated language-learning narratives, procedural mystery generation, and tabletop role-playing co-creativity. Across these settings, adventure is not merely a genre label. It functions as a technical paradigm for studying how agents or learners operate in environments where observations are partial, actions are consequential, clues may have delayed utility, and successful progress depends on memory, planning, interpretation, and interaction with a structured world (Atkinson et al., 2018, Ammanabrolu et al., 2018, Ahn et al., 1 Sep 2025).

1. Adventure as an Interactive Research Paradigm

Adventure games are treated in AI research as environments in which the world is mediated primarily through text or GUI observations and progression depends on inferring relevant actions rather than selecting from an explicitly operationalized move set. The Text-Based Adventure AI Competition characterizes this as a benchmark where an agent must infer what objects exist, what verbs may apply, what commands can change the world, and what information should be remembered for later use (Atkinson et al., 2018). This distinguishes adventure settings from domains in which legal actions are pre-enumerated.

In text-based formulations, the environment is frequently modeled as a partially observable decision process. "Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning" formalizes the task as a POMDP,

$\langle S, T, A, \Omega, O, R, \gamma \rangle,$

with textual observations rather than direct state access (Ammanabrolu et al., 2018). FlashAdventure similarly frames GUI-based adventure play as a POMDP,

$$\langle \mathcal{S},\mathcal{A},\mathcal{O},\Omega,\mathcal{T}, \mathcal{R}\rangle,$$

where the hidden state is rendered as RGB screenshots and the action space consists of GUI actions such as clicks, drags, typing, scrolling, and a finish action (Ahn et al., 1 Sep 2025).

This research use of adventure emphasizes several recurring computational properties. First, observability is incomplete: agents see descriptions, screenshots, or dialogue-conditioned local contexts rather than the full world state (Ammanabrolu et al., 2018, Urbanek et al., 2019, Ahn et al., 1 Sep 2025). Second, action spaces are combinatorial or weakly structured: text commands may be open-ended, while GUI actions must be grounded in visually diverse interfaces (Atkinson et al., 2018, Ahn et al., 1 Sep 2025). Third, task success is long-horizon and clue-dependent: relevant evidence may only become actionable many steps after it is observed (Ahn et al., 1 Sep 2025). This suggests that adventure environments are especially useful where the goal is to test memory, world modeling, action grounding, and sequential reasoning under delayed utility.

2. Text Adventure and Interactive Fiction in AI

The classical computational meaning of adventure remains most explicit in text-adventure and interactive-fiction research. The Text-Based Adventure AI Competition used classic parser-based games and later a set of 20 diverse text adventures to evaluate agents on average percentage of points achieved and percentage of games with non-zero score (Atkinson et al., 2018). In that reassessment, NAIL obtained 2.56 average percentage completion and 45.5 percent non-zero-score rate, outperforming BYU-Agent 2016, Golovin, and CARL, while RandomAgent achieved 1.66 completion and 34 percent non-zero score (Atkinson et al., 2018). The results established progress across 2016 to 2018 while also showing that robust general competence remained far from solved.

The main technical obstacles are consistent across papers. They include natural language understanding, action generation, exploration, memory, and planning (Atkinson et al., 2018). "Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning" addresses these with a knowledge graph represented as RDF triples

$\langle subject, relation, object \rangle,$

used both as persistent memory and as a mechanism for pruning the action space (Ammanabrolu et al., 2018). The action space is characterized as

$A = \mathcal{O}(|V| \times |O|^2),$

reflecting the combinatorics of verbs and one- or two-object commands (Ammanabrolu et al., 2018). KG-DQN combines graph embedding, observation encoding, and action encoding, and uses a Q-value defined as a dot product between state and action embeddings:

$$Q(\mathbf{s_t}, \mathbf{a_t}) = \mathbf{s_t} \cdot \mathbf{a_t}.$$

On TextWorld "home" games, the paper reports that all KG-DQN variants converge faster than baselines and that KG-DQN is about 40% faster than the best baseline on the small game (Ammanabrolu et al., 2018).

A complementary line of work treats the central difficulty not as action-space size alone but as semantic instability in language-mediated state descriptions. "Zero-Shot Learning of Text Adventure Games with Sentence-Level Semantics" introduces DSQN, a Deep Siamese Q-Network that learns when different trajectories correspond to the same underlying game state (Yin et al., 2020). Its Siamese module is defined as

$$f_{snn}(t_i, t_j) = \sigma \left(W_{snn} \left|f_e(t_i) - f_e(t_j)\right| + b\right),$$

and the factorized Q-value is

$Q(t,a) = Q_{snn}(t,a) + Q_{var}(t,a).$

The reported result is that DSQN with factorization reaches the baseline convergence point using only 15% of its iterations, reaches a convergence point 15% higher than the baseline's, and attains 42% on unrelated treasure-hunting games versus 24% for DQN and 21% for DSQN without factorization (Yin et al., 2020). The same paper reports SNN accuracies of 99.4 on two in-genre tests and 85.0 on the unrelated test for the factorized model (Yin et al., 2020).

Another strand treats adventure play across families of related games rather than single instances. "Learn How to Cook a New Recipe in a New House" states that deep Q-learning can be applied to families of text video games by combining curriculum learning, navigation-based map familiarization, and contextual multi-armed bandit decision policies, with improved task completion rates over reasonable baselines on never-before-seen games of that theme (Yin et al., 2019). Because only the abstract-level description is available in the supplied material, more specific methodological or quantitative claims cannot be extracted.

3. Grounded Worlds, Dialogue, and Full Story Arcs

Adventure is also used as a setting for grounded language learning and embodied interaction. "Learning to Speak and Act in a Fantasy Text Adventure Game" introduces LIGHT, a crowdsourced fantasy world with 663 locations, 3462 objects, 1755 characters, and 11k human-human episodes/dialogues (Urbanek et al., 2019). In each episode, two characters interact in a room, with access to persona, location description, present objects, and the other character's current state or history; on each turn, each agent can perform one action or emote and produce one utterance (Urbanek et al., 2019). The task is not limited to dialogue generation: it includes dialogue response prediction, action prediction, and emote prediction grounded in local world state.

The paper evaluates retrieval and generative models, including Starspace, Transformer Memory Network, BERT Bi-Ranker, BERT Cross-Ranker, and a Generative Transformer (Urbanek et al., 2019). Retrieval models score context-candidate pairs via a learned similarity, described as a dot product,

$s(c, r) = \mathbf{h}_c^\top \mathbf{h}_r,$

while generative models maximize token-level likelihood

$p(y \mid x) = \prod_t p(y_t \mid y_{<t}, x).$

The core empirical claim is that full grounding in persona, setting, objects, characters, and prior actions improves prediction over dialogue-only conditioning, while humans still outperform models, especially on unseen settings (Urbanek et al., 2019). This demonstrates a specifically adventure-like form of grounding: language is useful insofar as it is language about a world.

GUI-based benchmarks generalize this insight from textual worlds to visual interfaces. FlashAdventure introduces a benchmark of 34 free-to-play Flash adventure games covering point-and-click mystery/detective, hidden object, room escape, visual novel / dating sim, and simulation subgenres (Ahn et al., 1 Sep 2025). Unlike many prior benchmarks, it evaluates full story arc completion rather than short local objectives. The paper defines Success Rate from final reward,

$\mathcal{R}(s_T,a_T)\in\{0,1\},$

Milestone Completion Rate via manually curated intermediate milestones, and Steps with a cap of 1,000 steps (Ahn et al., 1 Sep 2025).

A central concept in FlashAdventure is the observation-behavior gap, the delay between when a clue is observed and when it can be meaningfully used (Ahn et al., 1 Sep 2025). To address this, the paper proposes COAST, the Clue-Oriented Agent for Sequential Tasks, organized around a long-term clue memory $$\langle \mathcal{S},\mathcal{A},\mathcal{O},\Omega,\mathcal{T}, \mathcal{R}\rangle,$$0 and a Seek-Map-Solve cycle (Ahn et al., 1 Sep 2025). In the benchmark’s headline results, Claude-3.7-Sonnet Computer-Use achieved 0.0% success and 17.11% MCR, OpenAI CUA 5.88% success and 15.39% MCR, Claude-3.7-Sonnet Computer-Use + COAST 5.88% success and 19.89% MCR, human performance under the same 1,000-step limit 50.98% success and 78.98% MCR, and unlimited-step human performance 97.06% success and 100% MCR (Ahn et al., 1 Sep 2025). The benchmark therefore positions full-story adventure play as a stress test for long-horizon reasoning, memory, and action grounding.

4. Adventure as a Pedagogical Form

Adventure is not restricted to entertainment or agent evaluation. It also appears as an instructional design pattern in systems that turn learning tasks into sequential interactive worlds. "TermAdventure: Interactively Teaching UNIX Command Line, Text Adventure Style" treats the shell itself as the game world: students read a short story-like task description, act by typing ordinary UNIX commands, and advance through levels only when a predefined test succeeds (Šuppa et al., 2021). The runtime is a single Go binary injected through Bash’s PROMPT_COMMAND, with prompt state displayed via PS1 (Šuppa et al., 2021). Levels are defined in YAML and form a directed acyclic graph, allowing branching and randomized paths (Šuppa et al., 2021).

The design goal is to preserve the authentic command-line environment rather than separate practice from submission. Immediate feedback is central: each command is checked by a level-specific Bash test whose exit status determines whether the level is passed (Šuppa et al., 2021). Auxiliary tools include ta_print_again for reprinting the current task and ta_help for signaling that help is needed, while the current level text is saved in `$$\langle \mathcal{S},\mathcal{A},\mathcal{O},\Omega,\mathcal{T}, \mathcal{R}\rangle,$$1$$\langle \mathcal{S},\mathcal{A},\mathcal{O},\Omega,\mathcal{T}, \mathcal{R}\rangle,$$2$ The paper reports deployment in a course titled <strong>“UNIX for Data Scientists”</strong> with <strong>12 lectures</strong>, where student feedback had mean rating <strong>8.4 / 10</strong> and standard deviation <strong>1.3</strong> (<a href="/papers/2104.05456" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">Šuppa et al., 2021</a>).</p> <p>A related educational use of adventure appears in &quot;Choose Your Own Adventure: Interactive E-Books to Improve Word Knowledge and Comprehension Skills&quot; (<a href="/papers/2403.02496" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">Day et al., 2024</a>). This feasibility study involved <strong>N = 421 students</strong> in grades 3–5 and <strong>16 teachers</strong> across Southern California and Central Arizona (<a href="/papers/2403.02496" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">Day et al., 2024</a>). The two Word Knowledge e-Books used branching story paths and embedded multiple-choice comprehension questions with immediate feedback; progress halted until a correct response was given (<a href="/papers/2403.02496" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">Day et al., 2024</a>). Students read &quot;Dragon’s Lair&quot; and, in California, also &quot;Hurricane!&quot;, with three strategy versions per book: word learning or general comprehension, summarization, and question generation (<a href="/papers/2403.02496" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">Day et al., 2024</a>).</p> <p>The reported findings were significant gains in both targeted word knowledge and hurricane concept knowledge. For &quot;Dragon’s Lair&quot;, the paired-samples test was</p> <p>$\langle \mathcal{S},\mathcal{A},\mathcal{O},\Omega,\mathcal{T}, \mathcal{R}\rangle,$3

and for "Hurricane!",

$\langle \mathcal{S},\mathcal{A},\mathcal{O},\Omega,\mathcal{T}, \mathcal{R}\rangle,$4

(Day et al., 2024). Hierarchical Linear Modeling found no significant differences by strategy version in either book, while embedded question performance significantly predicted posttest in "Hurricane!" with coefficient $\langle \mathcal{S},\mathcal{A},\mathcal{O},\Omega,\mathcal{T}, \mathcal{R}\rangle,$5 (Day et al., 2024). Here adventure serves as a pedagogical vehicle for agency, rereading, and immediate corrective feedback.

"GenQuest: An LLM-based Text Adventure Game for Language Learners" extends this logic to generative interactive fiction for EFL learning (Wang et al., 6 Oct 2025). The system uses milestones, decision points, and endings to structure a choose-your-own-adventure narrative while avoiding unconstrained branching (Wang et al., 6 Oct 2025). It incorporates CEFR levels from A1 to C2 by generating six semantically similar narrative samples and having the learner choose the most appropriate one (Wang et al., 6 Oct 2025). A GPT-4o-based language assistant can explain highlighted words, phrases, or sentences in context and store them in a personal review list (Wang et al., 6 Oct 2025). In a pilot with 9 participants, vocabulary test scores ranged from 6 to 18.5 out of 20, with mean 13.44 and SD 4.62; TAM survey means were 5.33 for perceived usefulness and 5.85 for perceived ease of use (Wang et al., 6 Oct 2025). This suggests that adventure structures are increasingly used as adaptive pedagogical scaffolds rather than fixed authored curricula.

5. Procedural and LLM-Generated Adventure

Adventure also functions as a target for procedural generation and narrative synthesis. "DATA Agent" generates murder mystery adventure games from linked open data, especially DBpedia, OpenStreetMap, and Wikimedia Commons (Green et al., 2018). A seed person becomes the victim, suspects are selected via an evolutionary strategy, and semantic paths connecting victim and suspects are transformed into cities, buildings, NPCs, and items (Green et al., 2018). The suspect-selection procedure uses a $$\langle \mathcal{S},\mathcal{A},\mathcal{O},\Omega,\mathcal{T}, \mathcal{R}\rangle,$$6 evolution strategy with candidate genomes of $$\langle \mathcal{S},\mathcal{A},\mathcal{O},\Omega,\mathcal{T}, \mathcal{R}\rangle,$$7 suspects, populations of 50, and 500 generations (Green et al., 2018). Paths have maximum length 4 edges, yielding compact games with about 15 objects, contrasted with about 60 objects in the earlier WikiMystery system (Green et al., 2018).

The generated game loop is investigative: receive a case, explore cities and buildings, talk to NPCs and inspect items, unlock new areas, and finally interrogate suspects by comparing their claims with collected evidence (Green et al., 2018). In a user study with 30 participants, 23 solved both games and 28 solved at least one (Green et al., 2018). The authors conclude that the mysteries were straightforward to solve, indicating a tradeoff between playability and challenge (Green et al., 2018).

"Ambient Adventures: Teaching ChatGPT on Developing Complex Stories" treats adventure in a different procedural sense: a household becomes a quest environment for imaginary play (Chen et al., 2023). The pipeline maps real objects to imaginary objects, generates a short narrative with a topic such as saving a princess or escaping a horror setting, simplifies each sentence to an action-object phrase, then maps those phrases back to admissible real-world actions to be executed in a TextWorld-based house simulation (Chen et al., 2023). The paper formalizes the object and action sets as $$\langle \mathcal{S},\mathcal{A},\mathcal{O},\Omega,\mathcal{T}, \mathcal{R}\rangle,$$8, $$\langle \mathcal{S},\mathcal{A},\mathcal{O},\Omega,\mathcal{T}, \mathcal{R}\rangle,$$9, and $\langle subject, relation, object \rangle,$0, and defines success by completing all input actions sequentially, with reward values

$\langle subject, relation, object \rangle,$1

(Chen et al., 2023). The last action in the sequence is set as the win state (Chen et al., 2023).

The examples show a full transformation from fantasy narrative to executable household action sequence. The paper also notes that ChatGPT often requires multiple iterations of prompting and more directional detail, particularly when interactive object relations are complex (Chen et al., 2023). This indicates that automatically authored adventure quests remain sensitive to environmental grounding and prompt control.

6. Co-Creation, Tabletop Play, and Broader Meanings of Adventure

Research on adventure now extends beyond digital games into tabletop co-creativity. "Co-Creativity at the Table: A Qualitative Analysis of Creative Interactions in the Podcast 'Adventure AI'" studies three seasons of a Dungeons & Dragons podcast in which ChatGPT, personified as "Alex the Language Lord", is used as a co-creative partner rather than a full Dungeon Master (Dodd et al., 16 Jun 2026). The analysis covers Season 1 (2023), Season 5 (2024), and Season 12 (2025), using template analysis and reflexive thematic analysis over Apple Podcasts transcripts (Dodd et al., 16 Jun 2026).

The paper identifies role structures for humans and AI. The human DM occupies Author, Editor, Curator, Game Designer, Actor, Referee, and Storyteller roles, while the AI occupies Author, Editor, Director, Actor, and Storyteller roles (Dodd et al., 16 Jun 2026). The added notion of Curator denotes choosing AI-generated material without remixing it (Dodd et al., 16 Jun 2026). Across seasons, the group becomes more skilled at steering and revising outputs, while the AI remains strongest at idea generation, naming, riddles, descriptive writing, and optional material (Dodd et al., 16 Jun 2026).

The same study also documents persistent failure modes: weak continuity, inconsistent details, broken or overpowered mechanics, and poor handling of player agency in climactic scenes (Dodd et al., 16 Jun 2026). The paper therefore argues that AI is useful in tabletop adventure play when it supports brainstorming and narration, but is less appropriate for fairness, rules adjudication, interpersonal management, and tightly coherent long-form control (Dodd et al., 16 Jun 2026). A plausible implication is that the “adventure” frame here serves as a testbed for division of labor between human creative judgment and machine generativity.

The term adventure also appears in a non-gaming mathematical context in "A sequel to the adventure of RGB-tilings to explore the Four Color Theorem" (Liu, 2024). There, “adventure” is a metaphor for an ongoing program of combinatorial exploration using RGB-tilings, eRGB-tilings, synonym/equivalence/congruence relations, and $\langle subject, relation, object \rangle,$2-adjustment to study local configurations in minimal non-4-colorable maximal planar graphs (Liu, 2024). Because this usage is metaphorical rather than interactive, it broadens the term’s scholarly range but does not alter its dominant meaning in computational media research.

7. Conceptual Unities and Open Problems

Despite their heterogeneity, these works converge on several structural features of adventure. One is world-dependence: the right utterance, command, GUI action, or pedagogical response is conditioned on an explicit environment with objects, affordances, roles, or clues (Urbanek et al., 2019, Ammanabrolu et al., 2018, Ahn et al., 1 Sep 2025). A second is delayed relevance: information gathered early may become useful only much later, creating the need for persistent memory, clue mapping, journal systems, or semantic state abstraction (Ammanabrolu et al., 2018, Yin et al., 2020, Green et al., 2018, Ahn et al., 1 Sep 2025). A third is branching under constraint: although adventure often implies open choice, many successful systems stabilize branching with milestones, DAGs, or curated story structure rather than unconstrained generative divergence (Šuppa et al., 2021, Day et al., 2024, Wang et al., 6 Oct 2025).

The literature also clarifies what current agents still do poorly. In text games, average completion rates remain extremely low and even strong systems often rely on hand-engineered priors, memory structures, or action pruning (Atkinson et al., 2018, Ammanabrolu et al., 2018). In GUI-based full-story settings, even the best reported agents remain far behind humans on success and milestone completion (Ahn et al., 1 Sep 2025). In tabletop settings, AI generation supports ideation but not the deeper social, dramaturgical, and balancing functions of human facilitation (Dodd et al., 16 Jun 2026). In pedagogical applications, branching and immediate feedback appear effective, but no single comprehension strategy clearly dominates in the e-book setting, and LLM-based narrative systems still face coherence and multimodal-design issues (Day et al., 2024, Wang et al., 6 Oct 2025).

Taken together, these findings support a precise interpretation of adventure as a research object. It is a family of interactive systems in which meaning emerges from traversal through a world, not from isolated action prediction alone. Whether the medium is parser text, screenshots, a UNIX shell, an e-book, a generated mystery, a household simulation, or a tabletop session, adventure organizes interaction around exploration, clue accumulation, branching progression, and eventual resolution. This suggests that adventure will remain a useful locus for work on grounded language, sequential decision-making, human-AI co-creativity, and educational interaction design (Atkinson et al., 2018, Urbanek et al., 2019, Ahn et al., 1 Sep 2025).