未來研究主要根據本研究與其設計實驗的發現而提出，包括解決設計問題程序的 自動化、相互作用網路拓樸關係、代理人的學習機制與想法地圖區域控制與視覺 化。另外，有關角色代理人在內部記憶組織的案例知識調整，也是未來研究的重 點。
1. 解決設計問題程序的自動化：在 DIM 中，解決設計問題程序是固定且預先設 定的，然而根據第六章節中設計實驗的發現，此解決問題的程序主要根據具 有相鄰連結原則之想法元件的連結數量而決定。因此，如何自動化的產生議 題屬性值，並進行動態性的調整解決設計問題的程序，在 DIM 的架構下，未 來必須加以考慮。
架構在角色扮演的理論，此未來研究可以經由了解 StA 的推理能力（包括計 算與判斷外部 IE 地圖中，不同想法元件之連結種類與數量），與此 StA 與不 同 ScA 之間在外部相互作用中之語言行動等代理人溝通語言機制的研究而達 成。
2. 相互作用網路拓樸關係：根據本研究之設計實驗的發現，在分散式想法連結 的過程中，不同的網路拓樸關係（例如：環狀、星狀、點對點與簇群狀，見 圖 70）建立不同知識實體之間的連結關係[120]，進而影響一個分散式想法連 結的設計結果。
因此，DIM 提供不同相互作用之網路拓樸關係，提供設計者在內部與外部相 互作用中動態的選擇不同的網路拓樸關係，除了增加可以增加產生想法地圖 的多樣性外，同時可以進行研究不同網路拓樸關係、連結過程與設計結果的 關係。這些網路拓樸關係可以結合不同類型的語言行動與代理人溝通語言機 制，在不同的 JADE 代理人平台之間，經由組織區劃這些平台所包含的不同 容器，並建立這些容器與 DIM 系統代理人之間的連結而達成。
圖 70 . 相互作用之網路拓樸關係：環狀、星狀、點對點與簇群狀
3. 代理人的學習機制：為增加產生設計想法的多樣性，DIM 需加強角色在 ICF 地圖轉換與 ICF 詞典解譯的功能。ICF 地圖轉換功能提供角色在內部交互作用 連結想法元件的差異性，ICF 詞典除了可以藉由容許程度增加其變化性外，同 時可以藉由增加 ICF 詞典中的領域概念語彙而達成，而上述這些功能的進行，
皆須透過角色之間的學習而達成。
架構在角色扮演的理論下，有關 DIM 的連結過程，代理人的學習機制主要包 括競爭學習與演化學習。競爭學習是不同角色代理人在連結過程中，它們所 產生的想法元件被選擇為共同想法之競爭，為這些角色代理人爭取存活的依 據。由於角色代理人彼此之間的競爭學習，演化學習是改變因相互競爭而失 敗的角色代理人之內部記憶組織，包括演化不同類型的 ICF 地圖與 ICF 詞典 的解譯能力，另外，想法元件中的喜好值也會因為演化學習而改變（喜好值 的增加或減少）。
4. 想法地圖區域控制與視覺化：在第六章的設計實驗中發現，當產生的 IE 地圖 過於龐大時，設計者很容易在這些地圖中迷失，而造成選擇與組合設計想法 的障礙。Ulrich 和 Eppinger[161]認為雖然有成千上萬的想法可以在發散連結步 驟中連結一起，但其中僅有 5 至 20 個可以被採用為問題解決的參考。為了形 成一個可以理解的想法地圖，因此，在想法地圖中的想法元件與連結應被審 慎的減少，以有效的提供設計者進行想法組合的依據。對於前述，在未來研 究中，可以經由連結密度、連結形式指標與連結深度指標，提供 DIM 系統控 制地圖範圍的運算機制，包括如深度控制、連結控制與元件控制等，以提升 設計者進行想法組合與設計生產的有效性。
除此之外，使用資訊視覺化（information visualization）的相關技術與理論 [162]，有助於使用者清楚的閱讀想法地圖的資訊以進行設計想法的連結與組 合，以免 IE 地圖所產生的想法元件過多，而造成設計者不易在有限的視窗看 到所有想法元件與其元件連結。
5. 案例調整：改變舊有的、或經過擷取的解決問題方法，此過程稱之為案例調 整 。 Dave 等 人 [87] 認 為 此 過 程 又 可 細 分 為 兩 種 方 式 ： 調 整 與 組 合
（combination）；基本上，案例組合採用類似於案例調整的技術，其主要差別 在於必須有兩個或兩個以上的案例，從案例基礎中被選取，然後再進行案例 之間的組合。
在 DIM 系統中，案例調整經由增加角色代理人內部記憶組織的想法元件與連 結，與原有角色 IE 地圖中的想法元件進行組合，進而產生新的角色 IE 地圖。
惟受限於角色本身 ICF 詞典解譯詞彙的能力，角色 IE 地圖的組合只能增加個 別角色代理人所認識詞彙的想法元件，而未能增加其他角色代理人不同詞彙 的想法元件，在未來研究中，DIM 可以藉由動態性的增加角色代理人之 ICF 詞典的詞彙而予以達成。除了角色 IE 地圖組合性的調整外，想法元件本身的 調整可以藉由想法元件中的概念屬性詞彙數量的增加而達成，這些都將提供 本研究在未來有關案例調整的研究方向。
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