<p><strong>Decision-making (DM)</strong> is a topic widely studied by different fields of science. (Cognitive sciences, Neurosciences, Computer sciences, Behavioral Economics, Operations research, etc. *1)</p>

<p>However, DM problems are varied and the computational approach to address that problem will vary accordingly. For instance:</p>

<p>If you have to make a frequent decision that affects the previous one you are dealing with a  sequential DM problem. In those cases, <strong>reinforcement learning</strong> *2 or <strong>deep reinforcement learning</strong> *3 can be used to tackle this problem. Examples of these problems can be seen in video-games where the game AI needs to take different actions (policies) over time to maximise its score. (reward)</p>

<p>If you the problem is not sequential but you deal with multiple criteria to find the most attractive alternative then you are dealing with a multi-criteria decision-making problem, topic widely researched in operations research. There are some typically-used algorithms that are utilised to assist human-decision making like <strong>AHP*4, TOPSIS*5, ELECTRE*6, PROMETREE*7</strong>. An example of MCDC is selecting a house to buy, where you have to consider location, price among other desirable or undesirable characteristics.</p>

<p>Depending on the level of uncertainty, subjective data and incomplete information of the problem you might require to use fuzzy, intuitionistic or neutrosophic variations of the mentioned algorithms. *8</p>

<p>You might need to optimise DM through different competing goals. In that case, you are dealing with a multi-objective decision-making optimisation problem (MODM). See <strong>Decision trees*9, genetic algorithms*10</strong> .</p>

<p>Furthermore, a DM problem can have different 'agents' making decision that can affect ours. So that is known as 'multi-agent' decision-making. In computer science, <strong>multi-agent system simulations</strong> are commonly used to research these problems. *11</p>

<p>You can also have the case where the agents have to make a collaborative decision that affects all of them. So that is known as 'group' decision-making.</p>

<p>In the industry, computational DM can be seen with the widely used <strong>recommender systems</strong> such as the ones in Netflix or Amazon.*13 In the B2B sector, AI in DM can be seen in decision-support systems and <strong>prescriptive analytics</strong> services *14.</p>

<p>I hope you find that information useful. There is indeed, much more about this complex topic, I just tried to summarise.</p>

<p>Some resources you might want to check:</p>

<ul>
<li><strong>Deep RTS:</strong> A playground for reinforcement learning agents in real-time strategy game environments. (Repository: <a href="https://github.com/cair/deep-rts" rel="nofollow noreferrer">https://github.com/cair/deep-rts</a>) (Pre-print Paper: <a href="https://arxiv.org/abs/1808.05032" rel="nofollow noreferrer">https://arxiv.org/abs/1808.05032</a>)</li>
<li><strong>OpenAI Gym:</strong> A general-purpose playground to test reinforcement learning AI algorithms. (Github: <a href="https://github.com/openai/gym" rel="nofollow noreferrer">https://github.com/openai/gym</a>, page: <a href="https://gym.openai.com/" rel="nofollow noreferrer">https://gym.openai.com/</a>)</li>
<li><strong>DecisionRadar:</strong> An online application to apply TOPSIS decision-making algorithm. (Site: <a href="https://decision-radar.com/" rel="nofollow noreferrer">https://decision-radar.com/</a>)</li>
<li><strong>AgentSimJS:</strong> A 3D multi-agent simulation system built in Javascript. (Repository: <a href="https://github.com/maxdeben83/agentsimjs" rel="nofollow noreferrer">https://github.com/maxdeben83/agentsimjs</a>)</li>
</ul>

<p><strong>REFERENCES:</strong></p>

<ul>
<li>*1 Atkinson, J. W. (1964). An introduction to motivation.</li>
<li>*1 Berridge, K. C. (2004). Motivation concepts in behavioral neuroscience. Physiology &amp; behavior, 81(2), 179-209.</li>
<li>*1 Hwang, C. L., &amp; Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making (pp. 58-191).Springer, Berlin, Heidelberg.    </li>
<li>*1 Tversky, A., &amp; Kahneman, D.(1981). The framing of decisions and the psychology of choice science, 211(4481), 453-458.    </li>
<li>*2 Littman, M. L. (1994). Markov games as a framework for multi-agent reinforcement learning. In Machine Learning Proceedings 1994 (pp.    157-163).    </li>
<li><p>*3 Van    Hasselt, H., Guez, A., &amp; Silver, D. (2016, February). Deep    Reinforcement Learning with Double Q-Learning. In AAAI (Vol. 2, p.   5).</p></li>
<li><p>*4 Aczél, J., &amp; Saaty, T. L. (1983). Procedures for    synthesizing ratio judgements. Journal of Mathematical Psychology,    27(1),
93–102.    doi:10.1016/0022-2496(83)90028-7</p></li>
<li><p>*4 Saaty, R. W. (1987). The analytic hierarchy process—what it is and how it is used.    Mathematical Modelling, 9(3-5), 167.<br>
doi:10.1016/0270-0255(87)90473-8</p></li>
<li><p>*4 Saaty, T. L. (1986). Axiomatic    Foundation of the Analytic Hierarchy Process. Management Science,    32(7), 841.<br>
doi:10.1287/mnsc.32.7.841</p></li>
<li><p>*4 Hwang, C. L., &amp; Yoon, K.    (1981).    Methods for multiple attribute decision making. In    Multiple attribute    decision
making (pp. 58-191). Springer, Berlin,    Heidelberg.</p></li>
<li><p>*6 Zhou,    Y. (1915). Multi-Criteria Decision Making in    Software Development: A    Systematic Literature Review.</p></li>
<li><p>*7 Zhou,    Y. (1915). Multi-Criteria Decision Making in Software Development: A    Systematic Literature Review.</p></li>
<li><p>*8 Pramanik, S., Biswas, P., &amp; Giri, B. C. (2015). Hybrid vector similarity measures and their<br>
applications to multi-attribute    decision making under neutrosophic
environment. Neural Computing and    Applications, 28(5), 1163<br>
doi:10.1007/s00521-015-2125-3</p></li>
<li><p>*8 Mardani, A., Nilashi, M.,    Zavadskas, E. K., Awang, S. R., Zare, H., &amp; Jamal, N. M. (2018).    Decision Making Methods Based on Fuzzy 
Aggregation Operators:    Three Decades Review from 1986 to 2017.<br>
International Journal of    Information Technology &amp; Decision Making,
17(02), 391–466.    doi:10.1142/s021962201830001x</p></li>
<li><p>*9 Zhao, H. (2007). A    multi-objective genetic programming approach to developing Pareto    optimal decision trees. Decision Support
Systems,    43(3), 809-826.</p></li>
<li><p>*9 Laumanns, M., &amp; Ocenasek, J. (2002, September). Bayesian optimization algorithms for multi-objective optimization. In<br>
International Conference on Parallel Problem Solving from Nature (pp.
298-307). Springer, Berlin, Heidelberg.</p></li>
<li><p>*9 Jin, Y. (Ed.). (2006).    Multi-objective machine learning (Vol. 16). Springer Science &amp;    Business Media.</p></li>
<li><p>10 Tamaki, H., Kita, H., &amp;    Kobayashi, S. (1996,    May).
Multi-objective optimization by genetic    algorithms: A    review.
In Evolutionary Computation, 1996., Proceedings    of IEEE<br>
International Conference on (pp. 517-522). IEEE.</p></li>
<li><p>*11    Rodriguez,    S., Gaud, N., &amp; Galland, S. (2014, August). SARL: a       general-purpose agent-oriented programming language. In
Web       Intelligence (WI) and Intelligent Agent Technologies (IAT),
2014       IEEE/WIC/ACM International Joint Conferences on (Vol. 3,
pp.       103-110). IEEE.</p></li>
<li><p>*12 Rao, A. S. (1996, January). AgentSpeak (L):    BDI agents speak out in a logical computable language. In European    Workshop on Modelling    Autonomous Agents in a Multi-Agent World    (pp. 42-55). Springer,    Berlin, Heidelberg.</p></li>
<li><p>*13 Ricci, F.,    Rokach, L., &amp; Shapira, B. (2015). Recommender systems: introduction    and challenges. In Recommender systems
handbook (pp.    1-34).    Springer, Boston, MA.</p></li>
<li><p>*14 <a href="https://www.ibm.com/analytics/prescriptive-analytics" rel="nofollow noreferrer">https://www.ibm.com/analytics/prescriptive-analytics</a></p></li>
</ul>
Artificial Intelligence for Decision Making
<p>Do you know <strong>any example</strong> for this topic?.
I have searched google but had no luck with any Decision Making using Artificial Intelligence example ( at least any truly developed)</p>